Data Integration by stakeholder category.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Integration by Stakeholder\n",
    "\n",
    "This notebook is gathering stakeholder profiling information form all sources of data (survey, NLP, graphs) on my 3 cities of reference: Taipei (Taiwan), Tel Aviv (Israel) and Tallinn (Estonia)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "warnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. collect data from survey study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/2.Survey/allsurveys.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>participation_channel</th>\n",
       "      <th>city_id</th>\n",
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       "      <th>total_social_medias</th>\n",
       "      <th>social_media_score</th>\n",
       "      <th>line</th>\n",
       "      <th>whatsapp</th>\n",
       "      <th>wechat</th>\n",
       "      <th>slack</th>\n",
       "      <th>telegram</th>\n",
       "      <th>messenger</th>\n",
       "      <th>other_messaging_app</th>\n",
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       "      <th>influencer_national_gov</th>\n",
       "      <th>influencer_local_gov</th>\n",
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       "      <th>influencer_private_research</th>\n",
       "      <th>influencer_corporate</th>\n",
       "      <th>influencer_startup</th>\n",
       "      <th>influencer_freelance</th>\n",
       "      <th>influencer_local_nonprofit</th>\n",
       "      <th>influencer_international_nonprofit</th>\n",
       "      <th>influencer_individual</th>\n",
       "      <th>total_influencers</th>\n",
       "      <th>relationship_public</th>\n",
       "      <th>relationship_academic</th>\n",
       "      <th>relationship_private</th>\n",
       "      <th>relationship_civil</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>global_events</th>\n",
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       "      <th>public_infrastructure</th>\n",
       "      <th>governmental_meeting</th>\n",
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       "      <th>other_meeting_place</th>\n",
       "      <th>total_meeting_places</th>\n",
       "      <th>opinion_share</th>\n",
       "      <th>share_score</th>\n",
       "      <th>opinion_change</th>\n",
       "      <th>change_score</th>\n",
       "      <th>engagement_wish</th>\n",
       "      <th>wish_score</th>\n",
       "      <th>online_survey</th>\n",
       "      <th>workshop</th>\n",
       "      <th>interview</th>\n",
       "      <th>voting_app</th>\n",
       "      <th>project</th>\n",
       "      <th>total_engagement</th>\n",
       "      <th>channels_score</th>\n",
       "      <th>engagement_freq</th>\n",
       "      <th>freq_score</th>\n",
       "      <th>engagement_score</th>\n",
       "      <th>survey_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Survey</td>\n",
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       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "      <td>1960-08-06</td>\n",
       "      <td>1979-08-01</td>\n",
       "      <td>הצפון הישן HaTzafon HaYashan</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Civil Society</td>\n",
       "      <td>Individual Citizen</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Neutral</td>\n",
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       "      <td>Neutral</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2020/03/02 9:28:50 PM UTC+3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Survey</td>\n",
       "      <td>Tel Aviv</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "      <td>1936-12-11</td>\n",
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       "      <td>הצפון הישן HaTzafon HaYashan</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>Public Sector</td>\n",
       "      <td>Government (National)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Less than one hour a year</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2020/03/11 3:22:11 PM UTC+3</td>\n",
       "    </tr>\n",
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       "      <td>Survey</td>\n",
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       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1971-07-10</td>\n",
       "      <td>2016-04-16</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>Individual Citizen</td>\n",
       "      <td>0</td>\n",
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       "      <td>Neutral</td>\n",
       "      <td>Neutral</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Less than one hour a year</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2019/11/08 3:54:52 PM UTC+8</td>\n",
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       "      <td>1988-11-10</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Private Sector</td>\n",
       "      <td>Investor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>One hour a year</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2020/07/01 11:45:27 AM UTC+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Survey</td>\n",
       "      <td>Tallinn</td>\n",
       "      <td>329</td>\n",
       "      <td>329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Man</td>\n",
       "      <td>1999-12-09</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>Pirita</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Public Sector</td>\n",
       "      <td>Government (National)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>One hour a year</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>2020/06/25 5:49:33 PM UTC+3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  participation_channel   city_id  survey_id  agent_id  twitter_profile  \\\n",
       "0                Survey  Tel Aviv        164       164              NaN   \n",
       "1                Survey  Tel Aviv        178       178              NaN   \n",
       "2                Survey    Taipei          6         6              NaN   \n",
       "3                Survey   Tallinn        356       356              NaN   \n",
       "4                Survey   Tallinn        329       329              NaN   \n",
       "\n",
       "  gender  birth_date arrival_date                   district_id  \\\n",
       "0    Man  1960-08-06   1979-08-01  הצפון הישן HaTzafon HaYashan   \n",
       "1    Man  1936-12-11   1963-01-01  הצפון הישן HaTzafon HaYashan   \n",
       "2  Woman  1971-07-10   2016-04-16                  Zhōngshān/中山   \n",
       "3    Man  1988-11-10   1988-11-10                      Kesklinn   \n",
       "4    Man  1999-12-09   2019-07-01                        Pirita   \n",
       "\n",
       "   engagement_feeling  feeling_score  source_social_medias  \\\n",
       "0                   1              0                     0   \n",
       "1                   2              0                     0   \n",
       "2                   1              0                     0   \n",
       "3                   1              0                     0   \n",
       "4                   1              0                     0   \n",
       "\n",
       "   source_messaging_app  source_word_mouth  source_internet  source_email  \\\n",
       "0                     0                  1                0             0   \n",
       "1                     0                  0                0             1   \n",
       "2                     0                  0                1             0   \n",
       "3                     0                  0                0             0   \n",
       "4                     0                  1                1             0   \n",
       "\n",
       "   source_print_medias  source_newspapers  source_radio  source_tv  \\\n",
       "0                    0                  0             0          0   \n",
       "1                    0                  0             0          0   \n",
       "2                    0                  1             0          0   \n",
       "3                    0                  1             0          0   \n",
       "4                    0                  0             0          0   \n",
       "\n",
       "   source_city_council  source_other  total_sources  source_score  facebook  \\\n",
       "0                    0             0              1             0         0   \n",
       "1                    0             0              1             0         0   \n",
       "2                    0             0              2             0         0   \n",
       "3                    0             0              1             0         0   \n",
       "4                    0             0              2             0         1   \n",
       "\n",
       "   twitter  youtube  medium  linkedin  instagram  Pinterest  Snapchat  \\\n",
       "0        0        1       0         0          0          0         0   \n",
       "1        0        0       0         1          0          0         0   \n",
       "2        0        0       0         0          1          0         0   \n",
       "3        0        0       0         1          0          0         0   \n",
       "4        0        1       0         0          1          0         1   \n",
       "\n",
       "   other_social_media  total_social_medias  social_media_score  line  \\\n",
       "0                   0                    1                   0     0   \n",
       "1                   0                    1                   0     0   \n",
       "2                   0                    1                   0     1   \n",
       "3                   0                    1                   0     0   \n",
       "4                   0                    4                   1     0   \n",
       "\n",
       "   whatsapp  wechat  slack  telegram  messenger  other_messaging_app  \\\n",
       "0         1       0      0         0          0                    0   \n",
       "1         1       0      0         0          0                    0   \n",
       "2         1       0      0         0          0                    0   \n",
       "3         0       0      0         1          0                    0   \n",
       "4         0       0      0         0          1                    0   \n",
       "\n",
       "   total_messaging_app  messaging_score  agent_category  \\\n",
       "0                    1                0   Civil Society   \n",
       "1                    1                0   Public Sector   \n",
       "2                    2                1   Civil Society   \n",
       "3                    1                0  Private Sector   \n",
       "4                    1                0   Public Sector   \n",
       "\n",
       "              agent_type  influencer_national_gov  influencer_local_gov  \\\n",
       "0     Individual Citizen                        0                     0   \n",
       "1  Government (National)                        1                     0   \n",
       "2     Individual Citizen                        0                     0   \n",
       "3               Investor                        0                     0   \n",
       "4  Government (National)                        1                     0   \n",
       "\n",
       "   influencer_public_services  influencer_investor  influencer_academic  \\\n",
       "0                           0                    0                    0   \n",
       "1                           0                    0                    0   \n",
       "2                           0                    0                    0   \n",
       "3                           0                    1                    0   \n",
       "4                           0                    0                    0   \n",
       "\n",
       "   influencer_private_research  influencer_corporate  influencer_startup  \\\n",
       "0                            0                     0                   0   \n",
       "1                            0                     0                   0   \n",
       "2                            0                     0                   0   \n",
       "3                            0                     0                   0   \n",
       "4                            0                     0                   0   \n",
       "\n",
       "   influencer_freelance  influencer_local_nonprofit  \\\n",
       "0                     0                           0   \n",
       "1                     0                           0   \n",
       "2                     0                           0   \n",
       "3                     0                           0   \n",
       "4                     0                           0   \n",
       "\n",
       "   influencer_international_nonprofit  influencer_individual  \\\n",
       "0                                   0                      1   \n",
       "1                                   0                      0   \n",
       "2                                   1                      0   \n",
       "3                                   0                      0   \n",
       "4                                   0                      0   \n",
       "\n",
       "   total_influencers relationship_public relationship_academic  \\\n",
       "0                  1             Neutral               Neutral   \n",
       "1                  1             Neutral              Positive   \n",
       "2                  1             Neutral               Neutral   \n",
       "3                  1             Neutral               Neutral   \n",
       "4                  1             Neutral               Neutral   \n",
       "\n",
       "  relationship_private relationship_civil  neighborhood  global_events  \\\n",
       "0              Neutral            Neutral             0              0   \n",
       "1             Positive            Neutral             0              0   \n",
       "2              Neutral            Neutral             0              1   \n",
       "3             Positive            Neutral             0              0   \n",
       "4             Positive            Neutral             1              0   \n",
       "\n",
       "   startup_structure  business_clusters  public_infrastructure  \\\n",
       "0                  0                  0                      0   \n",
       "1                  0                  0                      0   \n",
       "2                  0                  0                      0   \n",
       "3                  1                  1                      0   \n",
       "4                  0                  0                      0   \n",
       "\n",
       "   governmental_meeting  online  other_meeting_place  total_meeting_places  \\\n",
       "0                     0       0                    0                     0   \n",
       "1                     0       1                    0                     1   \n",
       "2                     0       0                    0                     1   \n",
       "3                     0       1                    0                     3   \n",
       "4                     0       0                    0                     1   \n",
       "\n",
       "   opinion_share  share_score  opinion_change  change_score engagement_wish  \\\n",
       "0              1            0               5             1              No   \n",
       "1              2            0               2             0              No   \n",
       "2              2            0               2             0              No   \n",
       "3              2            0               2             0              No   \n",
       "4              2            0               2             0              No   \n",
       "\n",
       "   wish_score  online_survey  workshop  interview  voting_app  project  \\\n",
       "0           0              0         0          0           0        0   \n",
       "1           0              1         0          0           0        0   \n",
       "2           0              1         0          0           0        0   \n",
       "3           0              0         1          0           0        0   \n",
       "4           0              1         0          0           0        0   \n",
       "\n",
       "   total_engagement  channels_score            engagement_freq  freq_score  \\\n",
       "0                 0               0  Less than one hour a year           0   \n",
       "1                 1               0  Less than one hour a year           0   \n",
       "2                 1               0  Less than one hour a year           0   \n",
       "3                 1               0            One hour a year           0   \n",
       "4                 1               0            One hour a year           0   \n",
       "\n",
       "   engagement_score                   survey_date  \n",
       "0               0.1   2020/03/02 9:28:50 PM UTC+3  \n",
       "1               0.1   2020/03/11 3:22:11 PM UTC+3  \n",
       "2               0.1   2019/11/08 3:54:52 PM UTC+8  \n",
       "3               0.1  2020/07/01 11:45:27 AM UTC+3  \n",
       "4               0.1   2020/06/25 5:49:33 PM UTC+3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private Sector           164\n",
       "Civil Society            118\n",
       "Research & Innovation     52\n",
       "Public Sector             32\n",
       "Name: agent_category, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many participants in each agent_category ?\n",
    "survey['agent_category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Individual Citizen           100\n",
       "Startup & SMEs                71\n",
       "Corporate Company             57\n",
       "Academic Research             40\n",
       "Freelance                     21\n",
       "Investor                      16\n",
       "Non-Profit (Local)            14\n",
       "Government (Local)            12\n",
       "Private Research              12\n",
       "Government (National)         11\n",
       "Public Services                8\n",
       "Non-Profit(International)      4\n",
       "Name: agent_type, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many in each type ?\n",
    "survey['agent_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe of agent's category with private divided in corpo and startup\n",
    "stakeholders = pd.DataFrame(np.array([['public', 32], ['corpo', 57], ['startup', 71], ['academic', 40], ['civil', 118], ['media', 'nan']]), columns=['category', 'num_survey'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey_id</th>\n",
       "      <th>agent_id</th>\n",
       "      <th>twitter_profile</th>\n",
       "      <th>engagement_feeling</th>\n",
       "      <th>feeling_score</th>\n",
       "      <th>source_social_medias</th>\n",
       "      <th>source_messaging_app</th>\n",
       "      <th>source_word_mouth</th>\n",
       "      <th>source_internet</th>\n",
       "      <th>source_email</th>\n",
       "      <th>source_print_medias</th>\n",
       "      <th>source_newspapers</th>\n",
       "      <th>source_radio</th>\n",
       "      <th>source_tv</th>\n",
       "      <th>source_city_council</th>\n",
       "      <th>source_other</th>\n",
       "      <th>total_sources</th>\n",
       "      <th>source_score</th>\n",
       "      <th>facebook</th>\n",
       "      <th>twitter</th>\n",
       "      <th>youtube</th>\n",
       "      <th>medium</th>\n",
       "      <th>linkedin</th>\n",
       "      <th>instagram</th>\n",
       "      <th>Pinterest</th>\n",
       "      <th>Snapchat</th>\n",
       "      <th>other_social_media</th>\n",
       "      <th>total_social_medias</th>\n",
       "      <th>social_media_score</th>\n",
       "      <th>line</th>\n",
       "      <th>whatsapp</th>\n",
       "      <th>wechat</th>\n",
       "      <th>slack</th>\n",
       "      <th>telegram</th>\n",
       "      <th>messenger</th>\n",
       "      <th>other_messaging_app</th>\n",
       "      <th>total_messaging_app</th>\n",
       "      <th>messaging_score</th>\n",
       "      <th>influencer_national_gov</th>\n",
       "      <th>influencer_local_gov</th>\n",
       "      <th>influencer_public_services</th>\n",
       "      <th>influencer_investor</th>\n",
       "      <th>influencer_academic</th>\n",
       "      <th>influencer_private_research</th>\n",
       "      <th>influencer_corporate</th>\n",
       "      <th>influencer_startup</th>\n",
       "      <th>influencer_freelance</th>\n",
       "      <th>influencer_local_nonprofit</th>\n",
       "      <th>influencer_international_nonprofit</th>\n",
       "      <th>influencer_individual</th>\n",
       "      <th>total_influencers</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>global_events</th>\n",
       "      <th>startup_structure</th>\n",
       "      <th>business_clusters</th>\n",
       "      <th>public_infrastructure</th>\n",
       "      <th>governmental_meeting</th>\n",
       "      <th>online</th>\n",
       "      <th>other_meeting_place</th>\n",
       "      <th>total_meeting_places</th>\n",
       "      <th>opinion_share</th>\n",
       "      <th>share_score</th>\n",
       "      <th>opinion_change</th>\n",
       "      <th>change_score</th>\n",
       "      <th>wish_score</th>\n",
       "      <th>online_survey</th>\n",
       "      <th>workshop</th>\n",
       "      <th>interview</th>\n",
       "      <th>voting_app</th>\n",
       "      <th>project</th>\n",
       "      <th>total_engagement</th>\n",
       "      <th>channels_score</th>\n",
       "      <th>freq_score</th>\n",
       "      <th>engagement_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Civil Society</th>\n",
       "      <td>156.720339</td>\n",
       "      <td>156.720339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.601695</td>\n",
       "      <td>0.330508</td>\n",
       "      <td>0.822034</td>\n",
       "      <td>0.262712</td>\n",
       "      <td>0.567797</td>\n",
       "      <td>0.627119</td>\n",
       "      <td>0.228814</td>\n",
       "      <td>0.118644</td>\n",
       "      <td>0.372881</td>\n",
       "      <td>0.161017</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>3.703390</td>\n",
       "      <td>0.542373</td>\n",
       "      <td>0.855932</td>\n",
       "      <td>0.110169</td>\n",
       "      <td>0.389831</td>\n",
       "      <td>0.093220</td>\n",
       "      <td>0.398305</td>\n",
       "      <td>0.398305</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.016949</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>2.313559</td>\n",
       "      <td>0.737288</td>\n",
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       "      <td>0.542373</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.169492</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.211864</td>\n",
       "      <td>0.059322</td>\n",
       "      <td>1.618644</td>\n",
       "      <td>0.406780</td>\n",
       "      <td>0.381356</td>\n",
       "      <td>0.313559</td>\n",
       "      <td>0.194915</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.135593</td>\n",
       "      <td>0.152542</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.076271</td>\n",
       "      <td>0.296610</td>\n",
       "      <td>0.254237</td>\n",
       "      <td>0.466102</td>\n",
       "      <td>2.457627</td>\n",
       "      <td>0.211864</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.279661</td>\n",
       "      <td>0.245763</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.703390</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>3.186441</td>\n",
       "      <td>0.703390</td>\n",
       "      <td>3.186441</td>\n",
       "      <td>0.822034</td>\n",
       "      <td>0.796610</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.440678</td>\n",
       "      <td>0.322034</td>\n",
       "      <td>0.584746</td>\n",
       "      <td>0.355932</td>\n",
       "      <td>2.449153</td>\n",
       "      <td>0.745763</td>\n",
       "      <td>0.838983</td>\n",
       "      <td>0.662712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private Sector</th>\n",
       "      <td>188.634146</td>\n",
       "      <td>188.634146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.524390</td>\n",
       "      <td>0.323171</td>\n",
       "      <td>0.798780</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.603659</td>\n",
       "      <td>0.609756</td>\n",
       "      <td>0.213415</td>\n",
       "      <td>0.103659</td>\n",
       "      <td>0.201220</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.054878</td>\n",
       "      <td>0.036585</td>\n",
       "      <td>3.189024</td>\n",
       "      <td>0.506098</td>\n",
       "      <td>0.798780</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.426829</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.615854</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0.042683</td>\n",
       "      <td>0.018293</td>\n",
       "      <td>0.012195</td>\n",
       "      <td>2.695122</td>\n",
       "      <td>0.792683</td>\n",
       "      <td>0.274390</td>\n",
       "      <td>0.652439</td>\n",
       "      <td>0.079268</td>\n",
       "      <td>0.213415</td>\n",
       "      <td>0.121951</td>\n",
       "      <td>0.140244</td>\n",
       "      <td>0.054878</td>\n",
       "      <td>1.536585</td>\n",
       "      <td>0.396341</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>0.231707</td>\n",
       "      <td>0.182927</td>\n",
       "      <td>0.170732</td>\n",
       "      <td>0.067073</td>\n",
       "      <td>0.274390</td>\n",
       "      <td>0.353659</td>\n",
       "      <td>0.335366</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.097561</td>\n",
       "      <td>0.225610</td>\n",
       "      <td>2.524390</td>\n",
       "      <td>0.280488</td>\n",
       "      <td>0.292683</td>\n",
       "      <td>0.469512</td>\n",
       "      <td>0.414634</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.603659</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>2.243902</td>\n",
       "      <td>3.237805</td>\n",
       "      <td>0.737805</td>\n",
       "      <td>3.018293</td>\n",
       "      <td>0.786585</td>\n",
       "      <td>0.737805</td>\n",
       "      <td>0.737805</td>\n",
       "      <td>0.378049</td>\n",
       "      <td>0.298780</td>\n",
       "      <td>0.573171</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>2.353659</td>\n",
       "      <td>0.737805</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.645122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Sector</th>\n",
       "      <td>217.343750</td>\n",
       "      <td>217.343750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.125000</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.656250</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.406250</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.781250</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.468750</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.468750</td>\n",
       "      <td>0.656250</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>0.906250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>1.406250</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.218750</td>\n",
       "      <td>2.093750</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.343750</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.187500</td>\n",
       "      <td>3.562500</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>3.031250</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.312500</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research &amp; Innovation</th>\n",
       "      <td>207.250000</td>\n",
       "      <td>207.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.019231</td>\n",
       "      <td>0.211538</td>\n",
       "      <td>0.788462</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>3.403846</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.788462</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.211538</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.096154</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>1.557692</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.442308</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.057692</td>\n",
       "      <td>0.192308</td>\n",
       "      <td>0.173077</td>\n",
       "      <td>0.211538</td>\n",
       "      <td>2.538462</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.211538</td>\n",
       "      <td>0.134615</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>2.115385</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>2.942308</td>\n",
       "      <td>0.730769</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>2.326923</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>0.865385</td>\n",
       "      <td>0.611538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        survey_id    agent_id  twitter_profile  \\\n",
       "agent_category                                                   \n",
       "Civil Society          156.720339  156.720339              NaN   \n",
       "Private Sector         188.634146  188.634146              NaN   \n",
       "Public Sector          217.343750  217.343750              NaN   \n",
       "Research & Innovation  207.250000  207.250000              NaN   \n",
       "\n",
       "                       engagement_feeling  feeling_score  \\\n",
       "agent_category                                             \n",
       "Civil Society                    3.601695       0.330508   \n",
       "Private Sector                   3.524390       0.323171   \n",
       "Public Sector                    5.125000       0.531250   \n",
       "Research & Innovation            3.019231       0.211538   \n",
       "\n",
       "                       source_social_medias  source_messaging_app  \\\n",
       "agent_category                                                      \n",
       "Civil Society                      0.822034              0.262712   \n",
       "Private Sector                     0.798780              0.219512   \n",
       "Public Sector                      0.781250              0.218750   \n",
       "Research & Innovation              0.788462              0.307692   \n",
       "\n",
       "                       source_word_mouth  source_internet  source_email  \\\n",
       "agent_category                                                            \n",
       "Civil Society                   0.567797         0.627119      0.228814   \n",
       "Private Sector                  0.603659         0.609756      0.213415   \n",
       "Public Sector                   0.562500         0.656250      0.437500   \n",
       "Research & Innovation           0.730769         0.576923      0.288462   \n",
       "\n",
       "                       source_print_medias  source_newspapers  source_radio  \\\n",
       "agent_category                                                                \n",
       "Civil Society                     0.118644           0.372881      0.161017   \n",
       "Private Sector                    0.103659           0.201220      0.097561   \n",
       "Public Sector                     0.125000           0.187500      0.187500   \n",
       "Research & Innovation             0.096154           0.153846      0.173077   \n",
       "\n",
       "                       source_tv  source_city_council  source_other  \\\n",
       "agent_category                                                        \n",
       "Civil Society           0.389831             0.101695      0.050847   \n",
       "Private Sector          0.250000             0.054878      0.036585   \n",
       "Public Sector           0.406250             0.218750      0.000000   \n",
       "Research & Innovation   0.230769             0.019231      0.038462   \n",
       "\n",
       "                       total_sources  source_score  facebook   twitter  \\\n",
       "agent_category                                                           \n",
       "Civil Society               3.703390      0.542373  0.855932  0.110169   \n",
       "Private Sector              3.189024      0.506098  0.798780  0.134146   \n",
       "Public Sector               3.781250      0.531250  0.906250  0.187500   \n",
       "Research & Innovation       3.403846      0.461538  0.788462  0.057692   \n",
       "\n",
       "                        youtube    medium  linkedin  instagram  Pinterest  \\\n",
       "agent_category                                                              \n",
       "Civil Society          0.389831  0.093220  0.398305   0.398305   0.016949   \n",
       "Private Sector         0.426829  0.134146  0.615854   0.512195   0.042683   \n",
       "Public Sector          0.375000  0.031250  0.437500   0.468750   0.031250   \n",
       "Research & Innovation  0.423077  0.115385  0.384615   0.384615   0.019231   \n",
       "\n",
       "                       Snapchat  other_social_media  total_social_medias  \\\n",
       "agent_category                                                             \n",
       "Civil Society          0.016949            0.033898             2.313559   \n",
       "Private Sector         0.018293            0.012195             2.695122   \n",
       "Public Sector          0.031250            0.000000             2.468750   \n",
       "Research & Innovation  0.038462            0.000000             2.211538   \n",
       "\n",
       "                       social_media_score      line  whatsapp    wechat  \\\n",
       "agent_category                                                            \n",
       "Civil Society                    0.737288  0.449153  0.542373  0.101695   \n",
       "Private Sector                   0.792683  0.274390  0.652439  0.079268   \n",
       "Public Sector                    0.656250  0.093750  0.906250  0.000000   \n",
       "Research & Innovation            0.692308  0.230769  0.750000  0.096154   \n",
       "\n",
       "                          slack  telegram  messenger  other_messaging_app  \\\n",
       "agent_category                                                              \n",
       "Civil Society          0.169492  0.084746   0.211864             0.059322   \n",
       "Private Sector         0.213415  0.121951   0.140244             0.054878   \n",
       "Public Sector          0.125000  0.125000   0.125000             0.031250   \n",
       "Research & Innovation  0.057692  0.134615   0.250000             0.038462   \n",
       "\n",
       "                       total_messaging_app  messaging_score  \\\n",
       "agent_category                                                \n",
       "Civil Society                     1.618644         0.406780   \n",
       "Private Sector                    1.536585         0.396341   \n",
       "Public Sector                     1.406250         0.312500   \n",
       "Research & Innovation             1.557692         0.423077   \n",
       "\n",
       "                       influencer_national_gov  influencer_local_gov  \\\n",
       "agent_category                                                         \n",
       "Civil Society                         0.381356              0.313559   \n",
       "Private Sector                        0.353659              0.231707   \n",
       "Public Sector                         0.562500              0.437500   \n",
       "Research & Innovation                 0.442308              0.288462   \n",
       "\n",
       "                       influencer_public_services  influencer_investor  \\\n",
       "agent_category                                                           \n",
       "Civil Society                            0.194915             0.033898   \n",
       "Private Sector                           0.182927             0.170732   \n",
       "Public Sector                            0.156250             0.062500   \n",
       "Research & Innovation                    0.173077             0.134615   \n",
       "\n",
       "                       influencer_academic  influencer_private_research  \\\n",
       "agent_category                                                            \n",
       "Civil Society                     0.050847                     0.135593   \n",
       "Private Sector                    0.067073                     0.274390   \n",
       "Public Sector                     0.031250                     0.125000   \n",
       "Research & Innovation             0.288462                     0.269231   \n",
       "\n",
       "                       influencer_corporate  influencer_startup  \\\n",
       "agent_category                                                    \n",
       "Civil Society                      0.152542            0.101695   \n",
       "Private Sector                     0.353659            0.335366   \n",
       "Public Sector                      0.093750            0.156250   \n",
       "Research & Innovation              0.153846            0.153846   \n",
       "\n",
       "                       influencer_freelance  influencer_local_nonprofit  \\\n",
       "agent_category                                                            \n",
       "Civil Society                      0.076271                    0.296610   \n",
       "Private Sector                     0.097561                    0.134146   \n",
       "Public Sector                      0.031250                    0.093750   \n",
       "Research & Innovation              0.057692                    0.192308   \n",
       "\n",
       "                       influencer_international_nonprofit  \\\n",
       "agent_category                                              \n",
       "Civil Society                                    0.254237   \n",
       "Private Sector                                   0.097561   \n",
       "Public Sector                                    0.125000   \n",
       "Research & Innovation                            0.173077   \n",
       "\n",
       "                       influencer_individual  total_influencers  neighborhood  \\\n",
       "agent_category                                                                  \n",
       "Civil Society                       0.466102           2.457627      0.211864   \n",
       "Private Sector                      0.225610           2.524390      0.280488   \n",
       "Public Sector                       0.218750           2.093750      0.187500   \n",
       "Research & Innovation               0.211538           2.538462      0.326923   \n",
       "\n",
       "                       global_events  startup_structure  business_clusters  \\\n",
       "agent_category                                                               \n",
       "Civil Society               0.271186           0.322034           0.279661   \n",
       "Private Sector              0.292683           0.469512           0.414634   \n",
       "Public Sector               0.281250           0.343750           0.093750   \n",
       "Research & Innovation       0.269231           0.346154           0.211538   \n",
       "\n",
       "                       public_infrastructure  governmental_meeting    online  \\\n",
       "agent_category                                                                 \n",
       "Civil Society                       0.245763              0.067797  0.703390   \n",
       "Private Sector                      0.134146              0.024390  0.603659   \n",
       "Public Sector                       0.281250              0.312500  0.687500   \n",
       "Research & Innovation               0.134615              0.115385  0.634615   \n",
       "\n",
       "                       other_meeting_place  total_meeting_places  \\\n",
       "agent_category                                                     \n",
       "Civil Society                     0.000000              2.101695   \n",
       "Private Sector                    0.024390              2.243902   \n",
       "Public Sector                     0.000000              2.187500   \n",
       "Research & Innovation             0.076923              2.115385   \n",
       "\n",
       "                       opinion_share  share_score  opinion_change  \\\n",
       "agent_category                                                      \n",
       "Civil Society               3.186441     0.703390        3.186441   \n",
       "Private Sector              3.237805     0.737805        3.018293   \n",
       "Public Sector               3.562500     0.781250        3.031250   \n",
       "Research & Innovation       3.000000     0.653846        2.942308   \n",
       "\n",
       "                       change_score  wish_score  online_survey  workshop  \\\n",
       "agent_category                                                             \n",
       "Civil Society              0.822034    0.796610       0.745763  0.440678   \n",
       "Private Sector             0.786585    0.737805       0.737805  0.378049   \n",
       "Public Sector              0.750000    0.812500       0.625000  0.312500   \n",
       "Research & Innovation      0.730769    0.807692       0.615385  0.461538   \n",
       "\n",
       "                       interview  voting_app   project  total_engagement  \\\n",
       "agent_category                                                             \n",
       "Civil Society           0.322034    0.584746  0.355932          2.449153   \n",
       "Private Sector          0.298780    0.573171  0.365854          2.353659   \n",
       "Public Sector           0.250000    0.625000  0.500000          2.312500   \n",
       "Research & Innovation   0.346154    0.576923  0.326923          2.326923   \n",
       "\n",
       "                       channels_score  freq_score  engagement_score  \n",
       "agent_category                                                       \n",
       "Civil Society                0.745763    0.838983          0.662712  \n",
       "Private Sector               0.737805    0.829268          0.645122  \n",
       "Public Sector                0.750000    0.812500          0.662500  \n",
       "Research & Innovation        0.634615    0.865385          0.611538  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_category']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey_id</th>\n",
       "      <th>agent_id</th>\n",
       "      <th>twitter_profile</th>\n",
       "      <th>engagement_feeling</th>\n",
       "      <th>feeling_score</th>\n",
       "      <th>source_social_medias</th>\n",
       "      <th>source_messaging_app</th>\n",
       "      <th>source_word_mouth</th>\n",
       "      <th>source_internet</th>\n",
       "      <th>source_email</th>\n",
       "      <th>source_print_medias</th>\n",
       "      <th>source_newspapers</th>\n",
       "      <th>source_radio</th>\n",
       "      <th>source_tv</th>\n",
       "      <th>source_city_council</th>\n",
       "      <th>source_other</th>\n",
       "      <th>total_sources</th>\n",
       "      <th>source_score</th>\n",
       "      <th>facebook</th>\n",
       "      <th>twitter</th>\n",
       "      <th>youtube</th>\n",
       "      <th>medium</th>\n",
       "      <th>linkedin</th>\n",
       "      <th>instagram</th>\n",
       "      <th>Pinterest</th>\n",
       "      <th>Snapchat</th>\n",
       "      <th>other_social_media</th>\n",
       "      <th>total_social_medias</th>\n",
       "      <th>social_media_score</th>\n",
       "      <th>line</th>\n",
       "      <th>whatsapp</th>\n",
       "      <th>wechat</th>\n",
       "      <th>slack</th>\n",
       "      <th>telegram</th>\n",
       "      <th>messenger</th>\n",
       "      <th>other_messaging_app</th>\n",
       "      <th>total_messaging_app</th>\n",
       "      <th>messaging_score</th>\n",
       "      <th>influencer_national_gov</th>\n",
       "      <th>influencer_local_gov</th>\n",
       "      <th>influencer_public_services</th>\n",
       "      <th>influencer_investor</th>\n",
       "      <th>influencer_academic</th>\n",
       "      <th>influencer_private_research</th>\n",
       "      <th>influencer_corporate</th>\n",
       "      <th>influencer_startup</th>\n",
       "      <th>influencer_freelance</th>\n",
       "      <th>influencer_local_nonprofit</th>\n",
       "      <th>influencer_international_nonprofit</th>\n",
       "      <th>influencer_individual</th>\n",
       "      <th>total_influencers</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>global_events</th>\n",
       "      <th>startup_structure</th>\n",
       "      <th>business_clusters</th>\n",
       "      <th>public_infrastructure</th>\n",
       "      <th>governmental_meeting</th>\n",
       "      <th>online</th>\n",
       "      <th>other_meeting_place</th>\n",
       "      <th>total_meeting_places</th>\n",
       "      <th>opinion_share</th>\n",
       "      <th>share_score</th>\n",
       "      <th>opinion_change</th>\n",
       "      <th>change_score</th>\n",
       "      <th>wish_score</th>\n",
       "      <th>online_survey</th>\n",
       "      <th>workshop</th>\n",
       "      <th>interview</th>\n",
       "      <th>voting_app</th>\n",
       "      <th>project</th>\n",
       "      <th>total_engagement</th>\n",
       "      <th>channels_score</th>\n",
       "      <th>freq_score</th>\n",
       "      <th>engagement_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Academic Research</th>\n",
       "      <td>223.700000</td>\n",
       "      <td>223.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>0.225000</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.775000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>2.175000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.577500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corporate Company</th>\n",
       "      <td>190.122807</td>\n",
       "      <td>190.122807</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.719298</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.228070</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.192982</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>3.052632</td>\n",
       "      <td>0.403509</td>\n",
       "      <td>0.771930</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.543860</td>\n",
       "      <td>0.456140</td>\n",
       "      <td>0.035088</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.350877</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.298246</td>\n",
       "      <td>0.649123</td>\n",
       "      <td>0.070175</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>1.385965</td>\n",
       "      <td>0.298246</td>\n",
       "      <td>0.403509</td>\n",
       "      <td>0.245614</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>2.087719</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.280702</td>\n",
       "      <td>0.403509</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.017544</td>\n",
       "      <td>0.596491</td>\n",
       "      <td>0.035088</td>\n",
       "      <td>1.964912</td>\n",
       "      <td>3.228070</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>3.017544</td>\n",
       "      <td>0.771930</td>\n",
       "      <td>0.649123</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.350877</td>\n",
       "      <td>0.192982</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.403509</td>\n",
       "      <td>2.368421</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.807018</td>\n",
       "      <td>0.605263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freelance</th>\n",
       "      <td>146.095238</td>\n",
       "      <td>146.095238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.761905</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>3.190476</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>1.190476</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>2.571429</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>2.095238</td>\n",
       "      <td>3.190476</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>2.857143</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.809524</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.523810</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>2.285714</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.638095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government (Local)</th>\n",
       "      <td>184.750000</td>\n",
       "      <td>184.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.416667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.583333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.416667</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>3.083333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.741667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government (National)</th>\n",
       "      <td>243.545455</td>\n",
       "      <td>243.545455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.727273</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.090909</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.272727</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.090909</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.818182</td>\n",
       "      <td>3.181818</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>2.636364</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>2.090909</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.527273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Individual Citizen</th>\n",
       "      <td>152.530000</td>\n",
       "      <td>152.530000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.590000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.440000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>3.770000</td>\n",
       "      <td>0.530000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>2.320000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>1.630000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.110000</td>\n",
       "      <td>3.120000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.810000</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.656000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investor</th>\n",
       "      <td>222.687500</td>\n",
       "      <td>222.687500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.437500</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.062500</td>\n",
       "      <td>3.312500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>2.062500</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.581250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Profit (Local)</th>\n",
       "      <td>162.928571</td>\n",
       "      <td>162.928571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.785714</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>3.357143</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.214286</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.642857</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.785714</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.071429</td>\n",
       "      <td>3.785714</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>3.214286</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.785714</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.721429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Profit(International)</th>\n",
       "      <td>239.750000</td>\n",
       "      <td>239.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private Research</th>\n",
       "      <td>152.416667</td>\n",
       "      <td>152.416667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.083333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Services</th>\n",
       "      <td>221.375000</td>\n",
       "      <td>221.375000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.625000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>3.625000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Startup &amp; SMEs</th>\n",
       "      <td>193.746479</td>\n",
       "      <td>193.746479</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.788732</td>\n",
       "      <td>0.352113</td>\n",
       "      <td>0.887324</td>\n",
       "      <td>0.239437</td>\n",
       "      <td>0.676056</td>\n",
       "      <td>0.676056</td>\n",
       "      <td>0.126761</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.183099</td>\n",
       "      <td>0.112676</td>\n",
       "      <td>0.253521</td>\n",
       "      <td>0.056338</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>3.366197</td>\n",
       "      <td>0.605634</td>\n",
       "      <td>0.859155</td>\n",
       "      <td>0.169014</td>\n",
       "      <td>0.450704</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.676056</td>\n",
       "      <td>0.563380</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.028169</td>\n",
       "      <td>2.971831</td>\n",
       "      <td>0.859155</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>0.690141</td>\n",
       "      <td>0.084507</td>\n",
       "      <td>0.323944</td>\n",
       "      <td>0.140845</td>\n",
       "      <td>0.197183</td>\n",
       "      <td>0.070423</td>\n",
       "      <td>1.802817</td>\n",
       "      <td>0.549296</td>\n",
       "      <td>0.380282</td>\n",
       "      <td>0.253521</td>\n",
       "      <td>0.267606</td>\n",
       "      <td>0.183099</td>\n",
       "      <td>0.070423</td>\n",
       "      <td>0.281690</td>\n",
       "      <td>0.281690</td>\n",
       "      <td>0.549296</td>\n",
       "      <td>0.098592</td>\n",
       "      <td>0.197183</td>\n",
       "      <td>0.126761</td>\n",
       "      <td>0.295775</td>\n",
       "      <td>2.985915</td>\n",
       "      <td>0.408451</td>\n",
       "      <td>0.338028</td>\n",
       "      <td>0.507042</td>\n",
       "      <td>0.549296</td>\n",
       "      <td>0.154930</td>\n",
       "      <td>0.042254</td>\n",
       "      <td>0.563380</td>\n",
       "      <td>0.014085</td>\n",
       "      <td>2.577465</td>\n",
       "      <td>3.267606</td>\n",
       "      <td>0.774648</td>\n",
       "      <td>3.070423</td>\n",
       "      <td>0.830986</td>\n",
       "      <td>0.802817</td>\n",
       "      <td>0.732394</td>\n",
       "      <td>0.408451</td>\n",
       "      <td>0.366197</td>\n",
       "      <td>0.549296</td>\n",
       "      <td>0.366197</td>\n",
       "      <td>2.422535</td>\n",
       "      <td>0.746479</td>\n",
       "      <td>0.887324</td>\n",
       "      <td>0.697183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            survey_id    agent_id  twitter_profile  \\\n",
       "agent_type                                                           \n",
       "Academic Research          223.700000  223.700000              NaN   \n",
       "Corporate Company          190.122807  190.122807              NaN   \n",
       "Freelance                  146.095238  146.095238              NaN   \n",
       "Government (Local)         184.750000  184.750000              NaN   \n",
       "Government (National)      243.545455  243.545455              NaN   \n",
       "Individual Citizen         152.530000  152.530000              NaN   \n",
       "Investor                   222.687500  222.687500              NaN   \n",
       "Non-Profit (Local)         162.928571  162.928571              NaN   \n",
       "Non-Profit(International)  239.750000  239.750000              NaN   \n",
       "Private Research           152.416667  152.416667              NaN   \n",
       "Public Services            221.375000  221.375000              NaN   \n",
       "Startup & SMEs             193.746479  193.746479              NaN   \n",
       "\n",
       "                           engagement_feeling  feeling_score  \\\n",
       "agent_type                                                     \n",
       "Academic Research                    2.650000       0.200000   \n",
       "Corporate Company                    3.333333       0.315789   \n",
       "Freelance                            3.761905       0.285714   \n",
       "Government (Local)                   5.416667       0.666667   \n",
       "Government (National)                4.727273       0.454545   \n",
       "Individual Citizen                   3.590000       0.330000   \n",
       "Investor                             2.625000       0.250000   \n",
       "Non-Profit (Local)                   3.785714       0.357143   \n",
       "Non-Profit(International)            3.250000       0.250000   \n",
       "Private Research                     4.250000       0.250000   \n",
       "Public Services                      5.625000       0.500000   \n",
       "Startup & SMEs                       3.788732       0.352113   \n",
       "\n",
       "                           source_social_medias  source_messaging_app  \\\n",
       "agent_type                                                              \n",
       "Academic Research                      0.775000              0.250000   \n",
       "Corporate Company                      0.719298              0.210526   \n",
       "Freelance                              0.809524              0.190476   \n",
       "Government (Local)                     0.833333              0.166667   \n",
       "Government (National)                  0.636364              0.090909   \n",
       "Individual Citizen                     0.820000              0.280000   \n",
       "Investor                               0.687500              0.187500   \n",
       "Non-Profit (Local)                     0.785714              0.214286   \n",
       "Non-Profit(International)              1.000000              0.000000   \n",
       "Private Research                       0.833333              0.500000   \n",
       "Public Services                        0.875000              0.500000   \n",
       "Startup & SMEs                         0.887324              0.239437   \n",
       "\n",
       "                           source_word_mouth  source_internet  source_email  \\\n",
       "agent_type                                                                    \n",
       "Academic Research                   0.700000         0.600000      0.250000   \n",
       "Corporate Company                   0.526316         0.578947      0.228070   \n",
       "Freelance                           0.523810         0.523810      0.238095   \n",
       "Government (Local)                  0.583333         0.500000      0.666667   \n",
       "Government (National)               0.545455         0.636364      0.181818   \n",
       "Individual Citizen                  0.570000         0.610000      0.230000   \n",
       "Investor                            0.625000         0.562500      0.500000   \n",
       "Non-Profit (Local)                  0.642857         0.714286      0.285714   \n",
       "Non-Profit(International)           0.250000         0.750000      0.000000   \n",
       "Private Research                    0.833333         0.500000      0.416667   \n",
       "Public Services                     0.625000         0.875000      0.500000   \n",
       "Startup & SMEs                      0.676056         0.676056      0.126761   \n",
       "\n",
       "                           source_print_medias  source_newspapers  \\\n",
       "agent_type                                                          \n",
       "Academic Research                     0.100000           0.150000   \n",
       "Corporate Company                     0.070175           0.192982   \n",
       "Freelance                             0.095238           0.238095   \n",
       "Government (Local)                    0.166667           0.250000   \n",
       "Government (National)                 0.090909           0.181818   \n",
       "Individual Citizen                    0.130000           0.390000   \n",
       "Investor                              0.062500           0.250000   \n",
       "Non-Profit (Local)                    0.071429           0.214286   \n",
       "Non-Profit(International)             0.000000           0.500000   \n",
       "Private Research                      0.083333           0.166667   \n",
       "Public Services                       0.125000           0.125000   \n",
       "Startup & SMEs                        0.140845           0.183099   \n",
       "\n",
       "                           source_radio  source_tv  source_city_council  \\\n",
       "agent_type                                                                \n",
       "Academic Research              0.150000   0.200000             0.025000   \n",
       "Corporate Company              0.140351   0.263158             0.052632   \n",
       "Freelance                      0.047619   0.333333             0.142857   \n",
       "Government (Local)             0.166667   0.416667             0.166667   \n",
       "Government (National)          0.181818   0.363636             0.181818   \n",
       "Individual Citizen             0.170000   0.440000             0.080000   \n",
       "Investor                       0.000000   0.125000             0.000000   \n",
       "Non-Profit (Local)             0.142857   0.071429             0.142857   \n",
       "Non-Profit(International)      0.000000   0.250000             0.500000   \n",
       "Private Research               0.250000   0.333333             0.000000   \n",
       "Public Services                0.125000   0.375000             0.250000   \n",
       "Startup & SMEs                 0.112676   0.253521             0.056338   \n",
       "\n",
       "                           source_other  total_sources  source_score  \\\n",
       "agent_type                                                             \n",
       "Academic Research              0.050000       3.250000      0.475000   \n",
       "Corporate Company              0.070175       3.052632      0.403509   \n",
       "Freelance                      0.047619       3.190476      0.428571   \n",
       "Government (Local)             0.000000       3.916667      0.500000   \n",
       "Government (National)          0.000000       3.090909      0.363636   \n",
       "Individual Citizen             0.050000       3.770000      0.530000   \n",
       "Investor                       0.000000       3.000000      0.562500   \n",
       "Non-Profit (Local)             0.071429       3.357143      0.571429   \n",
       "Non-Profit(International)      0.000000       3.250000      0.750000   \n",
       "Private Research               0.000000       3.916667      0.416667   \n",
       "Public Services                0.000000       4.375000      0.750000   \n",
       "Startup & SMEs                 0.014085       3.366197      0.605634   \n",
       "\n",
       "                           facebook   twitter   youtube    medium  linkedin  \\\n",
       "agent_type                                                                    \n",
       "Academic Research          0.775000  0.075000  0.400000  0.125000  0.375000   \n",
       "Corporate Company          0.771930  0.070175  0.350877  0.122807  0.543860   \n",
       "Freelance                  0.857143  0.142857  0.523810  0.190476  0.428571   \n",
       "Government (Local)         0.916667  0.166667  0.416667  0.083333  0.416667   \n",
       "Government (National)      0.909091  0.181818  0.454545  0.000000  0.272727   \n",
       "Individual Citizen         0.860000  0.130000  0.390000  0.100000  0.360000   \n",
       "Investor                   0.562500  0.187500  0.437500  0.062500  0.875000   \n",
       "Non-Profit (Local)         0.785714  0.000000  0.500000  0.000000  0.571429   \n",
       "Non-Profit(International)  1.000000  0.000000  0.000000  0.250000  0.750000   \n",
       "Private Research           0.833333  0.000000  0.500000  0.083333  0.416667   \n",
       "Public Services            0.875000  0.250000  0.250000  0.000000  0.625000   \n",
       "Startup & SMEs             0.859155  0.169014  0.450704  0.140845  0.676056   \n",
       "\n",
       "                           instagram  Pinterest  Snapchat  other_social_media  \\\n",
       "agent_type                                                                      \n",
       "Academic Research           0.425000   0.025000  0.050000            0.000000   \n",
       "Corporate Company           0.456140   0.035088  0.000000            0.000000   \n",
       "Freelance                   0.619048   0.095238  0.000000            0.000000   \n",
       "Government (Local)          0.500000   0.083333  0.000000            0.000000   \n",
       "Government (National)       0.363636   0.000000  0.090909            0.000000   \n",
       "Individual Citizen          0.410000   0.020000  0.010000            0.040000   \n",
       "Investor                    0.312500   0.000000  0.000000            0.000000   \n",
       "Non-Profit (Local)          0.285714   0.000000  0.071429            0.000000   \n",
       "Non-Profit(International)   0.500000   0.000000  0.000000            0.000000   \n",
       "Private Research            0.250000   0.000000  0.000000            0.000000   \n",
       "Public Services             0.625000   0.000000  0.000000            0.000000   \n",
       "Startup & SMEs              0.563380   0.042254  0.042254            0.028169   \n",
       "\n",
       "                           total_social_medias  social_media_score      line  \\\n",
       "agent_type                                                                     \n",
       "Academic Research                     2.250000            0.675000  0.150000   \n",
       "Corporate Company                     2.350877            0.684211  0.298246   \n",
       "Freelance                             2.857143            0.809524  0.333333   \n",
       "Government (Local)                    2.583333            0.666667  0.166667   \n",
       "Government (National)                 2.272727            0.636364  0.090909   \n",
       "Individual Citizen                    2.320000            0.740000  0.490000   \n",
       "Investor                              2.437500            0.875000  0.000000   \n",
       "Non-Profit (Local)                    2.214286            0.714286  0.285714   \n",
       "Non-Profit(International)             2.500000            0.750000  0.000000   \n",
       "Private Research                      2.083333            0.750000  0.500000   \n",
       "Public Services                       2.625000            0.625000  0.000000   \n",
       "Startup & SMEs                        2.971831            0.859155  0.295775   \n",
       "\n",
       "                           whatsapp    wechat     slack  telegram  messenger  \\\n",
       "agent_type                                                                     \n",
       "Academic Research          0.775000  0.025000  0.025000  0.125000   0.225000   \n",
       "Corporate Company          0.649123  0.070175  0.140351  0.087719   0.087719   \n",
       "Freelance                  0.380952  0.095238  0.142857  0.047619   0.142857   \n",
       "Government (Local)         0.916667  0.000000  0.166667  0.166667   0.166667   \n",
       "Government (National)      0.818182  0.000000  0.000000  0.000000   0.181818   \n",
       "Individual Citizen         0.520000  0.120000  0.160000  0.070000   0.220000   \n",
       "Investor                   0.875000  0.062500  0.125000  0.250000   0.062500   \n",
       "Non-Profit (Local)         0.571429  0.000000  0.285714  0.214286   0.142857   \n",
       "Non-Profit(International)  1.000000  0.000000  0.000000  0.000000   0.250000   \n",
       "Private Research           0.666667  0.333333  0.166667  0.166667   0.333333   \n",
       "Public Services            1.000000  0.000000  0.125000  0.250000   0.000000   \n",
       "Startup & SMEs             0.690141  0.084507  0.323944  0.140845   0.197183   \n",
       "\n",
       "                           other_messaging_app  total_messaging_app  \\\n",
       "agent_type                                                            \n",
       "Academic Research                     0.050000             1.375000   \n",
       "Corporate Company                     0.052632             1.385965   \n",
       "Freelance                             0.047619             1.190476   \n",
       "Government (Local)                    0.000000             1.583333   \n",
       "Government (National)                 0.000000             1.090909   \n",
       "Individual Citizen                    0.050000             1.630000   \n",
       "Investor                              0.000000             1.375000   \n",
       "Non-Profit (Local)                    0.142857             1.642857   \n",
       "Non-Profit(International)             0.000000             1.250000   \n",
       "Private Research                      0.000000             2.166667   \n",
       "Public Services                       0.125000             1.500000   \n",
       "Startup & SMEs                        0.070423             1.802817   \n",
       "\n",
       "                           messaging_score  influencer_national_gov  \\\n",
       "agent_type                                                            \n",
       "Academic Research                 0.350000                 0.425000   \n",
       "Corporate Company                 0.298246                 0.403509   \n",
       "Freelance                         0.238095                 0.285714   \n",
       "Government (Local)                0.416667                 0.416667   \n",
       "Government (National)             0.090909                 0.727273   \n",
       "Individual Citizen                0.400000                 0.390000   \n",
       "Investor                          0.312500                 0.187500   \n",
       "Non-Profit (Local)                0.500000                 0.357143   \n",
       "Non-Profit(International)         0.250000                 0.250000   \n",
       "Private Research                  0.666667                 0.500000   \n",
       "Public Services                   0.375000                 0.500000   \n",
       "Startup & SMEs                    0.549296                 0.380282   \n",
       "\n",
       "                           influencer_local_gov  influencer_public_services  \\\n",
       "agent_type                                                                    \n",
       "Academic Research                      0.250000                    0.150000   \n",
       "Corporate Company                      0.245614                    0.140351   \n",
       "Freelance                              0.095238                    0.047619   \n",
       "Government (Local)                     0.750000                    0.166667   \n",
       "Government (National)                  0.272727                    0.000000   \n",
       "Individual Citizen                     0.320000                    0.200000   \n",
       "Investor                               0.250000                    0.187500   \n",
       "Non-Profit (Local)                     0.357143                    0.214286   \n",
       "Non-Profit(International)              0.000000                    0.000000   \n",
       "Private Research                       0.416667                    0.250000   \n",
       "Public Services                        0.250000                    0.250000   \n",
       "Startup & SMEs                         0.253521                    0.267606   \n",
       "\n",
       "                           influencer_investor  influencer_academic  \\\n",
       "agent_type                                                            \n",
       "Academic Research                     0.100000             0.325000   \n",
       "Corporate Company                     0.140351             0.017544   \n",
       "Freelance                             0.000000             0.190476   \n",
       "Government (Local)                    0.083333             0.000000   \n",
       "Government (National)                 0.000000             0.000000   \n",
       "Individual Citizen                    0.040000             0.040000   \n",
       "Investor                              0.437500             0.062500   \n",
       "Non-Profit (Local)                    0.000000             0.071429   \n",
       "Non-Profit(International)             0.000000             0.250000   \n",
       "Private Research                      0.250000             0.166667   \n",
       "Public Services                       0.125000             0.125000   \n",
       "Startup & SMEs                        0.183099             0.070423   \n",
       "\n",
       "                           influencer_private_research  influencer_corporate  \\\n",
       "agent_type                                                                     \n",
       "Academic Research                             0.200000              0.125000   \n",
       "Corporate Company                             0.140351              0.578947   \n",
       "Freelance                                     0.476190              0.047619   \n",
       "Government (Local)                            0.083333              0.000000   \n",
       "Government (National)                         0.090909              0.181818   \n",
       "Individual Citizen                            0.150000              0.150000   \n",
       "Investor                                      0.437500              0.250000   \n",
       "Non-Profit (Local)                            0.071429              0.214286   \n",
       "Non-Profit(International)                     0.000000              0.000000   \n",
       "Private Research                              0.500000              0.250000   \n",
       "Public Services                               0.250000              0.125000   \n",
       "Startup & SMEs                                0.281690              0.281690   \n",
       "\n",
       "                           influencer_startup  influencer_freelance  \\\n",
       "agent_type                                                            \n",
       "Academic Research                    0.075000              0.050000   \n",
       "Corporate Company                    0.157895              0.017544   \n",
       "Freelance                            0.285714              0.380952   \n",
       "Government (Local)                   0.166667              0.000000   \n",
       "Government (National)                0.090909              0.090909   \n",
       "Individual Citizen                   0.110000              0.080000   \n",
       "Investor                             0.125000              0.000000   \n",
       "Non-Profit (Local)                   0.071429              0.071429   \n",
       "Non-Profit(International)            0.000000              0.000000   \n",
       "Private Research                     0.416667              0.083333   \n",
       "Public Services                      0.125000              0.000000   \n",
       "Startup & SMEs                       0.549296              0.098592   \n",
       "\n",
       "                           influencer_local_nonprofit  \\\n",
       "agent_type                                              \n",
       "Academic Research                            0.200000   \n",
       "Corporate Company                            0.052632   \n",
       "Freelance                                    0.190476   \n",
       "Government (Local)                           0.083333   \n",
       "Government (National)                        0.090909   \n",
       "Individual Citizen                           0.250000   \n",
       "Investor                                     0.062500   \n",
       "Non-Profit (Local)                           0.642857   \n",
       "Non-Profit(International)                    0.250000   \n",
       "Private Research                             0.166667   \n",
       "Public Services                              0.125000   \n",
       "Startup & SMEs                               0.197183   \n",
       "\n",
       "                           influencer_international_nonprofit  \\\n",
       "agent_type                                                      \n",
       "Academic Research                                    0.225000   \n",
       "Corporate Company                                    0.052632   \n",
       "Freelance                                            0.190476   \n",
       "Government (Local)                                   0.166667   \n",
       "Government (National)                                0.181818   \n",
       "Individual Citizen                                   0.240000   \n",
       "Investor                                             0.000000   \n",
       "Non-Profit (Local)                                   0.214286   \n",
       "Non-Profit(International)                            0.750000   \n",
       "Private Research                                     0.000000   \n",
       "Public Services                                      0.000000   \n",
       "Startup & SMEs                                       0.126761   \n",
       "\n",
       "                           influencer_individual  total_influencers  \\\n",
       "agent_type                                                            \n",
       "Academic Research                       0.225000           2.350000   \n",
       "Corporate Company                       0.140351           2.087719   \n",
       "Freelance                               0.380952           2.571429   \n",
       "Government (Local)                      0.083333           2.000000   \n",
       "Government (National)                   0.272727           2.000000   \n",
       "Individual Citizen                      0.480000           2.450000   \n",
       "Investor                                0.000000           2.000000   \n",
       "Non-Profit (Local)                      0.500000           2.785714   \n",
       "Non-Profit(International)               0.000000           1.500000   \n",
       "Private Research                        0.166667           3.166667   \n",
       "Public Services                         0.375000           2.250000   \n",
       "Startup & SMEs                          0.295775           2.985915   \n",
       "\n",
       "                           neighborhood  global_events  startup_structure  \\\n",
       "agent_type                                                                  \n",
       "Academic Research              0.275000       0.250000           0.300000   \n",
       "Corporate Company              0.105263       0.280702           0.403509   \n",
       "Freelance                      0.285714       0.238095           0.380952   \n",
       "Government (Local)             0.083333       0.416667           0.583333   \n",
       "Government (National)          0.181818       0.181818           0.181818   \n",
       "Individual Citizen             0.200000       0.280000           0.320000   \n",
       "Investor                       0.312500       0.187500           0.687500   \n",
       "Non-Profit (Local)             0.214286       0.214286           0.357143   \n",
       "Non-Profit(International)      0.500000       0.250000           0.250000   \n",
       "Private Research               0.500000       0.333333           0.500000   \n",
       "Public Services                0.375000       0.250000           0.125000   \n",
       "Startup & SMEs                 0.408451       0.338028           0.507042   \n",
       "\n",
       "                           business_clusters  public_infrastructure  \\\n",
       "agent_type                                                            \n",
       "Academic Research                   0.125000               0.150000   \n",
       "Corporate Company                   0.368421               0.157895   \n",
       "Freelance                           0.142857               0.142857   \n",
       "Government (Local)                  0.000000               0.333333   \n",
       "Government (National)               0.090909               0.181818   \n",
       "Individual Citizen                  0.300000               0.230000   \n",
       "Investor                            0.375000               0.000000   \n",
       "Non-Profit (Local)                  0.142857               0.357143   \n",
       "Non-Profit(International)           0.250000               0.250000   \n",
       "Private Research                    0.500000               0.083333   \n",
       "Public Services                     0.125000               0.250000   \n",
       "Startup & SMEs                      0.549296               0.154930   \n",
       "\n",
       "                           governmental_meeting    online  \\\n",
       "agent_type                                                  \n",
       "Academic Research                      0.100000  0.550000   \n",
       "Corporate Company                      0.017544  0.596491   \n",
       "Freelance                              0.000000  0.857143   \n",
       "Government (Local)                     0.416667  0.583333   \n",
       "Government (National)                  0.272727  0.727273   \n",
       "Individual Citizen                     0.070000  0.710000   \n",
       "Investor                               0.000000  0.500000   \n",
       "Non-Profit (Local)                     0.071429  0.714286   \n",
       "Non-Profit(International)              0.000000  0.500000   \n",
       "Private Research                       0.166667  0.916667   \n",
       "Public Services                        0.250000  0.750000   \n",
       "Startup & SMEs                         0.042254  0.563380   \n",
       "\n",
       "                           other_meeting_place  total_meeting_places  \\\n",
       "agent_type                                                             \n",
       "Academic Research                     0.100000              1.850000   \n",
       "Corporate Company                     0.035088              1.964912   \n",
       "Freelance                             0.047619              2.095238   \n",
       "Government (Local)                    0.000000              2.416667   \n",
       "Government (National)                 0.000000              1.818182   \n",
       "Individual Citizen                    0.000000              2.110000   \n",
       "Investor                              0.000000              2.062500   \n",
       "Non-Profit (Local)                    0.000000              2.071429   \n",
       "Non-Profit(International)             0.000000              2.000000   \n",
       "Private Research                      0.000000              3.000000   \n",
       "Public Services                       0.000000              2.125000   \n",
       "Startup & SMEs                        0.014085              2.577465   \n",
       "\n",
       "                           opinion_share  share_score  opinion_change  \\\n",
       "agent_type                                                              \n",
       "Academic Research               2.850000     0.625000        2.875000   \n",
       "Corporate Company               3.228070     0.736842        3.017544   \n",
       "Freelance                       3.190476     0.714286        2.857143   \n",
       "Government (Local)              3.750000     0.916667        3.083333   \n",
       "Government (National)           3.181818     0.545455        2.636364   \n",
       "Individual Citizen              3.120000     0.690000        3.200000   \n",
       "Investor                        3.312500     0.625000        3.125000   \n",
       "Non-Profit (Local)              3.785714     0.785714        3.214286   \n",
       "Non-Profit(International)       2.750000     0.750000        2.750000   \n",
       "Private Research                3.500000     0.750000        3.166667   \n",
       "Public Services                 3.625000     0.875000        3.250000   \n",
       "Startup & SMEs                  3.267606     0.774648        3.070423   \n",
       "\n",
       "                           change_score  wish_score  online_survey  workshop  \\\n",
       "agent_type                                                                     \n",
       "Academic Research              0.675000    0.775000       0.550000  0.425000   \n",
       "Corporate Company              0.771930    0.649123       0.789474  0.350877   \n",
       "Freelance                      0.714286    0.809524       0.666667  0.380952   \n",
       "Government (Local)             0.916667    0.916667       0.833333  0.416667   \n",
       "Government (National)          0.545455    0.636364       0.545455  0.272727   \n",
       "Individual Citizen             0.810000    0.780000       0.790000  0.430000   \n",
       "Investor                       0.750000    0.687500       0.625000  0.312500   \n",
       "Non-Profit (Local)             0.928571    0.928571       0.500000  0.500000   \n",
       "Non-Profit(International)      0.750000    0.750000       0.500000  0.500000   \n",
       "Private Research               0.916667    0.916667       0.833333  0.583333   \n",
       "Public Services                0.750000    0.875000       0.500000  0.250000   \n",
       "Startup & SMEs                 0.830986    0.802817       0.732394  0.408451   \n",
       "\n",
       "                           interview  voting_app   project  total_engagement  \\\n",
       "agent_type                                                                     \n",
       "Academic Research           0.275000    0.600000  0.325000          2.175000   \n",
       "Corporate Company           0.192982    0.631579  0.403509          2.368421   \n",
       "Freelance                   0.333333    0.523810  0.380952          2.285714   \n",
       "Government (Local)          0.333333    0.666667  0.583333          2.833333   \n",
       "Government (National)       0.272727    0.545455  0.454545          2.090909   \n",
       "Individual Citizen          0.300000    0.580000  0.320000          2.420000   \n",
       "Investor                    0.312500    0.562500  0.250000          2.062500   \n",
       "Non-Profit (Local)          0.571429    0.714286  0.500000          2.785714   \n",
       "Non-Profit(International)   0.000000    0.250000  0.750000          2.000000   \n",
       "Private Research            0.583333    0.500000  0.333333          2.833333   \n",
       "Public Services             0.125000    0.625000  0.375000          1.875000   \n",
       "Startup & SMEs              0.366197    0.549296  0.366197          2.422535   \n",
       "\n",
       "                           channels_score  freq_score  engagement_score  \n",
       "agent_type                                                               \n",
       "Academic Research                0.600000    0.850000          0.577500  \n",
       "Corporate Company                0.789474    0.807018          0.605263  \n",
       "Freelance                        0.666667    0.857143          0.638095  \n",
       "Government (Local)               0.916667    0.916667          0.741667  \n",
       "Government (National)            0.636364    0.636364          0.527273  \n",
       "Individual Citizen               0.750000    0.820000          0.656000  \n",
       "Investor                         0.625000    0.625000          0.581250  \n",
       "Non-Profit (Local)               0.714286    1.000000          0.721429  \n",
       "Non-Profit(International)        0.750000    0.750000          0.625000  \n",
       "Private Research                 0.750000    0.916667          0.725000  \n",
       "Public Services                  0.625000    0.875000          0.700000  \n",
       "Startup & SMEs                   0.746479    0.887324          0.697183  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_type']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey_id</th>\n",
       "      <th>agent_id</th>\n",
       "      <th>twitter_profile</th>\n",
       "      <th>engagement_feeling</th>\n",
       "      <th>feeling_score</th>\n",
       "      <th>source_social_medias</th>\n",
       "      <th>source_messaging_app</th>\n",
       "      <th>source_word_mouth</th>\n",
       "      <th>source_internet</th>\n",
       "      <th>source_email</th>\n",
       "      <th>source_print_medias</th>\n",
       "      <th>source_newspapers</th>\n",
       "      <th>source_radio</th>\n",
       "      <th>source_tv</th>\n",
       "      <th>source_city_council</th>\n",
       "      <th>source_other</th>\n",
       "      <th>total_sources</th>\n",
       "      <th>source_score</th>\n",
       "      <th>facebook</th>\n",
       "      <th>twitter</th>\n",
       "      <th>youtube</th>\n",
       "      <th>medium</th>\n",
       "      <th>linkedin</th>\n",
       "      <th>instagram</th>\n",
       "      <th>Pinterest</th>\n",
       "      <th>Snapchat</th>\n",
       "      <th>other_social_media</th>\n",
       "      <th>total_social_medias</th>\n",
       "      <th>social_media_score</th>\n",
       "      <th>line</th>\n",
       "      <th>whatsapp</th>\n",
       "      <th>wechat</th>\n",
       "      <th>slack</th>\n",
       "      <th>telegram</th>\n",
       "      <th>messenger</th>\n",
       "      <th>other_messaging_app</th>\n",
       "      <th>total_messaging_app</th>\n",
       "      <th>messaging_score</th>\n",
       "      <th>influencer_national_gov</th>\n",
       "      <th>influencer_local_gov</th>\n",
       "      <th>influencer_public_services</th>\n",
       "      <th>influencer_investor</th>\n",
       "      <th>influencer_academic</th>\n",
       "      <th>influencer_private_research</th>\n",
       "      <th>influencer_corporate</th>\n",
       "      <th>influencer_startup</th>\n",
       "      <th>influencer_freelance</th>\n",
       "      <th>influencer_local_nonprofit</th>\n",
       "      <th>influencer_international_nonprofit</th>\n",
       "      <th>influencer_individual</th>\n",
       "      <th>total_influencers</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>global_events</th>\n",
       "      <th>startup_structure</th>\n",
       "      <th>business_clusters</th>\n",
       "      <th>public_infrastructure</th>\n",
       "      <th>governmental_meeting</th>\n",
       "      <th>online</th>\n",
       "      <th>other_meeting_place</th>\n",
       "      <th>total_meeting_places</th>\n",
       "      <th>opinion_share</th>\n",
       "      <th>share_score</th>\n",
       "      <th>opinion_change</th>\n",
       "      <th>change_score</th>\n",
       "      <th>wish_score</th>\n",
       "      <th>online_survey</th>\n",
       "      <th>workshop</th>\n",
       "      <th>interview</th>\n",
       "      <th>voting_app</th>\n",
       "      <th>project</th>\n",
       "      <th>total_engagement</th>\n",
       "      <th>channels_score</th>\n",
       "      <th>freq_score</th>\n",
       "      <th>engagement_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Civil Society</th>\n",
       "      <td>111.153539</td>\n",
       "      <td>111.153539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.172634</td>\n",
       "      <td>0.472402</td>\n",
       "      <td>0.384115</td>\n",
       "      <td>0.441984</td>\n",
       "      <td>0.497495</td>\n",
       "      <td>0.485633</td>\n",
       "      <td>0.421860</td>\n",
       "      <td>0.324748</td>\n",
       "      <td>0.485633</td>\n",
       "      <td>0.369114</td>\n",
       "      <td>0.489791</td>\n",
       "      <td>0.303536</td>\n",
       "      <td>0.220623</td>\n",
       "      <td>1.655394</td>\n",
       "      <td>0.500326</td>\n",
       "      <td>0.352656</td>\n",
       "      <td>0.314436</td>\n",
       "      <td>0.489791</td>\n",
       "      <td>0.291981</td>\n",
       "      <td>0.491637</td>\n",
       "      <td>0.491637</td>\n",
       "      <td>0.129631</td>\n",
       "      <td>0.129631</td>\n",
       "      <td>0.181739</td>\n",
       "      <td>1.075756</td>\n",
       "      <td>0.441984</td>\n",
       "      <td>0.499529</td>\n",
       "      <td>0.500326</td>\n",
       "      <td>0.303536</td>\n",
       "      <td>0.376785</td>\n",
       "      <td>0.279691</td>\n",
       "      <td>0.410372</td>\n",
       "      <td>0.237234</td>\n",
       "      <td>0.905062</td>\n",
       "      <td>0.493328</td>\n",
       "      <td>0.487791</td>\n",
       "      <td>0.465918</td>\n",
       "      <td>0.397825</td>\n",
       "      <td>0.181739</td>\n",
       "      <td>0.220623</td>\n",
       "      <td>0.343816</td>\n",
       "      <td>0.361079</td>\n",
       "      <td>0.303536</td>\n",
       "      <td>0.266563</td>\n",
       "      <td>0.458711</td>\n",
       "      <td>0.437288</td>\n",
       "      <td>0.500977</td>\n",
       "      <td>1.717900</td>\n",
       "      <td>0.410372</td>\n",
       "      <td>0.446468</td>\n",
       "      <td>0.469248</td>\n",
       "      <td>0.450747</td>\n",
       "      <td>0.432374</td>\n",
       "      <td>0.252469</td>\n",
       "      <td>0.458711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.236136</td>\n",
       "      <td>1.300553</td>\n",
       "      <td>0.458711</td>\n",
       "      <td>0.805421</td>\n",
       "      <td>0.384115</td>\n",
       "      <td>0.404237</td>\n",
       "      <td>0.437288</td>\n",
       "      <td>0.498586</td>\n",
       "      <td>0.469248</td>\n",
       "      <td>0.494867</td>\n",
       "      <td>0.480837</td>\n",
       "      <td>1.278337</td>\n",
       "      <td>0.437288</td>\n",
       "      <td>0.369114</td>\n",
       "      <td>0.192951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private Sector</th>\n",
       "      <td>103.503002</td>\n",
       "      <td>103.503002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.425456</td>\n",
       "      <td>0.469120</td>\n",
       "      <td>0.402140</td>\n",
       "      <td>0.415184</td>\n",
       "      <td>0.490635</td>\n",
       "      <td>0.489299</td>\n",
       "      <td>0.410973</td>\n",
       "      <td>0.305751</td>\n",
       "      <td>0.402140</td>\n",
       "      <td>0.297629</td>\n",
       "      <td>0.434339</td>\n",
       "      <td>0.228440</td>\n",
       "      <td>0.188317</td>\n",
       "      <td>1.421009</td>\n",
       "      <td>0.501494</td>\n",
       "      <td>0.402140</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.496132</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.487882</td>\n",
       "      <td>0.501382</td>\n",
       "      <td>0.202760</td>\n",
       "      <td>0.134418</td>\n",
       "      <td>0.110092</td>\n",
       "      <td>1.385189</td>\n",
       "      <td>0.406626</td>\n",
       "      <td>0.447573</td>\n",
       "      <td>0.477654</td>\n",
       "      <td>0.270984</td>\n",
       "      <td>0.410973</td>\n",
       "      <td>0.328232</td>\n",
       "      <td>0.348303</td>\n",
       "      <td>0.228440</td>\n",
       "      <td>0.778470</td>\n",
       "      <td>0.490635</td>\n",
       "      <td>0.479569</td>\n",
       "      <td>0.423215</td>\n",
       "      <td>0.387791</td>\n",
       "      <td>0.377427</td>\n",
       "      <td>0.250915</td>\n",
       "      <td>0.447573</td>\n",
       "      <td>0.479569</td>\n",
       "      <td>0.473564</td>\n",
       "      <td>0.297629</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.297629</td>\n",
       "      <td>0.419263</td>\n",
       "      <td>1.674241</td>\n",
       "      <td>0.450613</td>\n",
       "      <td>0.456388</td>\n",
       "      <td>0.500598</td>\n",
       "      <td>0.494168</td>\n",
       "      <td>0.341853</td>\n",
       "      <td>0.154730</td>\n",
       "      <td>0.490635</td>\n",
       "      <td>0.154730</td>\n",
       "      <td>1.268408</td>\n",
       "      <td>1.227811</td>\n",
       "      <td>0.441175</td>\n",
       "      <td>0.794711</td>\n",
       "      <td>0.410973</td>\n",
       "      <td>0.441175</td>\n",
       "      <td>0.441175</td>\n",
       "      <td>0.486385</td>\n",
       "      <td>0.459125</td>\n",
       "      <td>0.496132</td>\n",
       "      <td>0.483144</td>\n",
       "      <td>1.237174</td>\n",
       "      <td>0.441175</td>\n",
       "      <td>0.377427</td>\n",
       "      <td>0.184126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Sector</th>\n",
       "      <td>72.111745</td>\n",
       "      <td>72.111745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.055930</td>\n",
       "      <td>0.507007</td>\n",
       "      <td>0.420013</td>\n",
       "      <td>0.420013</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.482559</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.336011</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.498991</td>\n",
       "      <td>0.420013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.896251</td>\n",
       "      <td>0.507007</td>\n",
       "      <td>0.296145</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.491869</td>\n",
       "      <td>0.176777</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.507007</td>\n",
       "      <td>0.176777</td>\n",
       "      <td>0.176777</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.436492</td>\n",
       "      <td>0.482559</td>\n",
       "      <td>0.296145</td>\n",
       "      <td>0.296145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.336011</td>\n",
       "      <td>0.336011</td>\n",
       "      <td>0.336011</td>\n",
       "      <td>0.176777</td>\n",
       "      <td>0.665237</td>\n",
       "      <td>0.470929</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.504016</td>\n",
       "      <td>0.368902</td>\n",
       "      <td>0.245935</td>\n",
       "      <td>0.176777</td>\n",
       "      <td>0.336011</td>\n",
       "      <td>0.296145</td>\n",
       "      <td>0.368902</td>\n",
       "      <td>0.176777</td>\n",
       "      <td>0.296145</td>\n",
       "      <td>0.336011</td>\n",
       "      <td>0.420013</td>\n",
       "      <td>1.227622</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.456803</td>\n",
       "      <td>0.482559</td>\n",
       "      <td>0.296145</td>\n",
       "      <td>0.456803</td>\n",
       "      <td>0.470929</td>\n",
       "      <td>0.470929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.229673</td>\n",
       "      <td>1.293595</td>\n",
       "      <td>0.420013</td>\n",
       "      <td>0.822442</td>\n",
       "      <td>0.439941</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.491869</td>\n",
       "      <td>0.470929</td>\n",
       "      <td>0.439941</td>\n",
       "      <td>0.491869</td>\n",
       "      <td>0.508001</td>\n",
       "      <td>1.176038</td>\n",
       "      <td>0.439941</td>\n",
       "      <td>0.396558</td>\n",
       "      <td>0.239287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research &amp; Innovation</th>\n",
       "      <td>106.434897</td>\n",
       "      <td>106.434897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.270790</td>\n",
       "      <td>0.412384</td>\n",
       "      <td>0.412384</td>\n",
       "      <td>0.466041</td>\n",
       "      <td>0.447888</td>\n",
       "      <td>0.498867</td>\n",
       "      <td>0.457467</td>\n",
       "      <td>0.297678</td>\n",
       "      <td>0.364321</td>\n",
       "      <td>0.382005</td>\n",
       "      <td>0.425436</td>\n",
       "      <td>0.138675</td>\n",
       "      <td>0.194184</td>\n",
       "      <td>1.796070</td>\n",
       "      <td>0.503382</td>\n",
       "      <td>0.412384</td>\n",
       "      <td>0.235435</td>\n",
       "      <td>0.498867</td>\n",
       "      <td>0.322603</td>\n",
       "      <td>0.491251</td>\n",
       "      <td>0.491251</td>\n",
       "      <td>0.138675</td>\n",
       "      <td>0.194184</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.257706</td>\n",
       "      <td>0.466041</td>\n",
       "      <td>0.425436</td>\n",
       "      <td>0.437237</td>\n",
       "      <td>0.297678</td>\n",
       "      <td>0.235435</td>\n",
       "      <td>0.344642</td>\n",
       "      <td>0.437237</td>\n",
       "      <td>0.194184</td>\n",
       "      <td>0.872532</td>\n",
       "      <td>0.498867</td>\n",
       "      <td>0.501506</td>\n",
       "      <td>0.457467</td>\n",
       "      <td>0.382005</td>\n",
       "      <td>0.344642</td>\n",
       "      <td>0.457467</td>\n",
       "      <td>0.447888</td>\n",
       "      <td>0.364321</td>\n",
       "      <td>0.364321</td>\n",
       "      <td>0.235435</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.382005</td>\n",
       "      <td>0.412384</td>\n",
       "      <td>1.392720</td>\n",
       "      <td>0.473665</td>\n",
       "      <td>0.447888</td>\n",
       "      <td>0.480384</td>\n",
       "      <td>0.412384</td>\n",
       "      <td>0.344642</td>\n",
       "      <td>0.322603</td>\n",
       "      <td>0.486236</td>\n",
       "      <td>0.269069</td>\n",
       "      <td>1.278078</td>\n",
       "      <td>1.220736</td>\n",
       "      <td>0.480384</td>\n",
       "      <td>0.777463</td>\n",
       "      <td>0.447888</td>\n",
       "      <td>0.397959</td>\n",
       "      <td>0.491251</td>\n",
       "      <td>0.503382</td>\n",
       "      <td>0.480384</td>\n",
       "      <td>0.498867</td>\n",
       "      <td>0.473665</td>\n",
       "      <td>1.353589</td>\n",
       "      <td>0.486236</td>\n",
       "      <td>0.344642</td>\n",
       "      <td>0.214799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        survey_id    agent_id  twitter_profile  \\\n",
       "agent_category                                                   \n",
       "Civil Society          111.153539  111.153539              NaN   \n",
       "Private Sector         103.503002  103.503002              NaN   \n",
       "Public Sector           72.111745   72.111745              NaN   \n",
       "Research & Innovation  106.434897  106.434897              NaN   \n",
       "\n",
       "                       engagement_feeling  feeling_score  \\\n",
       "agent_category                                             \n",
       "Civil Society                    2.172634       0.472402   \n",
       "Private Sector                   2.425456       0.469120   \n",
       "Public Sector                    3.055930       0.507007   \n",
       "Research & Innovation            2.270790       0.412384   \n",
       "\n",
       "                       source_social_medias  source_messaging_app  \\\n",
       "agent_category                                                      \n",
       "Civil Society                      0.384115              0.441984   \n",
       "Private Sector                     0.402140              0.415184   \n",
       "Public Sector                      0.420013              0.420013   \n",
       "Research & Innovation              0.412384              0.466041   \n",
       "\n",
       "                       source_word_mouth  source_internet  source_email  \\\n",
       "agent_category                                                            \n",
       "Civil Society                   0.497495         0.485633      0.421860   \n",
       "Private Sector                  0.490635         0.489299      0.410973   \n",
       "Public Sector                   0.504016         0.482559      0.504016   \n",
       "Research & Innovation           0.447888         0.498867      0.457467   \n",
       "\n",
       "                       source_print_medias  source_newspapers  source_radio  \\\n",
       "agent_category                                                                \n",
       "Civil Society                     0.324748           0.485633      0.369114   \n",
       "Private Sector                    0.305751           0.402140      0.297629   \n",
       "Public Sector                     0.336011           0.396558      0.396558   \n",
       "Research & Innovation             0.297678           0.364321      0.382005   \n",
       "\n",
       "                       source_tv  source_city_council  source_other  \\\n",
       "agent_category                                                        \n",
       "Civil Society           0.489791             0.303536      0.220623   \n",
       "Private Sector          0.434339             0.228440      0.188317   \n",
       "Public Sector           0.498991             0.420013      0.000000   \n",
       "Research & Innovation   0.425436             0.138675      0.194184   \n",
       "\n",
       "                       total_sources  source_score  facebook   twitter  \\\n",
       "agent_category                                                           \n",
       "Civil Society               1.655394      0.500326  0.352656  0.314436   \n",
       "Private Sector              1.421009      0.501494  0.402140  0.341853   \n",
       "Public Sector               1.896251      0.507007  0.296145  0.396558   \n",
       "Research & Innovation       1.796070      0.503382  0.412384  0.235435   \n",
       "\n",
       "                        youtube    medium  linkedin  instagram  Pinterest  \\\n",
       "agent_category                                                              \n",
       "Civil Society          0.489791  0.291981  0.491637   0.491637   0.129631   \n",
       "Private Sector         0.496132  0.341853  0.487882   0.501382   0.202760   \n",
       "Public Sector          0.491869  0.176777  0.504016   0.507007   0.176777   \n",
       "Research & Innovation  0.498867  0.322603  0.491251   0.491251   0.138675   \n",
       "\n",
       "                       Snapchat  other_social_media  total_social_medias  \\\n",
       "agent_category                                                             \n",
       "Civil Society          0.129631            0.181739             1.075756   \n",
       "Private Sector         0.134418            0.110092             1.385189   \n",
       "Public Sector          0.176777            0.000000             1.436492   \n",
       "Research & Innovation  0.194184            0.000000             1.257706   \n",
       "\n",
       "                       social_media_score      line  whatsapp    wechat  \\\n",
       "agent_category                                                            \n",
       "Civil Society                    0.441984  0.499529  0.500326  0.303536   \n",
       "Private Sector                   0.406626  0.447573  0.477654  0.270984   \n",
       "Public Sector                    0.482559  0.296145  0.296145  0.000000   \n",
       "Research & Innovation            0.466041  0.425436  0.437237  0.297678   \n",
       "\n",
       "                          slack  telegram  messenger  other_messaging_app  \\\n",
       "agent_category                                                              \n",
       "Civil Society          0.376785  0.279691   0.410372             0.237234   \n",
       "Private Sector         0.410973  0.328232   0.348303             0.228440   \n",
       "Public Sector          0.336011  0.336011   0.336011             0.176777   \n",
       "Research & Innovation  0.235435  0.344642   0.437237             0.194184   \n",
       "\n",
       "                       total_messaging_app  messaging_score  \\\n",
       "agent_category                                                \n",
       "Civil Society                     0.905062         0.493328   \n",
       "Private Sector                    0.778470         0.490635   \n",
       "Public Sector                     0.665237         0.470929   \n",
       "Research & Innovation             0.872532         0.498867   \n",
       "\n",
       "                       influencer_national_gov  influencer_local_gov  \\\n",
       "agent_category                                                         \n",
       "Civil Society                         0.487791              0.465918   \n",
       "Private Sector                        0.479569              0.423215   \n",
       "Public Sector                         0.504016              0.504016   \n",
       "Research & Innovation                 0.501506              0.457467   \n",
       "\n",
       "                       influencer_public_services  influencer_investor  \\\n",
       "agent_category                                                           \n",
       "Civil Society                            0.397825             0.181739   \n",
       "Private Sector                           0.387791             0.377427   \n",
       "Public Sector                            0.368902             0.245935   \n",
       "Research & Innovation                    0.382005             0.344642   \n",
       "\n",
       "                       influencer_academic  influencer_private_research  \\\n",
       "agent_category                                                            \n",
       "Civil Society                     0.220623                     0.343816   \n",
       "Private Sector                    0.250915                     0.447573   \n",
       "Public Sector                     0.176777                     0.336011   \n",
       "Research & Innovation             0.457467                     0.447888   \n",
       "\n",
       "                       influencer_corporate  influencer_startup  \\\n",
       "agent_category                                                    \n",
       "Civil Society                      0.361079            0.303536   \n",
       "Private Sector                     0.479569            0.473564   \n",
       "Public Sector                      0.296145            0.368902   \n",
       "Research & Innovation              0.364321            0.364321   \n",
       "\n",
       "                       influencer_freelance  influencer_local_nonprofit  \\\n",
       "agent_category                                                            \n",
       "Civil Society                      0.266563                    0.458711   \n",
       "Private Sector                     0.297629                    0.341853   \n",
       "Public Sector                      0.176777                    0.296145   \n",
       "Research & Innovation              0.235435                    0.397959   \n",
       "\n",
       "                       influencer_international_nonprofit  \\\n",
       "agent_category                                              \n",
       "Civil Society                                    0.437288   \n",
       "Private Sector                                   0.297629   \n",
       "Public Sector                                    0.336011   \n",
       "Research & Innovation                            0.382005   \n",
       "\n",
       "                       influencer_individual  total_influencers  neighborhood  \\\n",
       "agent_category                                                                  \n",
       "Civil Society                       0.500977           1.717900      0.410372   \n",
       "Private Sector                      0.419263           1.674241      0.450613   \n",
       "Public Sector                       0.420013           1.227622      0.396558   \n",
       "Research & Innovation               0.412384           1.392720      0.473665   \n",
       "\n",
       "                       global_events  startup_structure  business_clusters  \\\n",
       "agent_category                                                               \n",
       "Civil Society               0.446468           0.469248           0.450747   \n",
       "Private Sector              0.456388           0.500598           0.494168   \n",
       "Public Sector               0.456803           0.482559           0.296145   \n",
       "Research & Innovation       0.447888           0.480384           0.412384   \n",
       "\n",
       "                       public_infrastructure  governmental_meeting    online  \\\n",
       "agent_category                                                                 \n",
       "Civil Society                       0.432374              0.252469  0.458711   \n",
       "Private Sector                      0.341853              0.154730  0.490635   \n",
       "Public Sector                       0.456803              0.470929  0.470929   \n",
       "Research & Innovation               0.344642              0.322603  0.486236   \n",
       "\n",
       "                       other_meeting_place  total_meeting_places  \\\n",
       "agent_category                                                     \n",
       "Civil Society                     0.000000              1.236136   \n",
       "Private Sector                    0.154730              1.268408   \n",
       "Public Sector                     0.000000              1.229673   \n",
       "Research & Innovation             0.269069              1.278078   \n",
       "\n",
       "                       opinion_share  share_score  opinion_change  \\\n",
       "agent_category                                                      \n",
       "Civil Society               1.300553     0.458711        0.805421   \n",
       "Private Sector              1.227811     0.441175        0.794711   \n",
       "Public Sector               1.293595     0.420013        0.822442   \n",
       "Research & Innovation       1.220736     0.480384        0.777463   \n",
       "\n",
       "                       change_score  wish_score  online_survey  workshop  \\\n",
       "agent_category                                                             \n",
       "Civil Society              0.384115    0.404237       0.437288  0.498586   \n",
       "Private Sector             0.410973    0.441175       0.441175  0.486385   \n",
       "Public Sector              0.439941    0.396558       0.491869  0.470929   \n",
       "Research & Innovation      0.447888    0.397959       0.491251  0.503382   \n",
       "\n",
       "                       interview  voting_app   project  total_engagement  \\\n",
       "agent_category                                                             \n",
       "Civil Society           0.469248    0.494867  0.480837          1.278337   \n",
       "Private Sector          0.459125    0.496132  0.483144          1.237174   \n",
       "Public Sector           0.439941    0.491869  0.508001          1.176038   \n",
       "Research & Innovation   0.480384    0.498867  0.473665          1.353589   \n",
       "\n",
       "                       channels_score  freq_score  engagement_score  \n",
       "agent_category                                                       \n",
       "Civil Society                0.437288    0.369114          0.192951  \n",
       "Private Sector               0.441175    0.377427          0.184126  \n",
       "Public Sector                0.439941    0.396558          0.239287  \n",
       "Research & Innovation        0.486236    0.344642          0.214799  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_category']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey_id</th>\n",
       "      <th>agent_id</th>\n",
       "      <th>twitter_profile</th>\n",
       "      <th>engagement_feeling</th>\n",
       "      <th>feeling_score</th>\n",
       "      <th>source_social_medias</th>\n",
       "      <th>source_messaging_app</th>\n",
       "      <th>source_word_mouth</th>\n",
       "      <th>source_internet</th>\n",
       "      <th>source_email</th>\n",
       "      <th>source_print_medias</th>\n",
       "      <th>source_newspapers</th>\n",
       "      <th>source_radio</th>\n",
       "      <th>source_tv</th>\n",
       "      <th>source_city_council</th>\n",
       "      <th>source_other</th>\n",
       "      <th>total_sources</th>\n",
       "      <th>source_score</th>\n",
       "      <th>facebook</th>\n",
       "      <th>twitter</th>\n",
       "      <th>youtube</th>\n",
       "      <th>medium</th>\n",
       "      <th>linkedin</th>\n",
       "      <th>instagram</th>\n",
       "      <th>Pinterest</th>\n",
       "      <th>Snapchat</th>\n",
       "      <th>other_social_media</th>\n",
       "      <th>total_social_medias</th>\n",
       "      <th>social_media_score</th>\n",
       "      <th>line</th>\n",
       "      <th>whatsapp</th>\n",
       "      <th>wechat</th>\n",
       "      <th>slack</th>\n",
       "      <th>telegram</th>\n",
       "      <th>messenger</th>\n",
       "      <th>other_messaging_app</th>\n",
       "      <th>total_messaging_app</th>\n",
       "      <th>messaging_score</th>\n",
       "      <th>influencer_national_gov</th>\n",
       "      <th>influencer_local_gov</th>\n",
       "      <th>influencer_public_services</th>\n",
       "      <th>influencer_investor</th>\n",
       "      <th>influencer_academic</th>\n",
       "      <th>influencer_private_research</th>\n",
       "      <th>influencer_corporate</th>\n",
       "      <th>influencer_startup</th>\n",
       "      <th>influencer_freelance</th>\n",
       "      <th>influencer_local_nonprofit</th>\n",
       "      <th>influencer_international_nonprofit</th>\n",
       "      <th>influencer_individual</th>\n",
       "      <th>total_influencers</th>\n",
       "      <th>neighborhood</th>\n",
       "      <th>global_events</th>\n",
       "      <th>startup_structure</th>\n",
       "      <th>business_clusters</th>\n",
       "      <th>public_infrastructure</th>\n",
       "      <th>governmental_meeting</th>\n",
       "      <th>online</th>\n",
       "      <th>other_meeting_place</th>\n",
       "      <th>total_meeting_places</th>\n",
       "      <th>opinion_share</th>\n",
       "      <th>share_score</th>\n",
       "      <th>opinion_change</th>\n",
       "      <th>change_score</th>\n",
       "      <th>wish_score</th>\n",
       "      <th>online_survey</th>\n",
       "      <th>workshop</th>\n",
       "      <th>interview</th>\n",
       "      <th>voting_app</th>\n",
       "      <th>project</th>\n",
       "      <th>total_engagement</th>\n",
       "      <th>channels_score</th>\n",
       "      <th>freq_score</th>\n",
       "      <th>engagement_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agent_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Academic Research</th>\n",
       "      <td>106.160305</td>\n",
       "      <td>106.160305</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.154899</td>\n",
       "      <td>0.405096</td>\n",
       "      <td>0.422902</td>\n",
       "      <td>0.438529</td>\n",
       "      <td>0.464095</td>\n",
       "      <td>0.496139</td>\n",
       "      <td>0.438529</td>\n",
       "      <td>0.303822</td>\n",
       "      <td>0.361620</td>\n",
       "      <td>0.361620</td>\n",
       "      <td>0.405096</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>0.220721</td>\n",
       "      <td>1.779513</td>\n",
       "      <td>0.505736</td>\n",
       "      <td>0.422902</td>\n",
       "      <td>0.266747</td>\n",
       "      <td>0.496139</td>\n",
       "      <td>0.334932</td>\n",
       "      <td>0.490290</td>\n",
       "      <td>0.500641</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>0.220721</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.354006</td>\n",
       "      <td>0.474342</td>\n",
       "      <td>0.361620</td>\n",
       "      <td>0.422902</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>0.334932</td>\n",
       "      <td>0.422902</td>\n",
       "      <td>0.220721</td>\n",
       "      <td>0.704837</td>\n",
       "      <td>0.483046</td>\n",
       "      <td>0.500641</td>\n",
       "      <td>0.438529</td>\n",
       "      <td>0.361620</td>\n",
       "      <td>0.303822</td>\n",
       "      <td>0.474342</td>\n",
       "      <td>0.405096</td>\n",
       "      <td>0.334932</td>\n",
       "      <td>0.266747</td>\n",
       "      <td>0.220721</td>\n",
       "      <td>0.405096</td>\n",
       "      <td>0.422902</td>\n",
       "      <td>0.422902</td>\n",
       "      <td>1.311683</td>\n",
       "      <td>0.452203</td>\n",
       "      <td>0.438529</td>\n",
       "      <td>0.464095</td>\n",
       "      <td>0.334932</td>\n",
       "      <td>0.361620</td>\n",
       "      <td>0.303822</td>\n",
       "      <td>0.503831</td>\n",
       "      <td>0.303822</td>\n",
       "      <td>1.144664</td>\n",
       "      <td>1.166850</td>\n",
       "      <td>0.490290</td>\n",
       "      <td>0.822364</td>\n",
       "      <td>0.474342</td>\n",
       "      <td>0.422902</td>\n",
       "      <td>0.503831</td>\n",
       "      <td>0.500641</td>\n",
       "      <td>0.452203</td>\n",
       "      <td>0.496139</td>\n",
       "      <td>0.474342</td>\n",
       "      <td>1.337573</td>\n",
       "      <td>0.496139</td>\n",
       "      <td>0.361620</td>\n",
       "      <td>0.214222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corporate Company</th>\n",
       "      <td>110.668112</td>\n",
       "      <td>110.668112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.262531</td>\n",
       "      <td>0.468961</td>\n",
       "      <td>0.453336</td>\n",
       "      <td>0.411306</td>\n",
       "      <td>0.503745</td>\n",
       "      <td>0.498117</td>\n",
       "      <td>0.423318</td>\n",
       "      <td>0.257713</td>\n",
       "      <td>0.398147</td>\n",
       "      <td>0.350438</td>\n",
       "      <td>0.444262</td>\n",
       "      <td>0.225282</td>\n",
       "      <td>0.257713</td>\n",
       "      <td>1.406884</td>\n",
       "      <td>0.494962</td>\n",
       "      <td>0.423318</td>\n",
       "      <td>0.257713</td>\n",
       "      <td>0.481487</td>\n",
       "      <td>0.331133</td>\n",
       "      <td>0.502500</td>\n",
       "      <td>0.502500</td>\n",
       "      <td>0.185637</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.217304</td>\n",
       "      <td>0.468961</td>\n",
       "      <td>0.461555</td>\n",
       "      <td>0.481487</td>\n",
       "      <td>0.257713</td>\n",
       "      <td>0.350438</td>\n",
       "      <td>0.285401</td>\n",
       "      <td>0.285401</td>\n",
       "      <td>0.225282</td>\n",
       "      <td>0.647919</td>\n",
       "      <td>0.461555</td>\n",
       "      <td>0.494962</td>\n",
       "      <td>0.434277</td>\n",
       "      <td>0.350438</td>\n",
       "      <td>0.350438</td>\n",
       "      <td>0.132453</td>\n",
       "      <td>0.350438</td>\n",
       "      <td>0.498117</td>\n",
       "      <td>0.367884</td>\n",
       "      <td>0.132453</td>\n",
       "      <td>0.225282</td>\n",
       "      <td>0.225282</td>\n",
       "      <td>0.350438</td>\n",
       "      <td>1.353312</td>\n",
       "      <td>0.309620</td>\n",
       "      <td>0.453336</td>\n",
       "      <td>0.494962</td>\n",
       "      <td>0.486664</td>\n",
       "      <td>0.367884</td>\n",
       "      <td>0.132453</td>\n",
       "      <td>0.494962</td>\n",
       "      <td>0.185637</td>\n",
       "      <td>1.101378</td>\n",
       "      <td>1.281984</td>\n",
       "      <td>0.444262</td>\n",
       "      <td>0.834335</td>\n",
       "      <td>0.423318</td>\n",
       "      <td>0.481487</td>\n",
       "      <td>0.411306</td>\n",
       "      <td>0.481487</td>\n",
       "      <td>0.398147</td>\n",
       "      <td>0.486664</td>\n",
       "      <td>0.494962</td>\n",
       "      <td>1.112134</td>\n",
       "      <td>0.411306</td>\n",
       "      <td>0.398147</td>\n",
       "      <td>0.179703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freelance</th>\n",
       "      <td>95.036785</td>\n",
       "      <td>95.036785</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.233937</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.511766</td>\n",
       "      <td>0.511766</td>\n",
       "      <td>0.436436</td>\n",
       "      <td>0.300793</td>\n",
       "      <td>0.436436</td>\n",
       "      <td>0.218218</td>\n",
       "      <td>0.483046</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.218218</td>\n",
       "      <td>1.569046</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.511766</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.507093</td>\n",
       "      <td>0.497613</td>\n",
       "      <td>0.300793</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.424279</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.483046</td>\n",
       "      <td>0.497613</td>\n",
       "      <td>0.300793</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.218218</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.218218</td>\n",
       "      <td>0.511766</td>\n",
       "      <td>0.436436</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.300793</td>\n",
       "      <td>0.218218</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.511766</td>\n",
       "      <td>0.218218</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.497613</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.497613</td>\n",
       "      <td>1.989257</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.436436</td>\n",
       "      <td>0.497613</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.218218</td>\n",
       "      <td>1.261141</td>\n",
       "      <td>1.435933</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.963624</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.402374</td>\n",
       "      <td>0.483046</td>\n",
       "      <td>0.497613</td>\n",
       "      <td>0.483046</td>\n",
       "      <td>0.511766</td>\n",
       "      <td>0.497613</td>\n",
       "      <td>1.230563</td>\n",
       "      <td>0.483046</td>\n",
       "      <td>0.358569</td>\n",
       "      <td>0.162715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government (Local)</th>\n",
       "      <td>66.347741</td>\n",
       "      <td>66.347741</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.058768</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.928652</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.564279</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.792961</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.953463</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.083625</td>\n",
       "      <td>1.138180</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>1.267304</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.202073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Government (National)</th>\n",
       "      <td>78.097841</td>\n",
       "      <td>78.097841</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.196589</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.513575</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.348400</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>1.183216</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.404520</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.167748</td>\n",
       "      <td>1.662419</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.674200</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>1.221028</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.504525</td>\n",
       "      <td>0.286674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Individual Citizen</th>\n",
       "      <td>113.845851</td>\n",
       "      <td>113.845851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.183940</td>\n",
       "      <td>0.472582</td>\n",
       "      <td>0.386123</td>\n",
       "      <td>0.451261</td>\n",
       "      <td>0.497570</td>\n",
       "      <td>0.490207</td>\n",
       "      <td>0.422953</td>\n",
       "      <td>0.337998</td>\n",
       "      <td>0.490207</td>\n",
       "      <td>0.377525</td>\n",
       "      <td>0.498888</td>\n",
       "      <td>0.272660</td>\n",
       "      <td>0.219043</td>\n",
       "      <td>1.644427</td>\n",
       "      <td>0.501614</td>\n",
       "      <td>0.348735</td>\n",
       "      <td>0.337998</td>\n",
       "      <td>0.490207</td>\n",
       "      <td>0.301511</td>\n",
       "      <td>0.482418</td>\n",
       "      <td>0.494311</td>\n",
       "      <td>0.140705</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.196946</td>\n",
       "      <td>1.081338</td>\n",
       "      <td>0.440844</td>\n",
       "      <td>0.502418</td>\n",
       "      <td>0.502117</td>\n",
       "      <td>0.326599</td>\n",
       "      <td>0.368453</td>\n",
       "      <td>0.256432</td>\n",
       "      <td>0.416333</td>\n",
       "      <td>0.219043</td>\n",
       "      <td>0.939106</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.490207</td>\n",
       "      <td>0.468826</td>\n",
       "      <td>0.402015</td>\n",
       "      <td>0.196946</td>\n",
       "      <td>0.196946</td>\n",
       "      <td>0.358870</td>\n",
       "      <td>0.358870</td>\n",
       "      <td>0.314466</td>\n",
       "      <td>0.272660</td>\n",
       "      <td>0.435194</td>\n",
       "      <td>0.429235</td>\n",
       "      <td>0.502117</td>\n",
       "      <td>1.665909</td>\n",
       "      <td>0.402015</td>\n",
       "      <td>0.451261</td>\n",
       "      <td>0.468826</td>\n",
       "      <td>0.460566</td>\n",
       "      <td>0.422953</td>\n",
       "      <td>0.256432</td>\n",
       "      <td>0.456048</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.254447</td>\n",
       "      <td>1.281413</td>\n",
       "      <td>0.464823</td>\n",
       "      <td>0.840875</td>\n",
       "      <td>0.394277</td>\n",
       "      <td>0.416333</td>\n",
       "      <td>0.409360</td>\n",
       "      <td>0.497570</td>\n",
       "      <td>0.460566</td>\n",
       "      <td>0.496045</td>\n",
       "      <td>0.468826</td>\n",
       "      <td>1.264751</td>\n",
       "      <td>0.435194</td>\n",
       "      <td>0.386123</td>\n",
       "      <td>0.194011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Investor</th>\n",
       "      <td>71.965472</td>\n",
       "      <td>71.965472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.526526</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.403113</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.516398</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.341565</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.460593</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.403113</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.341565</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.813941</td>\n",
       "      <td>0.341565</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.341565</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.341565</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.619139</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.403113</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.403113</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.341565</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.894427</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.403113</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.516398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.928709</td>\n",
       "      <td>1.195478</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.806226</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.478714</td>\n",
       "      <td>0.512348</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>1.388944</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.219754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Profit (Local)</th>\n",
       "      <td>87.466446</td>\n",
       "      <td>87.466446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.118729</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.363137</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.363137</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>1.780542</td>\n",
       "      <td>0.513553</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.513553</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.050902</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.513553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.363137</td>\n",
       "      <td>0.363137</td>\n",
       "      <td>0.744946</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>2.190138</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.363137</td>\n",
       "      <td>0.497245</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.268814</td>\n",
       "      <td>1.368805</td>\n",
       "      <td>0.425815</td>\n",
       "      <td>0.578934</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.267261</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>0.513553</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.518875</td>\n",
       "      <td>1.476929</td>\n",
       "      <td>0.468807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.204483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Profit(International)</th>\n",
       "      <td>105.031345</td>\n",
       "      <td>105.031345</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.629956</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.707825</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.290994</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.816497</td>\n",
       "      <td>1.258306</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.816497</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.095743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Private Research</th>\n",
       "      <td>91.110780</td>\n",
       "      <td>91.110780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.301185</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.831955</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900337</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.114641</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>1.527525</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.348400</td>\n",
       "      <td>1.314257</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.389249</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.514929</td>\n",
       "      <td>0.522233</td>\n",
       "      <td>0.492366</td>\n",
       "      <td>1.337116</td>\n",
       "      <td>0.452267</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>0.181534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Services</th>\n",
       "      <td>61.372254</td>\n",
       "      <td>61.372254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.159453</td>\n",
       "      <td>0.534522</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.534522</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.534522</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.326094</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.597990</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.755929</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.534522</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>1.752549</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.457738</td>\n",
       "      <td>0.916125</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>1.035098</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.534522</td>\n",
       "      <td>0.462910</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.834523</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>0.353553</td>\n",
       "      <td>0.151186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Startup &amp; SMEs</th>\n",
       "      <td>103.740985</td>\n",
       "      <td>103.740985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.557429</td>\n",
       "      <td>0.481028</td>\n",
       "      <td>0.318447</td>\n",
       "      <td>0.429777</td>\n",
       "      <td>0.471310</td>\n",
       "      <td>0.471310</td>\n",
       "      <td>0.335073</td>\n",
       "      <td>0.350338</td>\n",
       "      <td>0.389500</td>\n",
       "      <td>0.318447</td>\n",
       "      <td>0.438123</td>\n",
       "      <td>0.232214</td>\n",
       "      <td>0.118678</td>\n",
       "      <td>1.396316</td>\n",
       "      <td>0.492193</td>\n",
       "      <td>0.350338</td>\n",
       "      <td>0.377432</td>\n",
       "      <td>0.501105</td>\n",
       "      <td>0.350338</td>\n",
       "      <td>0.471310</td>\n",
       "      <td>0.499497</td>\n",
       "      <td>0.202599</td>\n",
       "      <td>0.202599</td>\n",
       "      <td>0.166633</td>\n",
       "      <td>1.539683</td>\n",
       "      <td>0.350338</td>\n",
       "      <td>0.459639</td>\n",
       "      <td>0.465727</td>\n",
       "      <td>0.280126</td>\n",
       "      <td>0.471310</td>\n",
       "      <td>0.350338</td>\n",
       "      <td>0.400704</td>\n",
       "      <td>0.257679</td>\n",
       "      <td>0.888333</td>\n",
       "      <td>0.501105</td>\n",
       "      <td>0.488911</td>\n",
       "      <td>0.438123</td>\n",
       "      <td>0.445862</td>\n",
       "      <td>0.389500</td>\n",
       "      <td>0.257679</td>\n",
       "      <td>0.453025</td>\n",
       "      <td>0.453025</td>\n",
       "      <td>0.501105</td>\n",
       "      <td>0.300235</td>\n",
       "      <td>0.400704</td>\n",
       "      <td>0.335073</td>\n",
       "      <td>0.459639</td>\n",
       "      <td>1.824382</td>\n",
       "      <td>0.495046</td>\n",
       "      <td>0.476405</td>\n",
       "      <td>0.503509</td>\n",
       "      <td>0.501105</td>\n",
       "      <td>0.364413</td>\n",
       "      <td>0.202599</td>\n",
       "      <td>0.499497</td>\n",
       "      <td>0.118678</td>\n",
       "      <td>1.410795</td>\n",
       "      <td>1.158296</td>\n",
       "      <td>0.420788</td>\n",
       "      <td>0.743043</td>\n",
       "      <td>0.377432</td>\n",
       "      <td>0.400704</td>\n",
       "      <td>0.445862</td>\n",
       "      <td>0.495046</td>\n",
       "      <td>0.485193</td>\n",
       "      <td>0.501105</td>\n",
       "      <td>0.485193</td>\n",
       "      <td>1.305614</td>\n",
       "      <td>0.438123</td>\n",
       "      <td>0.318447</td>\n",
       "      <td>0.176451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            survey_id    agent_id  twitter_profile  \\\n",
       "agent_type                                                           \n",
       "Academic Research          106.160305  106.160305              NaN   \n",
       "Corporate Company          110.668112  110.668112              NaN   \n",
       "Freelance                   95.036785   95.036785              NaN   \n",
       "Government (Local)          66.347741   66.347741              NaN   \n",
       "Government (National)       78.097841   78.097841              NaN   \n",
       "Individual Citizen         113.845851  113.845851              NaN   \n",
       "Investor                    71.965472   71.965472              NaN   \n",
       "Non-Profit (Local)          87.466446   87.466446              NaN   \n",
       "Non-Profit(International)  105.031345  105.031345              NaN   \n",
       "Private Research            91.110780   91.110780              NaN   \n",
       "Public Services             61.372254   61.372254              NaN   \n",
       "Startup & SMEs             103.740985  103.740985              NaN   \n",
       "\n",
       "                           engagement_feeling  feeling_score  \\\n",
       "agent_type                                                     \n",
       "Academic Research                    2.154899       0.405096   \n",
       "Corporate Company                    2.262531       0.468961   \n",
       "Freelance                            2.233937       0.462910   \n",
       "Government (Local)                   3.058768       0.492366   \n",
       "Government (National)                3.196589       0.522233   \n",
       "Individual Citizen                   2.183940       0.472582   \n",
       "Investor                             2.526526       0.447214   \n",
       "Non-Profit (Local)                   2.118729       0.497245   \n",
       "Non-Profit(International)            2.629956       0.500000   \n",
       "Private Research                     2.301185       0.452267   \n",
       "Public Services                      3.159453       0.534522   \n",
       "Startup & SMEs                       2.557429       0.481028   \n",
       "\n",
       "                           source_social_medias  source_messaging_app  \\\n",
       "agent_type                                                              \n",
       "Academic Research                      0.422902              0.438529   \n",
       "Corporate Company                      0.453336              0.411306   \n",
       "Freelance                              0.402374              0.402374   \n",
       "Government (Local)                     0.389249              0.389249   \n",
       "Government (National)                  0.504525              0.301511   \n",
       "Individual Citizen                     0.386123              0.451261   \n",
       "Investor                               0.478714              0.403113   \n",
       "Non-Profit (Local)                     0.425815              0.425815   \n",
       "Non-Profit(International)              0.000000              0.000000   \n",
       "Private Research                       0.389249              0.522233   \n",
       "Public Services                        0.353553              0.534522   \n",
       "Startup & SMEs                         0.318447              0.429777   \n",
       "\n",
       "                           source_word_mouth  source_internet  source_email  \\\n",
       "agent_type                                                                    \n",
       "Academic Research                   0.464095         0.496139      0.438529   \n",
       "Corporate Company                   0.503745         0.498117      0.423318   \n",
       "Freelance                           0.511766         0.511766      0.436436   \n",
       "Government (Local)                  0.514929         0.522233      0.492366   \n",
       "Government (National)               0.522233         0.504525      0.404520   \n",
       "Individual Citizen                  0.497570         0.490207      0.422953   \n",
       "Investor                            0.500000         0.512348      0.516398   \n",
       "Non-Profit (Local)                  0.497245         0.468807      0.468807   \n",
       "Non-Profit(International)           0.500000         0.500000      0.000000   \n",
       "Private Research                    0.389249         0.522233      0.514929   \n",
       "Public Services                     0.517549         0.353553      0.534522   \n",
       "Startup & SMEs                      0.471310         0.471310      0.335073   \n",
       "\n",
       "                           source_print_medias  source_newspapers  \\\n",
       "agent_type                                                          \n",
       "Academic Research                     0.303822           0.361620   \n",
       "Corporate Company                     0.257713           0.398147   \n",
       "Freelance                             0.300793           0.436436   \n",
       "Government (Local)                    0.389249           0.452267   \n",
       "Government (National)                 0.301511           0.404520   \n",
       "Individual Citizen                    0.337998           0.490207   \n",
       "Investor                              0.250000           0.447214   \n",
       "Non-Profit (Local)                    0.267261           0.425815   \n",
       "Non-Profit(International)             0.000000           0.577350   \n",
       "Private Research                      0.288675           0.389249   \n",
       "Public Services                       0.353553           0.353553   \n",
       "Startup & SMEs                        0.350338           0.389500   \n",
       "\n",
       "                           source_radio  source_tv  source_city_council  \\\n",
       "agent_type                                                                \n",
       "Academic Research              0.361620   0.405096             0.158114   \n",
       "Corporate Company              0.350438   0.444262             0.225282   \n",
       "Freelance                      0.218218   0.483046             0.358569   \n",
       "Government (Local)             0.389249   0.514929             0.389249   \n",
       "Government (National)          0.404520   0.504525             0.404520   \n",
       "Individual Citizen             0.377525   0.498888             0.272660   \n",
       "Investor                       0.000000   0.341565             0.000000   \n",
       "Non-Profit (Local)             0.363137   0.267261             0.363137   \n",
       "Non-Profit(International)      0.000000   0.500000             0.577350   \n",
       "Private Research               0.452267   0.492366             0.000000   \n",
       "Public Services                0.353553   0.517549             0.462910   \n",
       "Startup & SMEs                 0.318447   0.438123             0.232214   \n",
       "\n",
       "                           source_other  total_sources  source_score  \\\n",
       "agent_type                                                             \n",
       "Academic Research              0.220721       1.779513      0.505736   \n",
       "Corporate Company              0.257713       1.406884      0.494962   \n",
       "Freelance                      0.218218       1.569046      0.507093   \n",
       "Government (Local)             0.000000       1.928652      0.522233   \n",
       "Government (National)          0.000000       1.513575      0.504525   \n",
       "Individual Citizen             0.219043       1.644427      0.501614   \n",
       "Investor                       0.000000       1.460593      0.512348   \n",
       "Non-Profit (Local)             0.267261       1.780542      0.513553   \n",
       "Non-Profit(International)      0.000000       1.707825      0.500000   \n",
       "Private Research               0.000000       1.831955      0.514929   \n",
       "Public Services                0.000000       2.326094      0.462910   \n",
       "Startup & SMEs                 0.118678       1.396316      0.492193   \n",
       "\n",
       "                           facebook   twitter   youtube    medium  linkedin  \\\n",
       "agent_type                                                                    \n",
       "Academic Research          0.422902  0.266747  0.496139  0.334932  0.490290   \n",
       "Corporate Company          0.423318  0.257713  0.481487  0.331133  0.502500   \n",
       "Freelance                  0.358569  0.358569  0.511766  0.402374  0.507093   \n",
       "Government (Local)         0.288675  0.389249  0.514929  0.288675  0.514929   \n",
       "Government (National)      0.301511  0.404520  0.522233  0.000000  0.467099   \n",
       "Individual Citizen         0.348735  0.337998  0.490207  0.301511  0.482418   \n",
       "Investor                   0.512348  0.403113  0.512348  0.250000  0.341565   \n",
       "Non-Profit (Local)         0.425815  0.000000  0.518875  0.000000  0.513553   \n",
       "Non-Profit(International)  0.000000  0.000000  0.000000  0.500000  0.500000   \n",
       "Private Research           0.389249  0.000000  0.522233  0.288675  0.514929   \n",
       "Public Services            0.353553  0.462910  0.462910  0.000000  0.517549   \n",
       "Startup & SMEs             0.350338  0.377432  0.501105  0.350338  0.471310   \n",
       "\n",
       "                           instagram  Pinterest  Snapchat  other_social_media  \\\n",
       "agent_type                                                                      \n",
       "Academic Research           0.500641   0.158114  0.220721            0.000000   \n",
       "Corporate Company           0.502500   0.185637  0.000000            0.000000   \n",
       "Freelance                   0.497613   0.300793  0.000000            0.000000   \n",
       "Government (Local)          0.522233   0.288675  0.000000            0.000000   \n",
       "Government (National)       0.504525   0.000000  0.301511            0.000000   \n",
       "Individual Citizen          0.494311   0.140705  0.100000            0.196946   \n",
       "Investor                    0.478714   0.000000  0.000000            0.000000   \n",
       "Non-Profit (Local)          0.468807   0.000000  0.267261            0.000000   \n",
       "Non-Profit(International)   0.577350   0.000000  0.000000            0.000000   \n",
       "Private Research            0.452267   0.000000  0.000000            0.000000   \n",
       "Public Services             0.517549   0.000000  0.000000            0.000000   \n",
       "Startup & SMEs              0.499497   0.202599  0.202599            0.166633   \n",
       "\n",
       "                           total_social_medias  social_media_score      line  \\\n",
       "agent_type                                                                     \n",
       "Academic Research                     1.354006            0.474342  0.361620   \n",
       "Corporate Company                     1.217304            0.468961  0.461555   \n",
       "Freelance                             1.424279            0.402374  0.483046   \n",
       "Government (Local)                    1.564279            0.492366  0.389249   \n",
       "Government (National)                 1.348400            0.504525  0.301511   \n",
       "Individual Citizen                    1.081338            0.440844  0.502418   \n",
       "Investor                              0.813941            0.341565  0.000000   \n",
       "Non-Profit (Local)                    1.050902            0.468807  0.468807   \n",
       "Non-Profit(International)             1.290994            0.500000  0.000000   \n",
       "Private Research                      0.900337            0.452267  0.522233   \n",
       "Public Services                       1.597990            0.517549  0.000000   \n",
       "Startup & SMEs                        1.539683            0.350338  0.459639   \n",
       "\n",
       "                           whatsapp    wechat     slack  telegram  messenger  \\\n",
       "agent_type                                                                     \n",
       "Academic Research          0.422902  0.158114  0.158114  0.334932   0.422902   \n",
       "Corporate Company          0.481487  0.257713  0.350438  0.285401   0.285401   \n",
       "Freelance                  0.497613  0.300793  0.358569  0.218218   0.358569   \n",
       "Government (Local)         0.288675  0.000000  0.389249  0.389249   0.389249   \n",
       "Government (National)      0.404520  0.000000  0.000000  0.000000   0.404520   \n",
       "Individual Citizen         0.502117  0.326599  0.368453  0.256432   0.416333   \n",
       "Investor                   0.341565  0.250000  0.341565  0.447214   0.250000   \n",
       "Non-Profit (Local)         0.513553  0.000000  0.468807  0.425815   0.363137   \n",
       "Non-Profit(International)  0.000000  0.000000  0.000000  0.000000   0.500000   \n",
       "Private Research           0.492366  0.492366  0.389249  0.389249   0.492366   \n",
       "Public Services            0.000000  0.000000  0.353553  0.462910   0.000000   \n",
       "Startup & SMEs             0.465727  0.280126  0.471310  0.350338   0.400704   \n",
       "\n",
       "                           other_messaging_app  total_messaging_app  \\\n",
       "agent_type                                                            \n",
       "Academic Research                     0.220721             0.704837   \n",
       "Corporate Company                     0.225282             0.647919   \n",
       "Freelance                             0.218218             0.511766   \n",
       "Government (Local)                    0.000000             0.792961   \n",
       "Government (National)                 0.000000             0.301511   \n",
       "Individual Citizen                    0.219043             0.939106   \n",
       "Investor                              0.000000             0.619139   \n",
       "Non-Profit (Local)                    0.363137             0.744946   \n",
       "Non-Profit(International)             0.000000             0.500000   \n",
       "Private Research                      0.000000             1.114641   \n",
       "Public Services                       0.353553             0.755929   \n",
       "Startup & SMEs                        0.257679             0.888333   \n",
       "\n",
       "                           messaging_score  influencer_national_gov  \\\n",
       "agent_type                                                            \n",
       "Academic Research                 0.483046                 0.500641   \n",
       "Corporate Company                 0.461555                 0.494962   \n",
       "Freelance                         0.436436                 0.462910   \n",
       "Government (Local)                0.514929                 0.514929   \n",
       "Government (National)             0.301511                 0.467099   \n",
       "Individual Citizen                0.492366                 0.490207   \n",
       "Investor                          0.478714                 0.403113   \n",
       "Non-Profit (Local)                0.518875                 0.497245   \n",
       "Non-Profit(International)         0.500000                 0.500000   \n",
       "Private Research                  0.492366                 0.522233   \n",
       "Public Services                   0.517549                 0.534522   \n",
       "Startup & SMEs                    0.501105                 0.488911   \n",
       "\n",
       "                           influencer_local_gov  influencer_public_services  \\\n",
       "agent_type                                                                    \n",
       "Academic Research                      0.438529                    0.361620   \n",
       "Corporate Company                      0.434277                    0.350438   \n",
       "Freelance                              0.300793                    0.218218   \n",
       "Government (Local)                     0.452267                    0.389249   \n",
       "Government (National)                  0.467099                    0.000000   \n",
       "Individual Citizen                     0.468826                    0.402015   \n",
       "Investor                               0.447214                    0.403113   \n",
       "Non-Profit (Local)                     0.497245                    0.425815   \n",
       "Non-Profit(International)              0.000000                    0.000000   \n",
       "Private Research                       0.514929                    0.452267   \n",
       "Public Services                        0.462910                    0.462910   \n",
       "Startup & SMEs                         0.438123                    0.445862   \n",
       "\n",
       "                           influencer_investor  influencer_academic  \\\n",
       "agent_type                                                            \n",
       "Academic Research                     0.303822             0.474342   \n",
       "Corporate Company                     0.350438             0.132453   \n",
       "Freelance                             0.000000             0.402374   \n",
       "Government (Local)                    0.288675             0.000000   \n",
       "Government (National)                 0.000000             0.000000   \n",
       "Individual Citizen                    0.196946             0.196946   \n",
       "Investor                              0.512348             0.250000   \n",
       "Non-Profit (Local)                    0.000000             0.267261   \n",
       "Non-Profit(International)             0.000000             0.500000   \n",
       "Private Research                      0.452267             0.389249   \n",
       "Public Services                       0.353553             0.353553   \n",
       "Startup & SMEs                        0.389500             0.257679   \n",
       "\n",
       "                           influencer_private_research  influencer_corporate  \\\n",
       "agent_type                                                                     \n",
       "Academic Research                             0.405096              0.334932   \n",
       "Corporate Company                             0.350438              0.498117   \n",
       "Freelance                                     0.511766              0.218218   \n",
       "Government (Local)                            0.288675              0.000000   \n",
       "Government (National)                         0.301511              0.404520   \n",
       "Individual Citizen                            0.358870              0.358870   \n",
       "Investor                                      0.512348              0.447214   \n",
       "Non-Profit (Local)                            0.267261              0.425815   \n",
       "Non-Profit(International)                     0.000000              0.000000   \n",
       "Private Research                              0.522233              0.452267   \n",
       "Public Services                               0.462910              0.353553   \n",
       "Startup & SMEs                                0.453025              0.453025   \n",
       "\n",
       "                           influencer_startup  influencer_freelance  \\\n",
       "agent_type                                                            \n",
       "Academic Research                    0.266747              0.220721   \n",
       "Corporate Company                    0.367884              0.132453   \n",
       "Freelance                            0.462910              0.497613   \n",
       "Government (Local)                   0.389249              0.000000   \n",
       "Government (National)                0.301511              0.301511   \n",
       "Individual Citizen                   0.314466              0.272660   \n",
       "Investor                             0.341565              0.000000   \n",
       "Non-Profit (Local)                   0.267261              0.267261   \n",
       "Non-Profit(International)            0.000000              0.000000   \n",
       "Private Research                     0.514929              0.288675   \n",
       "Public Services                      0.353553              0.000000   \n",
       "Startup & SMEs                       0.501105              0.300235   \n",
       "\n",
       "                           influencer_local_nonprofit  \\\n",
       "agent_type                                              \n",
       "Academic Research                            0.405096   \n",
       "Corporate Company                            0.225282   \n",
       "Freelance                                    0.402374   \n",
       "Government (Local)                           0.288675   \n",
       "Government (National)                        0.301511   \n",
       "Individual Citizen                           0.435194   \n",
       "Investor                                     0.250000   \n",
       "Non-Profit (Local)                           0.497245   \n",
       "Non-Profit(International)                    0.500000   \n",
       "Private Research                             0.389249   \n",
       "Public Services                              0.353553   \n",
       "Startup & SMEs                               0.400704   \n",
       "\n",
       "                           influencer_international_nonprofit  \\\n",
       "agent_type                                                      \n",
       "Academic Research                                    0.422902   \n",
       "Corporate Company                                    0.225282   \n",
       "Freelance                                            0.402374   \n",
       "Government (Local)                                   0.389249   \n",
       "Government (National)                                0.404520   \n",
       "Individual Citizen                                   0.429235   \n",
       "Investor                                             0.000000   \n",
       "Non-Profit (Local)                                   0.425815   \n",
       "Non-Profit(International)                            0.500000   \n",
       "Private Research                                     0.000000   \n",
       "Public Services                                      0.000000   \n",
       "Startup & SMEs                                       0.335073   \n",
       "\n",
       "                           influencer_individual  total_influencers  \\\n",
       "agent_type                                                            \n",
       "Academic Research                       0.422902           1.311683   \n",
       "Corporate Company                       0.350438           1.353312   \n",
       "Freelance                               0.497613           1.989257   \n",
       "Government (Local)                      0.288675           0.953463   \n",
       "Government (National)                   0.467099           1.183216   \n",
       "Individual Citizen                      0.502117           1.665909   \n",
       "Investor                                0.000000           0.894427   \n",
       "Non-Profit (Local)                      0.518875           2.190138   \n",
       "Non-Profit(International)               0.000000           1.000000   \n",
       "Private Research                        0.389249           1.527525   \n",
       "Public Services                         0.517549           1.752549   \n",
       "Startup & SMEs                          0.459639           1.824382   \n",
       "\n",
       "                           neighborhood  global_events  startup_structure  \\\n",
       "agent_type                                                                  \n",
       "Academic Research              0.452203       0.438529           0.464095   \n",
       "Corporate Company              0.309620       0.453336           0.494962   \n",
       "Freelance                      0.462910       0.436436           0.497613   \n",
       "Government (Local)             0.288675       0.514929           0.514929   \n",
       "Government (National)          0.404520       0.404520           0.404520   \n",
       "Individual Citizen             0.402015       0.451261           0.468826   \n",
       "Investor                       0.478714       0.403113           0.478714   \n",
       "Non-Profit (Local)             0.425815       0.425815           0.497245   \n",
       "Non-Profit(International)      0.577350       0.500000           0.500000   \n",
       "Private Research               0.522233       0.492366           0.522233   \n",
       "Public Services                0.517549       0.462910           0.353553   \n",
       "Startup & SMEs                 0.495046       0.476405           0.503509   \n",
       "\n",
       "                           business_clusters  public_infrastructure  \\\n",
       "agent_type                                                            \n",
       "Academic Research                   0.334932               0.361620   \n",
       "Corporate Company                   0.486664               0.367884   \n",
       "Freelance                           0.358569               0.358569   \n",
       "Government (Local)                  0.000000               0.492366   \n",
       "Government (National)               0.301511               0.404520   \n",
       "Individual Citizen                  0.460566               0.422953   \n",
       "Investor                            0.500000               0.000000   \n",
       "Non-Profit (Local)                  0.363137               0.497245   \n",
       "Non-Profit(International)           0.500000               0.500000   \n",
       "Private Research                    0.522233               0.288675   \n",
       "Public Services                     0.353553               0.462910   \n",
       "Startup & SMEs                      0.501105               0.364413   \n",
       "\n",
       "                           governmental_meeting    online  \\\n",
       "agent_type                                                  \n",
       "Academic Research                      0.303822  0.503831   \n",
       "Corporate Company                      0.132453  0.494962   \n",
       "Freelance                              0.000000  0.358569   \n",
       "Government (Local)                     0.514929  0.514929   \n",
       "Government (National)                  0.467099  0.467099   \n",
       "Individual Citizen                     0.256432  0.456048   \n",
       "Investor                               0.000000  0.516398   \n",
       "Non-Profit (Local)                     0.267261  0.468807   \n",
       "Non-Profit(International)              0.000000  0.577350   \n",
       "Private Research                       0.389249  0.288675   \n",
       "Public Services                        0.462910  0.462910   \n",
       "Startup & SMEs                         0.202599  0.499497   \n",
       "\n",
       "                           other_meeting_place  total_meeting_places  \\\n",
       "agent_type                                                             \n",
       "Academic Research                     0.303822              1.144664   \n",
       "Corporate Company                     0.185637              1.101378   \n",
       "Freelance                             0.218218              1.261141   \n",
       "Government (Local)                    0.000000              1.083625   \n",
       "Government (National)                 0.000000              1.167748   \n",
       "Individual Citizen                    0.000000              1.254447   \n",
       "Investor                              0.000000              0.928709   \n",
       "Non-Profit (Local)                    0.000000              1.268814   \n",
       "Non-Profit(International)             0.000000              0.816497   \n",
       "Private Research                      0.000000              1.348400   \n",
       "Public Services                       0.000000              1.457738   \n",
       "Startup & SMEs                        0.118678              1.410795   \n",
       "\n",
       "                           opinion_share  share_score  opinion_change  \\\n",
       "agent_type                                                              \n",
       "Academic Research               1.166850     0.490290        0.822364   \n",
       "Corporate Company               1.281984     0.444262        0.834335   \n",
       "Freelance                       1.435933     0.462910        0.963624   \n",
       "Government (Local)              1.138180     0.288675        0.514929   \n",
       "Government (National)           1.662419     0.522233        0.674200   \n",
       "Individual Citizen              1.281413     0.464823        0.840875   \n",
       "Investor                        1.195478     0.500000        0.806226   \n",
       "Non-Profit (Local)              1.368805     0.425815        0.578934   \n",
       "Non-Profit(International)       1.258306     0.500000        0.500000   \n",
       "Private Research                1.314257     0.452267        0.577350   \n",
       "Public Services                 0.916125     0.353553        1.035098   \n",
       "Startup & SMEs                  1.158296     0.420788        0.743043   \n",
       "\n",
       "                           change_score  wish_score  online_survey  workshop  \\\n",
       "agent_type                                                                     \n",
       "Academic Research              0.474342    0.422902       0.503831  0.500641   \n",
       "Corporate Company              0.423318    0.481487       0.411306  0.481487   \n",
       "Freelance                      0.462910    0.402374       0.483046  0.497613   \n",
       "Government (Local)             0.288675    0.288675       0.389249  0.514929   \n",
       "Government (National)          0.522233    0.504525       0.522233  0.467099   \n",
       "Individual Citizen             0.394277    0.416333       0.409360  0.497570   \n",
       "Investor                       0.447214    0.478714       0.500000  0.478714   \n",
       "Non-Profit (Local)             0.267261    0.267261       0.518875  0.518875   \n",
       "Non-Profit(International)      0.500000    0.500000       0.577350  0.577350   \n",
       "Private Research               0.288675    0.288675       0.389249  0.514929   \n",
       "Public Services                0.462910    0.353553       0.534522  0.462910   \n",
       "Startup & SMEs                 0.377432    0.400704       0.445862  0.495046   \n",
       "\n",
       "                           interview  voting_app   project  total_engagement  \\\n",
       "agent_type                                                                     \n",
       "Academic Research           0.452203    0.496139  0.474342          1.337573   \n",
       "Corporate Company           0.398147    0.486664  0.494962          1.112134   \n",
       "Freelance                   0.483046    0.511766  0.497613          1.230563   \n",
       "Government (Local)          0.492366    0.492366  0.514929          1.267304   \n",
       "Government (National)       0.467099    0.522233  0.522233          1.221028   \n",
       "Individual Citizen          0.460566    0.496045  0.468826          1.264751   \n",
       "Investor                    0.478714    0.512348  0.447214          1.388944   \n",
       "Non-Profit (Local)          0.513553    0.468807  0.518875          1.476929   \n",
       "Non-Profit(International)   0.000000    0.500000  0.500000          0.816497   \n",
       "Private Research            0.514929    0.522233  0.492366          1.337116   \n",
       "Public Services             0.353553    0.517549  0.517549          0.834523   \n",
       "Startup & SMEs              0.485193    0.501105  0.485193          1.305614   \n",
       "\n",
       "                           channels_score  freq_score  engagement_score  \n",
       "agent_type                                                               \n",
       "Academic Research                0.496139    0.361620          0.214222  \n",
       "Corporate Company                0.411306    0.398147          0.179703  \n",
       "Freelance                        0.483046    0.358569          0.162715  \n",
       "Government (Local)               0.288675    0.288675          0.202073  \n",
       "Government (National)            0.504525    0.504525          0.286674  \n",
       "Individual Citizen               0.435194    0.386123          0.194011  \n",
       "Investor                         0.500000    0.500000          0.219754  \n",
       "Non-Profit (Local)               0.468807    0.000000          0.204483  \n",
       "Non-Profit(International)        0.500000    0.500000          0.095743  \n",
       "Private Research                 0.452267    0.288675          0.181534  \n",
       "Public Services                  0.517549    0.353553          0.151186  \n",
       "Startup & SMEs                   0.438123    0.318447          0.176451  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_type']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['feeling_mean'] = [0.51, 0.33, 0.37, 0.26, 0.36, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['feeling_std'] = [0.30, 0.22, 0.25, 0.21, 0.21, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['source_social_medias_mean'] = [0.78, 0.71, 0.88, 0.77, 0.82, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['source_social_medias_std'] = [0.42, 0.45, 0.31, 0.42, 0.38, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if corr with social media, drop it\n",
    "stakeholders['total_sources_mean'] = [0.37, 0.30, 0.33, 0.32, 0.37, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if corr with social media, drop it\n",
    "stakeholders['total_sources_std'] = [0.18, 0.14, 0.13, 0.17, 0.16, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_public_mean'] = [0.38, 0.26, 0.29, 0.27, 0.29, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_public_std'] = [0.45, 0.42, 0.45, 0.43, 0.44, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_corpo_mean'] = [0.09, 0.57, 0.45, 0.12, 0.15, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_corpo_std'] = [0.29, 0.49, 0.45, 0.33, 0.36, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_startup_mean'] = [0.15, 0.15, 0.54, 0.07, 0.10, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_startup_std'] = [0.36, 0.36, 0.50, 0.26, 0.30, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_academic_mean'] = [0.03, 0.01, 0.07, 0.32, 0.05, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_academic_std'] = [0.17, 0.13, 0.25, 0.47, 0.22, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_civil_mean'] = [0.15, 0.1, 0.17, 0.17, 0.26, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['influencer_civil_std'] = [0.30, 0.23, 0.37, 0.36, 0.41, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['meet_online_mean'] = [0.68, 0.59, 0.56, 0.55, 0.70, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['meet_online_std'] = [0.47, 0.49, 0.49, 0.50, 0.45, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['opinion_share_mean'] = [0.35, 0.32, 0.32, 0.28, 0.31, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['meet_online_std'] = [0.12, 0.12, 0.11, 0.11, 0.13, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['wish_mean'] = [0.81, 0.64, 0.80, 0.77, 0.79, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['wish_std'] = [0.39, 0.48, 0.40, 0.42, 0.40, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['total_engagement_mean'] = [0.23, 0.23, 0.24, 0.21, 0.24, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['total_engagement_std'] = [0.11, 0.11, 0.13, 0.13, 0.12, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['engagement_score_mean'] = [0.66, 0.60, 0.59, 0.57, 0.66, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['engagement_score_std'] = [0.23, 0.17, 0.17, 0.21, 0.19, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_category         relationship_public\n",
       "Civil Society          Neutral                 83\n",
       "                       Negative                20\n",
       "                       Positive                12\n",
       "                       Neutral ; Negative       2\n",
       "                       Positive ; Neutral       1\n",
       "Private Sector         Neutral                109\n",
       "                       Negative                30\n",
       "                       Positive                23\n",
       "                       Positive ; Neutral       2\n",
       "Public Sector          Positive                17\n",
       "                       Negative                 7\n",
       "                       Neutral                  7\n",
       "                       Positive ; Negative      1\n",
       "Research & Innovation  Neutral                 27\n",
       "                       Negative                13\n",
       "                       Positive                12\n",
       "Name: relationship_public, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# relationship with public\n",
    "survey.groupby(['agent_category'])['relationship_public'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_type                 relationship_public\n",
       "Academic Research          Neutral                22\n",
       "                           Negative               11\n",
       "                           Positive                7\n",
       "Corporate Company          Neutral                36\n",
       "                           Negative               10\n",
       "                           Positive               10\n",
       "                           Positive ; Neutral      1\n",
       "Freelance                  Neutral                12\n",
       "                           Negative                7\n",
       "                           Positive                1\n",
       "                           Positive ; Neutral      1\n",
       "Government (Local)         Positive                6\n",
       "                           Negative                3\n",
       "                           Neutral                 2\n",
       "                           Positive ; Negative     1\n",
       "Government (National)      Positive                7\n",
       "                           Neutral                 3\n",
       "                           Negative                1\n",
       "Individual Citizen         Neutral                76\n",
       "                           Negative               14\n",
       "                           Positive                8\n",
       "                           Neutral ; Negative      1\n",
       "                           Positive ; Neutral      1\n",
       "Investor                   Neutral                12\n",
       "                           Negative                2\n",
       "                           Positive                2\n",
       "Non-Profit (Local)         Neutral                 6\n",
       "                           Negative                4\n",
       "                           Positive                3\n",
       "                           Neutral ; Negative      1\n",
       "Non-Profit(International)  Negative                2\n",
       "                           Neutral                 1\n",
       "                           Positive                1\n",
       "Private Research           Neutral                 5\n",
       "                           Positive                5\n",
       "                           Negative                2\n",
       "Public Services            Positive                4\n",
       "                           Negative                3\n",
       "                           Neutral                 1\n",
       "Startup & SMEs             Neutral                50\n",
       "                           Negative               11\n",
       "                           Positive               10\n",
       "Name: relationship_public, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_type'])['relationship_public'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am normalising values by : (1*pos + 0,5*neu)/num_survey\n",
    "# num_survey = 32, 57, 71, 40, 118, 0\n",
    "stakeholders['pos_relationship_public'] = [0.64, 0.49, 0.49, 0.45, 0.45, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_category         relationship_private\n",
       "Civil Society          Neutral                 78\n",
       "                       Positive                32\n",
       "                       Negative                 5\n",
       "                       Positive ; Neutral       2\n",
       "                       Neutral ; Negative       1\n",
       "Private Sector         Positive                80\n",
       "                       Neutral                 79\n",
       "                       Negative                 2\n",
       "                       Positive ; Neutral       2\n",
       "                       Positive ; Negative      1\n",
       "Public Sector          Neutral                 16\n",
       "                       Positive                15\n",
       "                       Negative                 1\n",
       "Research & Innovation  Neutral                 32\n",
       "                       Positive                17\n",
       "                       Negative                 3\n",
       "Name: relationship_private, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# relationship with private\n",
    "survey.groupby(['agent_category'])['relationship_private'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_type                 relationship_private\n",
       "Academic Research          Neutral                 26\n",
       "                           Positive                12\n",
       "                           Negative                 2\n",
       "Corporate Company          Positive                30\n",
       "                           Neutral                 26\n",
       "                           Positive ; Neutral       1\n",
       "Freelance                  Neutral                 14\n",
       "                           Positive                 6\n",
       "                           Positive ; Neutral       1\n",
       "Government (Local)         Neutral                  7\n",
       "                           Positive                 5\n",
       "Government (National)      Positive                 6\n",
       "                           Neutral                  4\n",
       "                           Negative                 1\n",
       "Individual Citizen         Neutral                 67\n",
       "                           Positive                27\n",
       "                           Negative                 3\n",
       "                           Positive ; Neutral       2\n",
       "                           Neutral ; Negative       1\n",
       "Investor                   Positive                10\n",
       "                           Neutral                  4\n",
       "                           Negative                 2\n",
       "Non-Profit (Local)         Neutral                  9\n",
       "                           Positive                 3\n",
       "                           Negative                 2\n",
       "Non-Profit(International)  Neutral                  2\n",
       "                           Positive                 2\n",
       "Private Research           Neutral                  6\n",
       "                           Positive                 5\n",
       "                           Negative                 1\n",
       "Public Services            Neutral                  5\n",
       "                           Positive                 3\n",
       "Startup & SMEs             Neutral                 35\n",
       "                           Positive                35\n",
       "                           Positive ; Negative      1\n",
       "Name: relationship_private, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_type'])['relationship_private'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am normalising values by : (1*pos + 0,5*neu)/num_survey\n",
    "# num_survey = 32, 57, 71, 40, 118, 0\n",
    "stakeholders['pos_relationship_private'] = [0.71, 0.75, 0.73, 0.62, 0.60, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_category         relationship_academic\n",
       "Civil Society          Neutral                   76\n",
       "                       Positive                  36\n",
       "                       Negative                   5\n",
       "                       Positive ; Neutral         1\n",
       "Private Sector         Neutral                  109\n",
       "                       Positive                  50\n",
       "                       Negative                   4\n",
       "                       Positive ; Neutral         1\n",
       "Public Sector          Positive                  24\n",
       "                       Neutral                    6\n",
       "                       Negative                   2\n",
       "Research & Innovation  Positive                  32\n",
       "                       Neutral                   18\n",
       "                       Negative                   1\n",
       "                       Positive ; Neutral         1\n",
       "Name: relationship_academic, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# relationship with academic\n",
    "survey.groupby(['agent_category'])['relationship_academic'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_type                 relationship_academic\n",
       "Academic Research          Positive                 30\n",
       "                           Neutral                   9\n",
       "                           Negative                  1\n",
       "Corporate Company          Neutral                  35\n",
       "                           Positive                 21\n",
       "                           Negative                  1\n",
       "Freelance                  Neutral                  13\n",
       "                           Positive                  7\n",
       "                           Positive ; Neutral        1\n",
       "Government (Local)         Positive                  9\n",
       "                           Neutral                   3\n",
       "Government (National)      Positive                  8\n",
       "                           Neutral                   2\n",
       "                           Negative                  1\n",
       "Individual Citizen         Neutral                  67\n",
       "                           Positive                 29\n",
       "                           Negative                  3\n",
       "                           Positive ; Neutral        1\n",
       "Investor                   Neutral                  11\n",
       "                           Positive                  3\n",
       "                           Negative                  2\n",
       "Non-Profit (Local)         Neutral                   7\n",
       "                           Positive                  5\n",
       "                           Negative                  2\n",
       "Non-Profit(International)  Neutral                   2\n",
       "                           Positive                  2\n",
       "Private Research           Neutral                   9\n",
       "                           Positive                  2\n",
       "                           Positive ; Neutral        1\n",
       "Public Services            Positive                  6\n",
       "                           Negative                  1\n",
       "                           Neutral                   1\n",
       "Startup & SMEs             Neutral                  50\n",
       "                           Positive                 20\n",
       "                           Negative                  1\n",
       "Name: relationship_academic, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_type'])['relationship_academic'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am normalising values by : (1*pos + 0,5*neu)/num_survey\n",
    "# num_survey = 32, 57, 71, 40, 118, 0\n",
    "stakeholders['pos_relationship_academic'] = [0.84, 0.66, 0.63, 0.6, 0.62, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_category         relationship_civil\n",
       "Civil Society          Neutral                63\n",
       "                       Positive               47\n",
       "                       Negative                4\n",
       "                       Positive ; Neutral      4\n",
       "Private Sector         Neutral               104\n",
       "                       Positive               49\n",
       "                       Negative               10\n",
       "                       Neutral ; Negative      1\n",
       "Public Sector          Positive               16\n",
       "                       Neutral                15\n",
       "                       Positive ; Neutral      1\n",
       "Research & Innovation  Neutral                30\n",
       "                       Positive               21\n",
       "                       Negative                1\n",
       "Name: relationship_civil, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# relationship with civil\n",
    "survey.groupby(['agent_category'])['relationship_civil'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "agent_type                 relationship_civil\n",
       "Academic Research          Neutral               24\n",
       "                           Positive              16\n",
       "Corporate Company          Neutral               39\n",
       "                           Positive              14\n",
       "                           Negative               4\n",
       "Freelance                  Neutral               13\n",
       "                           Positive               6\n",
       "                           Negative               2\n",
       "Government (Local)         Positive               8\n",
       "                           Neutral                4\n",
       "Government (National)      Neutral                6\n",
       "                           Positive               5\n",
       "Individual Citizen         Neutral               58\n",
       "                           Positive              35\n",
       "                           Negative               4\n",
       "                           Positive ; Neutral     3\n",
       "Investor                   Neutral                9\n",
       "                           Positive               4\n",
       "                           Negative               3\n",
       "Non-Profit (Local)         Positive               9\n",
       "                           Neutral                4\n",
       "                           Positive ; Neutral     1\n",
       "Non-Profit(International)  Positive               3\n",
       "                           Neutral                1\n",
       "Private Research           Neutral                6\n",
       "                           Positive               5\n",
       "                           Negative               1\n",
       "Public Services            Neutral                4\n",
       "                           Positive               3\n",
       "                           Positive ; Neutral     1\n",
       "Startup & SMEs             Neutral               44\n",
       "                           Positive              25\n",
       "                           Negative               1\n",
       "                           Neutral ; Negative     1\n",
       "Name: relationship_civil, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.groupby(['agent_type'])['relationship_civil'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I am normalising values by : (1*pos + 0,5*neu)/num_survey\n",
    "# num_survey = 32, 57, 71, 40, 118, 0\n",
    "stakeholders['pos_relationship_civil'] = [0.71, 0.58, 0.66, 0.7, 0.66, 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_survey</th>\n",
       "      <th>feeling_mean</th>\n",
       "      <th>feeling_std</th>\n",
       "      <th>source_social_medias_mean</th>\n",
       "      <th>source_social_medias_std</th>\n",
       "      <th>total_sources_mean</th>\n",
       "      <th>total_sources_std</th>\n",
       "      <th>influencer_public_mean</th>\n",
       "      <th>influencer_public_std</th>\n",
       "      <th>influencer_corpo_mean</th>\n",
       "      <th>influencer_corpo_std</th>\n",
       "      <th>influencer_startup_mean</th>\n",
       "      <th>influencer_startup_std</th>\n",
       "      <th>influencer_academic_mean</th>\n",
       "      <th>influencer_academic_std</th>\n",
       "      <th>influencer_civil_mean</th>\n",
       "      <th>influencer_civil_std</th>\n",
       "      <th>meet_online_mean</th>\n",
       "      <th>meet_online_std</th>\n",
       "      <th>opinion_share_mean</th>\n",
       "      <th>wish_mean</th>\n",
       "      <th>wish_std</th>\n",
       "      <th>total_engagement_mean</th>\n",
       "      <th>total_engagement_std</th>\n",
       "      <th>engagement_score_mean</th>\n",
       "      <th>engagement_score_std</th>\n",
       "      <th>pos_relationship_public</th>\n",
       "      <th>pos_relationship_private</th>\n",
       "      <th>pos_relationship_academic</th>\n",
       "      <th>pos_relationship_civil</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>public</th>\n",
       "      <td>32</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corpo</th>\n",
       "      <td>57</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>startup</th>\n",
       "      <td>71</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>academic</th>\n",
       "      <td>40</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civil</th>\n",
       "      <td>118</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>media</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_survey feeling_mean feeling_std source_social_medias_mean  \\\n",
       "category                                                                 \n",
       "public           32         0.51        0.30                      0.78   \n",
       "corpo            57         0.33        0.22                      0.71   \n",
       "startup          71         0.37        0.25                      0.88   \n",
       "academic         40         0.26        0.21                      0.77   \n",
       "civil           118         0.36        0.21                      0.82   \n",
       "media           nan          nan         nan                       nan   \n",
       "\n",
       "         source_social_medias_std total_sources_mean total_sources_std  \\\n",
       "category                                                                 \n",
       "public                       0.42               0.37              0.18   \n",
       "corpo                        0.45               0.30              0.14   \n",
       "startup                      0.31               0.33              0.13   \n",
       "academic                     0.42               0.32              0.17   \n",
       "civil                        0.38               0.37              0.16   \n",
       "media                         nan                nan               nan   \n",
       "\n",
       "         influencer_public_mean influencer_public_std influencer_corpo_mean  \\\n",
       "category                                                                      \n",
       "public                     0.38                  0.45                  0.09   \n",
       "corpo                      0.26                  0.42                  0.57   \n",
       "startup                    0.29                  0.45                  0.45   \n",
       "academic                   0.27                  0.43                  0.12   \n",
       "civil                      0.29                  0.44                  0.15   \n",
       "media                       nan                   nan                   nan   \n",
       "\n",
       "         influencer_corpo_std influencer_startup_mean influencer_startup_std  \\\n",
       "category                                                                       \n",
       "public                   0.29                    0.15                   0.36   \n",
       "corpo                    0.49                    0.15                   0.36   \n",
       "startup                  0.45                    0.54                   0.50   \n",
       "academic                 0.33                    0.07                   0.26   \n",
       "civil                    0.36                    0.10                   0.30   \n",
       "media                     nan                     nan                    nan   \n",
       "\n",
       "         influencer_academic_mean influencer_academic_std  \\\n",
       "category                                                    \n",
       "public                       0.03                    0.17   \n",
       "corpo                        0.01                    0.13   \n",
       "startup                      0.07                    0.25   \n",
       "academic                     0.32                    0.47   \n",
       "civil                        0.05                    0.22   \n",
       "media                         nan                     nan   \n",
       "\n",
       "         influencer_civil_mean influencer_civil_std meet_online_mean  \\\n",
       "category                                                               \n",
       "public                    0.15                 0.30             0.68   \n",
       "corpo                     0.10                 0.23             0.59   \n",
       "startup                   0.17                 0.37             0.56   \n",
       "academic                  0.17                 0.36             0.55   \n",
       "civil                     0.26                 0.41             0.70   \n",
       "media                      nan                  nan              nan   \n",
       "\n",
       "         meet_online_std opinion_share_mean wish_mean wish_std  \\\n",
       "category                                                         \n",
       "public              0.12               0.35      0.81     0.39   \n",
       "corpo               0.12               0.32      0.64     0.48   \n",
       "startup             0.11               0.32      0.80     0.40   \n",
       "academic            0.11               0.28      0.77     0.42   \n",
       "civil               0.13               0.31      0.79     0.40   \n",
       "media                nan                nan       nan      nan   \n",
       "\n",
       "         total_engagement_mean total_engagement_std engagement_score_mean  \\\n",
       "category                                                                    \n",
       "public                    0.23                 0.11                  0.66   \n",
       "corpo                     0.23                 0.11                  0.60   \n",
       "startup                   0.24                 0.13                  0.59   \n",
       "academic                  0.21                 0.13                  0.57   \n",
       "civil                     0.24                 0.12                  0.66   \n",
       "media                      nan                  nan                   nan   \n",
       "\n",
       "         engagement_score_std pos_relationship_public  \\\n",
       "category                                                \n",
       "public                   0.23                    0.64   \n",
       "corpo                    0.17                    0.49   \n",
       "startup                  0.17                    0.49   \n",
       "academic                 0.21                    0.45   \n",
       "civil                    0.19                    0.45   \n",
       "media                     nan                     nan   \n",
       "\n",
       "         pos_relationship_private pos_relationship_academic  \\\n",
       "category                                                      \n",
       "public                       0.71                      0.84   \n",
       "corpo                        0.75                      0.66   \n",
       "startup                      0.73                      0.63   \n",
       "academic                     0.62                      0.60   \n",
       "civil                        0.60                      0.62   \n",
       "media                         nan                       nan   \n",
       "\n",
       "         pos_relationship_civil  \n",
       "category                         \n",
       "public                     0.71  \n",
       "corpo                      0.58  \n",
       "startup                    0.66  \n",
       "academic                   0.70  \n",
       "civil                      0.66  \n",
       "media                       nan  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stakeholders.set_index('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. collect data from lexical analysis of twitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tweet files\n",
    "kop_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Taipei/Ko-P/Ko P Tweets & Replies.csv')\n",
    "sinica_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Taipei/Academia Sinica/Academia Sinica Tweets & Replies.csv')\n",
    "asus_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Taipei/Asus/Asus Tweets & Replies.csv')\n",
    "g0v_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Taipei/g0v/g0v Tweets & Replies.csv')\n",
    "gogoro_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Taipei/Gogoro/Gogoro Tweets & Replies.csv')\n",
    "tw_news_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Taipei/Taiwan News/Taiwan News Tweets & Replies.csv')\n",
    "\n",
    "adcash_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tallinn/AdCash/AdCash Tweets & Replies.csv')\n",
    "bolt_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tallinn/Bolt/Bolt Tweets & Replies.csv')\n",
    "cleantech_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tallinn/Cleantech ForEst/Cleantech ForEst Tweets & Replies.csv')\n",
    "postimees_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tallinn/Postimees/Postimees Tweets & Replies.csv')\n",
    "startup_estonia_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tallinn/Startup Estonia/Startup Estonia Tweets & Replies.csv')\n",
    "taltech_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tallinn/TalTech/TalTech Tweets & Replies.csv')\n",
    "\n",
    "checkpoint_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tel Aviv/Check Point Software/Check Point Software Tweets & Replies.csv')\n",
    "hasadna_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tel Aviv/Hasadna/Hasadna Tweets & Replies.csv')\n",
    "hayom_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tel Aviv/Israel Hayom/Israel Hayom Tweets & Replies.csv')\n",
    "moovit_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tel Aviv/Moovit/Moovit Tweets & Replies.csv')\n",
    "huldai_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tel Aviv/Ron Huldai/Ron Huldai Tweets & Replies.csv')\n",
    "telavivuni_tweets = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/Tel Aviv/Tel Aviv University/Tel Aviv University Tweets & Replies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge files by stakeholder category\n",
    "public = pd.concat([ kop_tweets,\n",
    "                     startup_estonia_tweets,\n",
    "                     huldai_tweets\n",
    "                     ], axis=0)\n",
    "\n",
    "corpo = pd.concat([   asus_tweets,\n",
    "                      bolt_tweets,\n",
    "                      checkpoint_tweets\n",
    "                     ], axis=0)\n",
    "\n",
    "startup = pd.concat([ gogoro_tweets,\n",
    "                      adcash_tweets,\n",
    "                      moovit_tweets\n",
    "                     ], axis=0)\n",
    "\n",
    "academic = pd.concat([ sinica_tweets,\n",
    "                       taltech_tweets,\n",
    "                       telavivuni_tweets\n",
    "                     ], axis=0)\n",
    "\n",
    "civil = pd.concat([   g0v_tweets,\n",
    "                      cleantech_tweets,\n",
    "                      hasadna_tweets\n",
    "                     ], axis=0)\n",
    "\n",
    "media = pd.concat([ tw_news_tweets,\n",
    "                    postimees_tweets,\n",
    "                    hayom_tweets\n",
    "                     ], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10701,)\n",
      "(10211,)\n",
      "(9480,)\n",
      "(4906,)\n",
      "(3002,)\n",
      "(9330,)\n"
     ]
    }
   ],
   "source": [
    "categories = [public,\n",
    "             corpo,\n",
    "             startup,\n",
    "             academic,\n",
    "             civil,\n",
    "             media]\n",
    "\n",
    "for x in categories:\n",
    "    print(x['text'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweetDate</th>\n",
       "      <th>twitterId</th>\n",
       "      <th>handle</th>\n",
       "      <th>text</th>\n",
       "      <th>profileUser</th>\n",
       "      <th>name</th>\n",
       "      <th>tweetLink</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>query</th>\n",
       "      <th>type</th>\n",
       "      <th>isReply</th>\n",
       "      <th>replyTo</th>\n",
       "      <th>initialTweet</th>\n",
       "      <th>error</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tue Dec 24 02:18:38 +0000 2019</td>\n",
       "      <td>7.068693e+17</td>\n",
       "      <td>KP_Taipei</td>\n",
       "      <td>On-behalf of the entire City Hall, I wish all ...</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>柯文哲 Ko Wen-Je</td>\n",
       "      <td>https://twitter.com/KP_Taipei/status/120929730...</td>\n",
       "      <td>2019-12-24T07:54:04.496Z</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>tweets-replies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mon Dec 23 02:49:29 +0000 2019</td>\n",
       "      <td>7.068693e+17</td>\n",
       "      <td>KP_Taipei</td>\n",
       "      <td>Very exciting to watch Taipei’s development of...</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>柯文哲 Ko Wen-Je</td>\n",
       "      <td>https://twitter.com/KP_Taipei/status/120894267...</td>\n",
       "      <td>2019-12-24T07:54:04.496Z</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>tweets-replies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mon Dec 23 01:31:42 +0000 2019</td>\n",
       "      <td>7.068693e+17</td>\n",
       "      <td>KP_Taipei</td>\n",
       "      <td>Recently I attended the #AsiaPacificMedicalAca...</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>柯文哲 Ko Wen-Je</td>\n",
       "      <td>https://twitter.com/KP_Taipei/status/120892310...</td>\n",
       "      <td>2019-12-24T07:54:04.496Z</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>tweets-replies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fri Dec 20 08:01:38 +0000 2019</td>\n",
       "      <td>7.068693e+17</td>\n",
       "      <td>KP_Taipei</td>\n",
       "      <td>阿北提醒大家，避免傳染禽流感，早點施打流感疫苗。\\n#阿北關心您\\n#對抗流感大家一起來 h...</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>柯文哲 Ko Wen-Je</td>\n",
       "      <td>https://twitter.com/KP_Taipei/status/120793406...</td>\n",
       "      <td>2019-12-24T07:54:04.496Z</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>tweets-replies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thu Dec 19 08:23:54 +0000 2019</td>\n",
       "      <td>7.068693e+17</td>\n",
       "      <td>KP_Taipei</td>\n",
       "      <td>It was a great honor to meet with the delegati...</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>柯文哲 Ko Wen-Je</td>\n",
       "      <td>https://twitter.com/KP_Taipei/status/120757728...</td>\n",
       "      <td>2019-12-24T07:54:04.496Z</td>\n",
       "      <td>https://twitter.com/KP_Taipei</td>\n",
       "      <td>tweets-replies</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        tweetDate     twitterId     handle  \\\n",
       "0  Tue Dec 24 02:18:38 +0000 2019  7.068693e+17  KP_Taipei   \n",
       "1  Mon Dec 23 02:49:29 +0000 2019  7.068693e+17  KP_Taipei   \n",
       "2  Mon Dec 23 01:31:42 +0000 2019  7.068693e+17  KP_Taipei   \n",
       "3  Fri Dec 20 08:01:38 +0000 2019  7.068693e+17  KP_Taipei   \n",
       "4  Thu Dec 19 08:23:54 +0000 2019  7.068693e+17  KP_Taipei   \n",
       "\n",
       "                                                text  \\\n",
       "0  On-behalf of the entire City Hall, I wish all ...   \n",
       "1  Very exciting to watch Taipei’s development of...   \n",
       "2  Recently I attended the #AsiaPacificMedicalAca...   \n",
       "3  阿北提醒大家，避免傳染禽流感，早點施打流感疫苗。\\n#阿北關心您\\n#對抗流感大家一起來 h...   \n",
       "4  It was a great honor to meet with the delegati...   \n",
       "\n",
       "                     profileUser           name  \\\n",
       "0  https://twitter.com/KP_Taipei  柯文哲 Ko Wen-Je   \n",
       "1  https://twitter.com/KP_Taipei  柯文哲 Ko Wen-Je   \n",
       "2  https://twitter.com/KP_Taipei  柯文哲 Ko Wen-Je   \n",
       "3  https://twitter.com/KP_Taipei  柯文哲 Ko Wen-Je   \n",
       "4  https://twitter.com/KP_Taipei  柯文哲 Ko Wen-Je   \n",
       "\n",
       "                                           tweetLink  \\\n",
       "0  https://twitter.com/KP_Taipei/status/120929730...   \n",
       "1  https://twitter.com/KP_Taipei/status/120894267...   \n",
       "2  https://twitter.com/KP_Taipei/status/120892310...   \n",
       "3  https://twitter.com/KP_Taipei/status/120793406...   \n",
       "4  https://twitter.com/KP_Taipei/status/120757728...   \n",
       "\n",
       "                  timestamp                          query            type  \\\n",
       "0  2019-12-24T07:54:04.496Z  https://twitter.com/KP_Taipei  tweets-replies   \n",
       "1  2019-12-24T07:54:04.496Z  https://twitter.com/KP_Taipei  tweets-replies   \n",
       "2  2019-12-24T07:54:04.496Z  https://twitter.com/KP_Taipei  tweets-replies   \n",
       "3  2019-12-24T07:54:04.496Z  https://twitter.com/KP_Taipei  tweets-replies   \n",
       "4  2019-12-24T07:54:04.496Z  https://twitter.com/KP_Taipei  tweets-replies   \n",
       "\n",
       "  isReply replyTo initialTweet  error Unnamed: 14  \n",
       "0     NaN     NaN          NaN    NaN         NaN  \n",
       "1     NaN     NaN          NaN    NaN         NaN  \n",
       "2     NaN     NaN          NaN    NaN         NaN  \n",
       "3     NaN     NaN          NaN    NaN         NaN  \n",
       "4     NaN     NaN          NaN    NaN         NaN  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_tweets'] = [10701, 10211, 9480, 4906, 3002, 9330]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recover nlp analysis file\n",
    "nlp = pd.read_csv(\"/Users/juliencarbonnell/Desktop/Thèse/FORMATIONS/IRONHACK/BOOTCAMP/U9 - Final Project/final project - multicollinearity.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = nlp.drop([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,27,28])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "category = ['public', 'corpo', 'startup', 'academic', 'civil', 'media']\n",
    "nlp['category'] = category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_tweets</th>\n",
       "      <th>sum_likes</th>\n",
       "      <th>tweets_liked</th>\n",
       "      <th>likes_mean</th>\n",
       "      <th>polarity_mean</th>\n",
       "      <th>polarity_std</th>\n",
       "      <th>subjectivity_mean</th>\n",
       "      <th>subjectivity_std</th>\n",
       "      <th>negative_mean</th>\n",
       "      <th>neutral_mean</th>\n",
       "      <th>positive_mean</th>\n",
       "      <th>negative_std</th>\n",
       "      <th>neutral_std</th>\n",
       "      <th>positive_std</th>\n",
       "      <th>tweet_len_mean</th>\n",
       "      <th>tweet_len_std</th>\n",
       "      <th>num_words_mean</th>\n",
       "      <th>num_words_std</th>\n",
       "      <th>num_sentences_mean</th>\n",
       "      <th>num_sentences_std</th>\n",
       "      <th>avg_word_len_mean</th>\n",
       "      <th>avg_word_len_std</th>\n",
       "      <th>num_token_mean</th>\n",
       "      <th>num_token_std</th>\n",
       "      <th>total_tokens</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>public</th>\n",
       "      <td>5391</td>\n",
       "      <td>26880</td>\n",
       "      <td>550</td>\n",
       "      <td>137</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.14</td>\n",
       "      <td>137.98</td>\n",
       "      <td>55.38</td>\n",
       "      <td>17.90</td>\n",
       "      <td>8.92</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0.79</td>\n",
       "      <td>7.55</td>\n",
       "      <td>4.30</td>\n",
       "      <td>11.71</td>\n",
       "      <td>6.92</td>\n",
       "      <td>5391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>corpo</th>\n",
       "      <td>9969</td>\n",
       "      <td>26880</td>\n",
       "      <td>550</td>\n",
       "      <td>26</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>166.34</td>\n",
       "      <td>72.87</td>\n",
       "      <td>23.43</td>\n",
       "      <td>10.73</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>6.36</td>\n",
       "      <td>1.72</td>\n",
       "      <td>14.05</td>\n",
       "      <td>6.76</td>\n",
       "      <td>9969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>startup</th>\n",
       "      <td>5492</td>\n",
       "      <td>26880</td>\n",
       "      <td>550</td>\n",
       "      <td>14</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.19</td>\n",
       "      <td>136.13</td>\n",
       "      <td>69.42</td>\n",
       "      <td>19.16</td>\n",
       "      <td>10.42</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.69</td>\n",
       "      <td>6.42</td>\n",
       "      <td>1.56</td>\n",
       "      <td>11.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>5492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>academic</th>\n",
       "      <td>4324</td>\n",
       "      <td>26880</td>\n",
       "      <td>550</td>\n",
       "      <td>62</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.15</td>\n",
       "      <td>146.44</td>\n",
       "      <td>63.05</td>\n",
       "      <td>19.05</td>\n",
       "      <td>9.37</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.78</td>\n",
       "      <td>7.37</td>\n",
       "      <td>2.43</td>\n",
       "      <td>11.85</td>\n",
       "      <td>6.37</td>\n",
       "      <td>4324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>civil</th>\n",
       "      <td>2796</td>\n",
       "      <td>26880</td>\n",
       "      <td>550</td>\n",
       "      <td>180</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>122.23</td>\n",
       "      <td>55.43</td>\n",
       "      <td>16.90</td>\n",
       "      <td>8.30</td>\n",
       "      <td>1.45</td>\n",
       "      <td>0.71</td>\n",
       "      <td>6.75</td>\n",
       "      <td>2.45</td>\n",
       "      <td>13.07</td>\n",
       "      <td>7.02</td>\n",
       "      <td>2796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>media</th>\n",
       "      <td>9329</td>\n",
       "      <td>26880</td>\n",
       "      <td>550</td>\n",
       "      <td>2</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.10</td>\n",
       "      <td>114.50</td>\n",
       "      <td>43.42</td>\n",
       "      <td>12.59</td>\n",
       "      <td>6.23</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.41</td>\n",
       "      <td>8.67</td>\n",
       "      <td>1.75</td>\n",
       "      <td>9.34</td>\n",
       "      <td>5.10</td>\n",
       "      <td>9329</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          num_tweets  sum_likes  tweets_liked  likes_mean  polarity_mean  \\\n",
       "category                                                                   \n",
       "public          5391      26880           550         137           0.15   \n",
       "corpo           9969      26880           550          26           0.19   \n",
       "startup         5492      26880           550          14           0.20   \n",
       "academic        4324      26880           550          62           0.18   \n",
       "civil           2796      26880           550         180           0.06   \n",
       "media           9329      26880           550           2           0.03   \n",
       "\n",
       "          polarity_std  subjectivity_mean  subjectivity_std  negative_mean  \\\n",
       "category                                                                     \n",
       "public            0.27               0.26              0.31           0.01   \n",
       "corpo             0.30               0.42              0.32           0.04   \n",
       "startup           0.27               0.37              0.32           0.02   \n",
       "academic          0.28               0.30              0.32           0.02   \n",
       "civil             0.19               0.15              0.27           0.01   \n",
       "media             0.17               0.15              0.26           0.05   \n",
       "\n",
       "          neutral_mean  positive_mean  negative_std  neutral_std  \\\n",
       "category                                                           \n",
       "public            0.88           0.11          0.04         0.15   \n",
       "corpo             0.82           0.15          0.07         0.14   \n",
       "startup           0.82           0.17          0.05         0.19   \n",
       "academic          0.86           0.13          0.05         0.16   \n",
       "civil             0.94           0.05          0.05         0.12   \n",
       "media             0.90           0.05          0.10         0.14   \n",
       "\n",
       "          positive_std  tweet_len_mean  tweet_len_std  num_words_mean  \\\n",
       "category                                                                \n",
       "public            0.14          137.98          55.38           17.90   \n",
       "corpo             0.14          166.34          72.87           23.43   \n",
       "startup           0.19          136.13          69.42           19.16   \n",
       "academic          0.15          146.44          63.05           19.05   \n",
       "civil             0.10          122.23          55.43           16.90   \n",
       "media             0.10          114.50          43.42           12.59   \n",
       "\n",
       "          num_words_std  num_sentences_mean  num_sentences_std  \\\n",
       "category                                                         \n",
       "public             8.92                1.49               0.79   \n",
       "corpo             10.73                1.83               0.91   \n",
       "startup           10.42                1.44               0.69   \n",
       "academic           9.37                1.51               0.78   \n",
       "civil              8.30                1.45               0.71   \n",
       "media              6.23                1.15               0.41   \n",
       "\n",
       "          avg_word_len_mean  avg_word_len_std  num_token_mean  num_token_std  \\\n",
       "category                                                                       \n",
       "public                 7.55              4.30           11.71           6.92   \n",
       "corpo                  6.36              1.72           14.05           6.76   \n",
       "startup                6.42              1.56           11.58           6.81   \n",
       "academic               7.37              2.43           11.85           6.37   \n",
       "civil                  6.75              2.45           13.07           7.02   \n",
       "media                  8.67              1.75            9.34           5.10   \n",
       "\n",
       "          total_tokens  \n",
       "category                \n",
       "public            5391  \n",
       "corpo             9969  \n",
       "startup           5492  \n",
       "academic          4324  \n",
       "civil             2796  \n",
       "media             9329  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.set_index(['category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# source manually\n",
    "stakeholders['max_likes'] = [3872, 157, 1100, 160, 36, 83]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize polarity which is taken on a scale of -1/1\n",
    "# polarity_mean = [0.15, 0.19, 0.20, 0.18, 0.06, 0.03]\n",
    "stakeholders['polarity_mean'] = [0.57, 0.59, 0.60, 0.59, 0.53, 0.51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# polarity_std / 2 = normalized\n",
    "stakeholders['polarity_std'] = [0.13, 0.15, 0.13, 0.14, 0.09, 0.08]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['subjectivity_mean'] = nlp['subjectivity_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['subjectivity_std'] = nlp['subjectivity_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['negative_mean'] = nlp['negative_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['negative_std'] = nlp['negative_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['neutral_mean'] = nlp['neutral_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['neutral_std'] = nlp['neutral_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['positive_mean'] = nlp['positive_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['positive_std'] = nlp['positive_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['tweet_len_mean'] = nlp['tweet_len_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['tweet_len_std'] = nlp['tweet_len_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_words_mean'] = nlp['num_words_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_words_std'] = nlp['num_words_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_sentences_mean'] = nlp['num_sentences_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_sentences_std'] = nlp['num_sentences_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['avg_word_len_mean'] = nlp['avg_word_len_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['avg_word_len_std'] = nlp['avg_word_len_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_token_mean'] = nlp['num_token_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_token_std'] = nlp['num_token_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['total_tokens'] = nlp['total_tokens']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correct error\n",
    "civil = civil[civil['text'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num stop words for each tweet\n",
    "from nltk.corpus import stopwords\n",
    "stop = stopwords.words('english')\n",
    "\n",
    "public['stopwords'] = public['text'].apply(lambda x: len([x for x in x.split() if x in stop]))\n",
    "corpo['stopwords'] = corpo['text'].apply(lambda x: len([x for x in x.split() if x in stop]))\n",
    "startup['stopwords'] = startup['text'].apply(lambda x: len([x for x in x.split() if x in stop]))\n",
    "academic['stopwords'] = academic['text'].apply(lambda x: len([x for x in x.split() if x in stop]))\n",
    "civil['stopwords'] = civil['text'].apply(lambda x: len([x for x in x.split() if x in stop]))\n",
    "media['stopwords'] = media['text'].apply(lambda x: len([x for x in x.split() if x in stop]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.689561723203439\n",
      "6.875918127509548\n",
      "5.17035864978903\n",
      "4.667957602935181\n",
      "2.7234255248250583\n",
      "2.2138263665594855\n",
      "4.6806893417998525\n",
      "4.739623836386037\n",
      "4.41491720316626\n",
      "3.9711010840330156\n",
      "4.258420802191145\n",
      "2.786281270537022\n"
     ]
    }
   ],
   "source": [
    "print(public['stopwords'].mean())\n",
    "print(corpo['stopwords'].mean())\n",
    "print(startup['stopwords'].mean())\n",
    "print(academic['stopwords'].mean())\n",
    "print(civil['stopwords'].mean())\n",
    "print(media['stopwords'].mean())\n",
    "\n",
    "print(public['stopwords'].std())\n",
    "print(corpo['stopwords'].std())\n",
    "print(startup['stopwords'].std())\n",
    "print(academic['stopwords'].std())\n",
    "print(civil['stopwords'].std())\n",
    "print(media['stopwords'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['stopwords_mean'] = [4.68, 6.87, 5.17, 4.66, 2.72, 2.21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['stopwords_std'] = [4.68, 4.73, 4.41, 3.97, 4.25, 2.78]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num punctuation\n",
    "import string\n",
    "\n",
    "def count_punct(text):\n",
    "    count = sum([1 for char in text if char in string.punctuation])\n",
    "    return count\n",
    "\n",
    "public['punctuation'] = public['text'].apply(lambda x: count_punct(x))\n",
    "corpo['punctuation'] = corpo['text'].apply(lambda x: count_punct(x))\n",
    "startup['punctuation'] = startup['text'].apply(lambda x: count_punct(x))\n",
    "academic['punctuation'] = academic['text'].apply(lambda x: count_punct(x))\n",
    "civil['punctuation'] = civil['text'].apply(lambda x: count_punct(x))\n",
    "media['punctuation'] = media['text'].apply(lambda x: count_punct(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.220633585646201\n",
      "10.135833904612673\n",
      "11.056434599156118\n",
      "10.302486750917245\n",
      "10.395201599466844\n",
      "10.703858520900322\n",
      "4.68652955416692\n",
      "5.149485281603566\n",
      "4.97505972557843\n",
      "4.839842914983961\n",
      "5.121500135150241\n",
      "6.5583846132622545\n"
     ]
    }
   ],
   "source": [
    "print(public['punctuation'].mean())\n",
    "print(corpo['punctuation'].mean())\n",
    "print(startup['punctuation'].mean())\n",
    "print(academic['punctuation'].mean())\n",
    "print(civil['punctuation'].mean())\n",
    "print(media['punctuation'].mean())\n",
    "\n",
    "print(public['punctuation'].std())\n",
    "print(corpo['punctuation'].std())\n",
    "print(startup['punctuation'].std())\n",
    "print(academic['punctuation'].std())\n",
    "print(civil['punctuation'].std())\n",
    "print(media['punctuation'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['punctuation_mean'] = [11.22, 10.13, 11.05, 10.30, 10.39, 10.70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['punctuation_std'] = [4.68, 5.14, 4.97, 4.83, 5.12, 6.55]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num hashtags\n",
    "public['hashtags'] = public['text'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
    "corpo['hashtags'] = corpo['text'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
    "startup['hashtags'] = startup['text'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
    "academic['hashtags'] = academic['text'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
    "civil['hashtags'] = civil['text'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))\n",
    "media['hashtags'] = media['text'].apply(lambda x: len([x for x in x.split() if x.startswith('#')]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6833940753200636\n",
      "1.680540593477622\n",
      "1.1839662447257384\n",
      "0.9097024052181003\n",
      "0.5344885038320559\n",
      "0.5186495176848874\n",
      "1.7617254479465143\n",
      "1.789071279925582\n",
      "1.6728144371297864\n",
      "1.35675139420233\n",
      "1.0799198179885723\n",
      "1.211269083560109\n"
     ]
    }
   ],
   "source": [
    "print(public['hashtags'].mean())\n",
    "print(corpo['hashtags'].mean())\n",
    "print(startup['hashtags'].mean())\n",
    "print(academic['hashtags'].mean())\n",
    "print(civil['hashtags'].mean())\n",
    "print(media['hashtags'].mean())\n",
    "\n",
    "print(public['hashtags'].std())\n",
    "print(corpo['hashtags'].std())\n",
    "print(startup['hashtags'].std())\n",
    "print(academic['hashtags'].std())\n",
    "print(civil['hashtags'].std())\n",
    "print(media['hashtags'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['hashtags_mean'] = [1.68, 1.68, 1.18, 0.90, 0.53, 0.51]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['hashtags_std'] = [1.76, 1.78, 1.67, 1.35, 1.07, 1.21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num numerics\n",
    "public['numerics'] = public['text'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
    "corpo['numerics'] = corpo['text'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
    "startup['numerics'] = startup['text'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
    "academic['numerics'] = academic['text'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
    "civil['numerics'] = civil['text'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))\n",
    "media['numerics'] = media['text'].apply(lambda x: len([x for x in x.split() if x.isdigit()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17484347257265676\n",
      "0.24591127215747724\n",
      "0.11909282700421941\n",
      "0.14737056665307785\n",
      "0.1602799066977674\n",
      "0.11961414790996784\n",
      "0.4946317295996914\n",
      "0.5648465243269846\n",
      "0.36697515826076804\n",
      "0.4313057906850306\n",
      "0.48093844543016706\n",
      "0.3652428772880393\n"
     ]
    }
   ],
   "source": [
    "print(public['numerics'].mean())\n",
    "print(corpo['numerics'].mean())\n",
    "print(startup['numerics'].mean())\n",
    "print(academic['numerics'].mean())\n",
    "print(civil['numerics'].mean())\n",
    "print(media['numerics'].mean())\n",
    "\n",
    "print(public['numerics'].std())\n",
    "print(corpo['numerics'].std())\n",
    "print(startup['numerics'].std())\n",
    "print(academic['numerics'].std())\n",
    "print(civil['numerics'].std())\n",
    "print(media['numerics'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['numerics_mean'] = [0.17, 0.24, 0.11, 0.14, 0.16, 0.11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['numerics_std'] = [0.49, 0.56, 0.36, 0.43, 0.48, 0.36]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num upper case\n",
    "public['upper'] = public['text'].apply(lambda x: len([x for x in x.split() if x.isupper()]))\n",
    "corpo['upper'] = corpo['text'].apply(lambda x: len([x for x in x.split() if x.isupper()]))\n",
    "startup['upper'] = startup['text'].apply(lambda x: len([x for x in x.split() if x.isupper()]))\n",
    "academic['upper'] = academic['text'].apply(lambda x: len([x for x in x.split() if x.isupper()]))\n",
    "civil['upper'] = civil['text'].apply(lambda x: len([x for x in x.split() if x.isupper()]))\n",
    "media['upper'] = media['text'].apply(lambda x: len([x for x in x.split() if x.isupper()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6474161293337072\n",
      "0.9670943100577808\n",
      "0.46677215189873417\n",
      "0.8255197717081125\n",
      "0.4241919360213262\n",
      "0.3502679528403001\n",
      "0.8994651087545574\n",
      "1.2600601515490444\n",
      "0.7485635734871035\n",
      "1.0245353910426627\n",
      "0.7176358559986689\n",
      "0.6741100086754898\n"
     ]
    }
   ],
   "source": [
    "print(public['upper'].mean())\n",
    "print(corpo['upper'].mean())\n",
    "print(startup['upper'].mean())\n",
    "print(academic['upper'].mean())\n",
    "print(civil['upper'].mean())\n",
    "print(media['upper'].mean())\n",
    "\n",
    "print(public['upper'].std())\n",
    "print(corpo['upper'].std())\n",
    "print(startup['upper'].std())\n",
    "print(academic['upper'].std())\n",
    "print(civil['upper'].std())\n",
    "print(media['upper'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['upper_mean'] = [0.64, 0.96, 0.46, 0.82, 0.42, 0.35]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['upper_std'] = [0.89, 1.26, 0.74, 1.02, 0.71, 0.67]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bow \n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "def stem_tokens(tokens, stemmer):\n",
    "    stemmed = []\n",
    "    for item in tokens:\n",
    "        stemmed.append(stemmer.stem(item))\n",
    "    return stemmed\n",
    "\n",
    "def tokenize(text):\n",
    "    tokens = word_tokenize(text)\n",
    "    stems = stem_tokens(tokens, stemmer)\n",
    "    return stems\n",
    "\n",
    "vect = CountVectorizer(tokenizer=tokenize, stop_words='english')\n",
    "\n",
    "# define each BoW as a list of thematic words (Lexicons)\n",
    "smartcity = ['digitaltransformation','2.0','smartcity','iot','smart','city','ai','smartcities','innovation','cities','5g','data','bigdata','technology','future','tech','digital','digitaltransformation','machinelearning','analytics','solutions','cloud','datascience','network','security','cybersecurity','urban','people','artificialintelligence','smarthome','internetofthings','autonomous','smart','technologies','robotics','software','research','monitoring','street','sensors','futureofwork','planning','governance','transportation','safecity','building','robot','industry40','startups','development','smartthings','urbanplanning','connected','internet','sustainable','waste','emergingtech','programming','environment','safety','lighting','safety','roads','workplaces','smartbuildings','driverless','selfdriving','smartmobility','futureworkspace','digitalcity','deeplearning','datadriven','application','robots','publicsafety','energyefficiency','labgov','citizens','transformation','controlroom','publicspace','airquality','efficiency','era','smarter','urbaninnovation','internetofeverything','newtown','iotforall','construction','privacy','surveillance','drones','smartbuilding','smartgrid','smartgrids','cybersecurity','policy','powergrid','smartcities','iot','electricity','smartmetering','smartmeters','technology','microgrids','microgrid','smartcity','powergrids','iot','ai','machinelearning','bigdata','serverless','cybersecurity','devcommunity','programming','coding','cloudcomputing','codenewbie','100daysofmlcode','womenintech','innovation','deeplearning','security','machine','digital','industrial','artificial','computing','robot','wearables','artificial','networks','drones','connectivity','wireless','urbanplanning','city','urban','cities','smartcities','architecture','urbanism','urbandesign','cityplanning','urbanization','futurecities','urbaninnovation','bigdata','cityplanner','architects','citylab','urbanplanner','urbanplanners','cityplanners','urbandata','townplanning','planners','neighborhoods','sustainablecities','tacticalurbanism','urbanmobility','pedestrians','neighborhood','citiesforpeople','neighbourhood','rethinkreplace','constructionmanagement','suburban','futurecity','cityvisionnaire','smarttechnology','citygoals','civilengineering','digitaltwin','bigdata','digitaltransformation','pedestrian-friendly','surveillance','technologies','urbandevelopment','cities','urban','urbanplanning'] \n",
    "civictech = ['aiethics','empowerment','learned','communityengagement','communitydevelopment','nonprofit','notforprofit','collaboration','vulnerable','diversity','egov','egovernance','inclusion','ethics','social','engagement','crowdsourcing','humanrights','citizenship','civic','opendata','referendum','rights','citizens','open','social','people','sharingeconomy','hack','citizens','communities','civictech','inequalities','equity','participatory','social','youthempowerment','govtech','localgov','civic','stateandlocal','community','governmentit','statelocalit','opendata','sharing','including','volunteer','social','people','shared','learn','opengov','localgovernment','education','hackathon','civictechto','techforgood','discussion','communities','local','digitalgov','future','online','governments','webmapping','capacity','accessibility','discuss','ethics','citizens','together','initiative','transparency','data4good','publicinteresttech','democracy','volunteers','society','makers','ecitizenship','eresidency','citizenship','egovernment','esafety','digitaltransformation','policy','social','technology','future','citizen','ecivility','digitaleconomy','ideagov','egov','people','edemocracy','eparticipation','education','citizens','progressive','security','ecitizenship','inclusive','watch','startups','concept','digitalnation','netgov','app','privacy','egovernance','tool','democratic','eublockchain','digitaleu','digitalnomad','apps','residents','populationeu','migrationpolicy','migrationsystem','migration','voted','govtech','smartcities','humandevelopment','erisks','ecitizen','artificial','intelligence','opendata','empowerment','leadership','growth','success','youth','wisdom','education','relationship','powerinpurpose','community','beyondlimits','powerwithin','support','empower','empowering','equality','enlightment','selfdiscovery','confidence','selfempowerment','leaders','forward','diversity','empowered','successmindset','cybersecurity','government','security','technology','opendata','open','openscience','opensource','opendataday','community','citizenscience','citizen','citizenship','openaccess','transparence','openbanking','womenwhocode','openapis','policy','grants','decisions','institutions','opengov','social','busopendata','datadriven','citizen','openstreetmap','researchdata','engagement','citizens']\n",
    "infrastructure = ['supply','platform','blockchain','system','internet','battery','batterystorage','energystorage','mobility','storage','blockchain','serverless','grids','blockchain','standards','realestate','construction','driverless','selfdriving','smartmobility','transportation','roads', 'building','traffic','autonomousvehicles','streets','platform','transportation','road','publicspace','realestate','monuments','housing','selfdrivingcars','high-speed','real-estate','mobility','highways','transportation','transport','automotive','transit','buses','cars','futureofmobility','car','travel','publictransport','autonomous','bus','auto','shared','traffic','delivery','emissions','5g','services','connectivity','van','trucks','vehicles','commute','electricvehicles','smartmobility','vehicle','logistics','multimodal','connected','metropolitan','efficiency','monitoring','providers','mobile','futurride','autonomousvehicle','engineer','seat','robotics','parking','cycling','driver','flying','rail','moving','wheelchair','automobile','walks','electricvehicle','electriccars','platform','cctv','drivers','driverless','systems','roads','drones','aviation','selfdriving','robots','electriccar','device','bike','autos','urbanmobility','speed','selfdrivingcars','network','street','urbanplanning','autocar','drive','electrification','streets','driverlesscars','bicycle','zero-emission','engineering','artificialintelligence','emobility','fleet','scooters','bikes','journey','manufacturers','route','logistics','driver','trucking','delivery','shipping','traffic','transport','freight','rentals','road','drivers','bus','mobility','rail','cargo','cars','trucks','trucker','fleet','mechanic','infrastructure','railways','diesel','taxi','truckdriver','truckers','shipments','driving','flights','pickup','airfreight','intelligentlogistics','bicycle','cycling','port','wheels','motors','machinery','ferry','infrastructure','bridge','network','architecture','building','construction','transport','metro','roads','highways','railways','realestate','housing','train','civilengineering','airport','station','connectivity','stations','tunnel','airports','equipment','bridges','km','electrical','highway','tunnels','road']\n",
    "sustainability = ['resource','resources','sustainablefinance','cyberresilience','recycling','resilient','batteries','emission','resilience','energytransition','sustainability','biodiversity','sustainabledevelopment','resources','sustainableurbanplanning','sustainabletransport','sustainable','environment','climatechange','energy','climate','innovation','circulareconomy','environmental','ecofriendly','nature','planet','carbon','recycling','climateaction','plastic','renewableenergy','zerowaste','water','waste','solar','agriculture','recycle','savetheplanet','pollution','emissions','sustainablefashion','hydrogen','eco','sustainableliving','reuse','clean','recycled','greenliving','renewable','circular','gogreen','biodiversity','sustainable','renewables','climatecrisis','organic','eco-friendly','earth','sustainabledevelopment','electric','electricity','efficiency','electricvehicles','recyclable','footprint','sustainablefinance','ocean','greener','co2','plasticfree','climateemergency','greenenergy','waterconservation','carbonfootprint','greenbuilding','energytransition','sustainablity','consumption','globalwarming','resilience','reusable','battery','decompose','aquaculture','oil','compost','circularity','plastics','environment','ecoconscious','savetheplanet','green_action','eco-outreach','eco-friendly','environmental_activism','climateactivism','energyefficiency','agtech','reduction','lessismore','responsibility','🌍','cleanup','cleantech','ethical','sustainably','ecofashion','foodtech','emobility','ecosystem','passivehouse','farmtotable','nature','wildlife','planet','ecofriendly','trees','conservation','climatecrisis','savetheplanet','clean','recycle','natural','biodiversity','tree','plants','emissions','reduce','ecology','zerowaste','forests','globalwarming','garden','species','gogreen','farming','agriculture','eco','plasticpollution','soil','greener','plasticfree','farmers','extinction','reuse','recycled','renewableenergy','circulareconomy','environmentally','deforestation','oceans','fuel','naturelovers','microplastics','airpollution','rivers','plastics','renewables','climateemergency','gas','sustainablefashion','environmentaljustice','eco-friendly','bees','fossil','wastewater','fornature','wildfires','greenhouse','ecosystems','renewables','oilandgas','hydrogen','cleanenergy','greenspaces','resilience','climateemergency','sustainablemobility','sustainabledevelopment','naturebasedsolutions','energyefficiency','actonclimate','solar','renewableenergy','alternativeenergy','solarpower','sustainable','environment','solarpanels','solarpv','scienceandenvironment','solarsystems','solarenergy','energymanagement','greennewdeal','solarenergy']\n",
    "governance = ['partnership','strategies','leadership','responsibility','regulations','ceo','policy','democracy2.0','elections','voter','collaboration','gov','governance','planning','management','localgov','project','partnership','partners','partner','leader','leaders','politicalparties','administration','decentralized','democracy2.0','political','society','minister','vision','crossbordercooperation','political','politics','democratic','government','politicians','elections','voter','public','commonwealth','politicalprogress','voting','debate','national','votingawareness','president','mayor','governance','parliament','campaign','liquiddemocracy','voters','brigade','constitutional','law','country','legalhack','decentralization','representatives','communityactivity','republican','policing','policy','policies','administration','governance','leadership','management','security','cybersecurity','ceo','corpgov','government','public','strategy','corruption','democracy','boardofdirectors','cio','policy','technology','leaders','boards','role','members','report','planning','throwback','organizations','national','directorship','structures','trust','directors','decentralized','countries','chair','discussion','conference','transparency','compensationcommittee','auditcommittee','cto','visionarytalks','riskmanagement','organisations','office','legal','regulations','audit','insights','corporategovernance','organization','voting','governing','organisation','policies','project','executive','responsible','leader','survey','interview','approach','staff','benefits','recruitment','regulatory','reporting','severity','obstacle','presidential','country','elections','institutions','alternative','digitaltransformation','freedom','nation','datavault','datawarehouse','regulation','processes','foundation','critical','control','candidate','promised','inauguration','anticorruption','executives','publicpolicy','principles','strategic','sharing','governments','federated','elected','framework','schedule','risks','certificate','meritocracy','stakeholders','partnership','society','equality','decisions','structure','implement','commitment','decision','driven','citiesalliance','assessment','corporations','cryptocurrencies','dataprotection']\n",
    "entrepreneurship = ['freemarket','investors','crypto','financial','accountability','enterprise','assetmanagement','accountable','company','finance','corporate','contractors','workforce','digitalbanking','entrepreneur','newmarket','investment', 'market','coding','startup','development','maker','fintech','economy','business','economic','finance','markets','investing','stocks','investment','gdp','money','financial','economics','trade','crypto','businesses','investors','retail','bitcoin','employment','job','startups','growth','work','entrepreneur','unemployment','entrepreneurship','sales','manufacturing','working','corporate','companies','funds','jobless','cryptocurrency','economicrecovery','smallbusiness','workers','investments','traders','budget','assets','growing','industries','asset','invest','startup','funding','development','currency','banks','monetary','debt','skills','fintech','fiscal','entrepreneurs','deficit','banking','benefits','rates','paying','billing','billingsoftware','earnings','workfromhome','accounting','personalfinance','ratings','accountant','treasury','equities','subsidies','opportunities','profits','stockstowatch','branding','marketing','customer','firms','localbusiness','capitalism','consumers','startup','startups','innovation','mindset','smallbusiness','businessowner','entrepreneurlife','digitalmarketing','management','founder','branding','growth','company','founders','funding','product','businesses','businessgrowth','sales','coaching','investment','entrepreneurial','venture','businessangel','capital','smallbusinessowner','businessman','customers','businesscoach','speaker','finance','market','skills','coding','ecommerce','entrepreneurmindset','startuplife','pitch','onlinebusiness','employment','businesswoman','socialmediamarketing','girlswhocode','crypto','womeninbusiness','investors','selfemployment','accelerator','incubator','designthinking','growthhacking','enterprise','changemakers','startupbusiness','businessideas','mentors','businesscoaching','businessmotivation','businessgoals','ventures','skill','innovators','prototype','growthmindset','pitching','innovations','businesslife','businessstrategy','smallbusinessowners','financial','companies','entrepreneurlifestyle','franchise','manufactures','beyourownboss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column with list of merged tweets\n",
    "stakeholders['text'] = [public['text'].tolist(), corpo['text'].tolist(), startup['text'].tolist(), academic['text'].tolist(), civil['text'].tolist(), media['text'].tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert the merged tweets lists into strings\n",
    "stakeholders['text'] = stakeholders['text'].apply(lambda x: str(x).strip('[]'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column to lower characters of tweets merged\n",
    "stakeholders['clean_text'] = stakeholders['text'].apply(lambda x: x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert emojis into words\n",
    "from emot.emo_unicode import UNICODE_EMO, EMOTICONS\n",
    "def convert_emojis(text):\n",
    "    for emot in UNICODE_EMO:\n",
    "        text = text.replace(emot, \"_\".join(UNICODE_EMO[emot].replace(\",\",\"\").replace(\":\",\"\").split()))\n",
    "    return text\n",
    "\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: convert_emojis(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert emoticons into words\n",
    "import re\n",
    "def convert_emoticons(text):\n",
    "    for emot in EMOTICONS:\n",
    "        text = re.sub(u'('+emot+')', \"_\".join(EMOTICONS[emot].replace(\",\",\"\").split()), text)\n",
    "    return text\n",
    "\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: convert_emoticons(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove punctuation\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].str.replace('[^\\w\\s]','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove symbols\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: re.sub(r'.,;?!/\\&*$€£`§\"+-%|:_’`()','', str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove stopwords\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: \" \".join(x for x in x.split() if x not in stop))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove urls\n",
    "def remove_url(text):\n",
    "    url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
    "    return url.sub(r'', text)\n",
    "\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: remove_url(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove htmls\n",
    "def remove_html(text):\n",
    "    html=re.compile(r'<.*?>')\n",
    "    return html.sub(r'',text)\n",
    "\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: remove_html(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove # \n",
    "import re\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: re.sub(r'#',' ', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove @mentions\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: re.sub(r'@[A-Za-z0-9]+','', str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove digits\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: re.sub(r'[0-9]+','', str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand contractions \n",
    "contractions = contractions = { \n",
    "\"ain't\": \"am not\",\n",
    "\"aren't\": \"are not\",\n",
    "\"can't\": \"cannot\",\n",
    "\"can't've\": \"cannot have\",\n",
    "\"'cause\": \"because\",\n",
    "\"could've\": \"could have\",\n",
    "\"couldn't\": \"could not\",\n",
    "\"couldn't've\": \"could not have\",\n",
    "\"didn't\": \"did not\",\n",
    "\"doesn't\": \"does not\",\n",
    "\"don't\": \"do not\",\n",
    "\"hadn't\": \"had not\",\n",
    "\"hadn't've\": \"had not have\",\n",
    "\"hasn't\": \"has not\",\n",
    "\"haven't\": \"have not\",\n",
    "\"he'd\": \"he would\",\n",
    "\"he'd've\": \"he would have\",\n",
    "\"he'll\": \"he will\",\n",
    "\"he'll've\": \"he will have\",\n",
    "\"he's\": \"he is\",\n",
    "\"how'd\": \"how did\",\n",
    "\"how'd'y\": \"how do you\",\n",
    "\"how'll\": \"how will\",\n",
    "\"how's\": \"how is\",\n",
    "\"I'd\": \"I would\",\n",
    "\"I'd've\": \"I would have\",\n",
    "\"I'll\": \"I will\",\n",
    "\"I'll've\": \"I will have\",\n",
    "\"I'm\": \"I am\",\n",
    "\"I've\": \"I have\",\n",
    "\"isn't\": \"is not\",\n",
    "\"it'd\": \"it would\",\n",
    "\"it'd've\": \"it would have\",\n",
    "\"it'll\": \"it will\",\n",
    "\"it'll've\": \"it will have\",\n",
    "\"it's\": \"it is\",\n",
    "\"let's\": \"let us\",\n",
    "\"ma'am\": \"madam\",\n",
    "\"mayn't\": \"may not\",\n",
    "\"might've\": \"might have\",\n",
    "\"mightn't\": \"might not\",\n",
    "\"mightn't've\": \"might not have\",\n",
    "\"must've\": \"must have\",\n",
    "\"mustn't\": \"must not\",\n",
    "\"mustn't've\": \"must not have\",\n",
    "\"needn't\": \"need not\",\n",
    "\"needn't've\": \"need not have\",\n",
    "\"o'clock\": \"of the clock\",\n",
    "\"oughtn't\": \"ought not\",\n",
    "\"oughtn't've\": \"ought not have\",\n",
    "\"shan't\": \"shall not\",\n",
    "\"sha'n't\": \"shall not\",\n",
    "\"shan't've\": \"shall not have\",\n",
    "\"she'd\": \"she would\",\n",
    "\"she'd've\": \"she would have\",\n",
    "\"she'll\": \"she will\",\n",
    "\"she'll've\": \"she will have\",\n",
    "\"she's\": \"she is\",\n",
    "\"should've\": \"should have\",\n",
    "\"shouldn't\": \"should not\",\n",
    "\"shouldn't've\": \"should not have\",\n",
    "\"so've\": \"so have\",\n",
    "\"so's\": \"so as / so is\",\n",
    "\"that'd\": \"that would\",\n",
    "\"that'd've\": \"that would have\",\n",
    "\"that's\": \"that is\",\n",
    "\"there'd\": \"there would\",\n",
    "\"there'd've\": \"there would have\",\n",
    "\"there's\": \"there is\",\n",
    "\"they'd\": \"they would\",\n",
    "\"they'd've\": \"they would have\",\n",
    "\"they'll\": \"they will\",\n",
    "\"they'll've\": \"they will have\",\n",
    "\"they're\": \"they are\",\n",
    "\"they've\": \"they have\",\n",
    "\"to've\": \"to have\",\n",
    "\"wasn't\": \"was not\",\n",
    "\"we'd\": \"we would\",\n",
    "\"we'd've\": \"we would have\",\n",
    "\"we'll\": \"we will\",\n",
    "\"we'll've\": \"we will have\",\n",
    "\"we're\": \"we are\",\n",
    "\"we've\": \"we have\",\n",
    "\"weren't\": \"were not\",\n",
    "\"what'll\": \"what will\",\n",
    "\"what'll've\": \"what will have\",\n",
    "\"what're\": \"what are\",\n",
    "\"what's\": \"what is\",\n",
    "\"what've\": \"what have\",\n",
    "\"when's\": \"when is\",\n",
    "\"when've\": \"when have\",\n",
    "\"where'd\": \"where did\",\n",
    "\"where's\": \"where is\",\n",
    "\"where've\": \"where have\",\n",
    "\"who'll\": \"who will\",\n",
    "\"who'll've\": \"who will have\",\n",
    "\"who's\": \"who is\",\n",
    "\"who've\": \"who have\",\n",
    "\"why's\": \"why is\",\n",
    "\"why've\": \"why have\",\n",
    "\"will've\": \"will have\",\n",
    "\"won't\": \"will not\",\n",
    "\"won't've\": \"will not have\",\n",
    "\"would've\": \"would have\",\n",
    "\"wouldn't\": \"would not\",\n",
    "\"wouldn't've\": \"would not have\",\n",
    "\"y'all\": \"you all\",\n",
    "\"y'all'd\": \"you all would\",\n",
    "\"y'all'd've\": \"you all would have\",\n",
    "\"y'all're\": \"you all are\",\n",
    "\"y'all've\": \"you all have\",\n",
    "\"you'd\": \"you would\",\n",
    "\"you'd've\": \"you would have\",\n",
    "\"you'll\": \"you will\",\n",
    "\"you'll've\": \"you will have\",\n",
    "\"you're\": \"you are\",\n",
    "\"you've\": \"you have\"\n",
    "}\n",
    "def cont_to_exp(x):\n",
    "    if type(x) is str:\n",
    "        x = x.replace('\\\\','')\n",
    "        for key in contractions:\n",
    "            value = contractions[key]\n",
    "            x = x.replace(key,value)\n",
    "        return x\n",
    "    else:\n",
    "        return x\n",
    "\n",
    "stakeholders['clean_text'] = stakeholders['clean_text'].apply(lambda x: cont_to_exp(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight of smart-city bow\n",
    "from nltk import word_tokenize\n",
    "\n",
    "sma_vec = vect.fit(smartcity)\n",
    "stakeholders['sma_bow'] = stakeholders['clean_text'].apply(lambda x: np.sum(sma_vec.transform([x]).toarray()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight of civic-tech bow\n",
    "civ_vec = vect.fit(civictech)\n",
    "stakeholders['civ_bow'] = stakeholders['clean_text'].apply(lambda x: np.sum(civ_vec.transform([x]).toarray()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight of infrastructure bow\n",
    "inf_vec = vect.fit(infrastructure)\n",
    "stakeholders['inf_bow'] = stakeholders['clean_text'].apply(lambda x: np.sum(inf_vec.transform([x]).toarray()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight of sustainability bow\n",
    "sus_vec = vect.fit(sustainability)\n",
    "stakeholders['sus_bow'] = stakeholders['clean_text'].apply(lambda x: np.sum(sus_vec.transform([x]).toarray()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight of governance bow\n",
    "gov_vec = vect.fit(governance)\n",
    "stakeholders['gov_bow'] = stakeholders['clean_text'].apply(lambda x: np.sum(gov_vec.transform([x]).toarray()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight of entrepreneurship bow\n",
    "ent_vec = vect.fit(entrepreneurship)\n",
    "stakeholders['ent_bow'] = stakeholders['clean_text'].apply(lambda x: np.sum(ent_vec.transform([x]).toarray()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders = stakeholders.drop(columns=['text','clean_text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>num_survey</th>\n",
       "      <th>feeling_mean</th>\n",
       "      <th>feeling_std</th>\n",
       "      <th>source_social_medias_mean</th>\n",
       "      <th>source_social_medias_std</th>\n",
       "      <th>total_sources_mean</th>\n",
       "      <th>total_sources_std</th>\n",
       "      <th>influencer_public_mean</th>\n",
       "      <th>influencer_public_std</th>\n",
       "      <th>influencer_corpo_mean</th>\n",
       "      <th>influencer_corpo_std</th>\n",
       "      <th>influencer_startup_mean</th>\n",
       "      <th>influencer_startup_std</th>\n",
       "      <th>influencer_academic_mean</th>\n",
       "      <th>influencer_academic_std</th>\n",
       "      <th>influencer_civil_mean</th>\n",
       "      <th>influencer_civil_std</th>\n",
       "      <th>meet_online_mean</th>\n",
       "      <th>meet_online_std</th>\n",
       "      <th>opinion_share_mean</th>\n",
       "      <th>wish_mean</th>\n",
       "      <th>wish_std</th>\n",
       "      <th>total_engagement_mean</th>\n",
       "      <th>total_engagement_std</th>\n",
       "      <th>engagement_score_mean</th>\n",
       "      <th>engagement_score_std</th>\n",
       "      <th>pos_relationship_public</th>\n",
       "      <th>pos_relationship_private</th>\n",
       "      <th>pos_relationship_academic</th>\n",
       "      <th>pos_relationship_civil</th>\n",
       "      <th>num_tweets</th>\n",
       "      <th>max_likes</th>\n",
       "      <th>polarity_mean</th>\n",
       "      <th>polarity_std</th>\n",
       "      <th>subjectivity_mean</th>\n",
       "      <th>subjectivity_std</th>\n",
       "      <th>negative_mean</th>\n",
       "      <th>negative_std</th>\n",
       "      <th>neutral_mean</th>\n",
       "      <th>neutral_std</th>\n",
       "      <th>positive_mean</th>\n",
       "      <th>positive_std</th>\n",
       "      <th>tweet_len_mean</th>\n",
       "      <th>tweet_len_std</th>\n",
       "      <th>num_words_mean</th>\n",
       "      <th>num_words_std</th>\n",
       "      <th>num_sentences_mean</th>\n",
       "      <th>num_sentences_std</th>\n",
       "      <th>avg_word_len_mean</th>\n",
       "      <th>avg_word_len_std</th>\n",
       "      <th>num_token_mean</th>\n",
       "      <th>num_token_std</th>\n",
       "      <th>total_tokens</th>\n",
       "      <th>stopwords_mean</th>\n",
       "      <th>stopwords_std</th>\n",
       "      <th>punctuation_mean</th>\n",
       "      <th>punctuation_std</th>\n",
       "      <th>hashtags_mean</th>\n",
       "      <th>hashtags_std</th>\n",
       "      <th>numerics_mean</th>\n",
       "      <th>numerics_std</th>\n",
       "      <th>upper_mean</th>\n",
       "      <th>upper_std</th>\n",
       "      <th>sma_bow</th>\n",
       "      <th>civ_bow</th>\n",
       "      <th>inf_bow</th>\n",
       "      <th>sus_bow</th>\n",
       "      <th>gov_bow</th>\n",
       "      <th>ent_bow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>public</td>\n",
       "      <td>32</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.71</td>\n",
       "      <td>10701</td>\n",
       "      <td>3872</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.68</td>\n",
       "      <td>4.68</td>\n",
       "      <td>11.22</td>\n",
       "      <td>4.68</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.89</td>\n",
       "      <td>6399</td>\n",
       "      <td>6432</td>\n",
       "      <td>2390</td>\n",
       "      <td>1297</td>\n",
       "      <td>4884</td>\n",
       "      <td>5120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>corpo</td>\n",
       "      <td>57</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.58</td>\n",
       "      <td>10211</td>\n",
       "      <td>157</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.87</td>\n",
       "      <td>4.73</td>\n",
       "      <td>10.13</td>\n",
       "      <td>5.14</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.26</td>\n",
       "      <td>6805</td>\n",
       "      <td>6071</td>\n",
       "      <td>4220</td>\n",
       "      <td>886</td>\n",
       "      <td>6060</td>\n",
       "      <td>3648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>startup</td>\n",
       "      <td>71</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.66</td>\n",
       "      <td>9480</td>\n",
       "      <td>1100</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.17</td>\n",
       "      <td>4.41</td>\n",
       "      <td>11.05</td>\n",
       "      <td>4.97</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5121</td>\n",
       "      <td>4225</td>\n",
       "      <td>6863</td>\n",
       "      <td>3012</td>\n",
       "      <td>2923</td>\n",
       "      <td>2764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>academic</td>\n",
       "      <td>40</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.70</td>\n",
       "      <td>4906</td>\n",
       "      <td>160</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.66</td>\n",
       "      <td>3.97</td>\n",
       "      <td>10.30</td>\n",
       "      <td>4.83</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.82</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2167</td>\n",
       "      <td>1972</td>\n",
       "      <td>610</td>\n",
       "      <td>732</td>\n",
       "      <td>2217</td>\n",
       "      <td>1164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>civil</td>\n",
       "      <td>118</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.66</td>\n",
       "      <td>3002</td>\n",
       "      <td>36</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.72</td>\n",
       "      <td>4.25</td>\n",
       "      <td>10.39</td>\n",
       "      <td>5.12</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1055</td>\n",
       "      <td>1100</td>\n",
       "      <td>355</td>\n",
       "      <td>636</td>\n",
       "      <td>443</td>\n",
       "      <td>917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>media</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>9330</td>\n",
       "      <td>83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.78</td>\n",
       "      <td>10.70</td>\n",
       "      <td>6.55</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1036</td>\n",
       "      <td>1477</td>\n",
       "      <td>779</td>\n",
       "      <td>292</td>\n",
       "      <td>2825</td>\n",
       "      <td>780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   category num_survey feeling_mean feeling_std source_social_medias_mean  \\\n",
       "0    public         32         0.51        0.30                      0.78   \n",
       "1     corpo         57         0.33        0.22                      0.71   \n",
       "2   startup         71         0.37        0.25                      0.88   \n",
       "3  academic         40         0.26        0.21                      0.77   \n",
       "4     civil        118         0.36        0.21                      0.82   \n",
       "5     media        nan          nan         nan                       nan   \n",
       "\n",
       "  source_social_medias_std total_sources_mean total_sources_std  \\\n",
       "0                     0.42               0.37              0.18   \n",
       "1                     0.45               0.30              0.14   \n",
       "2                     0.31               0.33              0.13   \n",
       "3                     0.42               0.32              0.17   \n",
       "4                     0.38               0.37              0.16   \n",
       "5                      nan                nan               nan   \n",
       "\n",
       "  influencer_public_mean influencer_public_std influencer_corpo_mean  \\\n",
       "0                   0.38                  0.45                  0.09   \n",
       "1                   0.26                  0.42                  0.57   \n",
       "2                   0.29                  0.45                  0.45   \n",
       "3                   0.27                  0.43                  0.12   \n",
       "4                   0.29                  0.44                  0.15   \n",
       "5                    nan                   nan                   nan   \n",
       "\n",
       "  influencer_corpo_std influencer_startup_mean influencer_startup_std  \\\n",
       "0                 0.29                    0.15                   0.36   \n",
       "1                 0.49                    0.15                   0.36   \n",
       "2                 0.45                    0.54                   0.50   \n",
       "3                 0.33                    0.07                   0.26   \n",
       "4                 0.36                    0.10                   0.30   \n",
       "5                  nan                     nan                    nan   \n",
       "\n",
       "  influencer_academic_mean influencer_academic_std influencer_civil_mean  \\\n",
       "0                     0.03                    0.17                  0.15   \n",
       "1                     0.01                    0.13                  0.10   \n",
       "2                     0.07                    0.25                  0.17   \n",
       "3                     0.32                    0.47                  0.17   \n",
       "4                     0.05                    0.22                  0.26   \n",
       "5                      nan                     nan                   nan   \n",
       "\n",
       "  influencer_civil_std meet_online_mean meet_online_std opinion_share_mean  \\\n",
       "0                 0.30             0.68            0.12               0.35   \n",
       "1                 0.23             0.59            0.12               0.32   \n",
       "2                 0.37             0.56            0.11               0.32   \n",
       "3                 0.36             0.55            0.11               0.28   \n",
       "4                 0.41             0.70            0.13               0.31   \n",
       "5                  nan              nan             nan                nan   \n",
       "\n",
       "  wish_mean wish_std total_engagement_mean total_engagement_std  \\\n",
       "0      0.81     0.39                  0.23                 0.11   \n",
       "1      0.64     0.48                  0.23                 0.11   \n",
       "2      0.80     0.40                  0.24                 0.13   \n",
       "3      0.77     0.42                  0.21                 0.13   \n",
       "4      0.79     0.40                  0.24                 0.12   \n",
       "5       nan      nan                   nan                  nan   \n",
       "\n",
       "  engagement_score_mean engagement_score_std pos_relationship_public  \\\n",
       "0                  0.66                 0.23                    0.64   \n",
       "1                  0.60                 0.17                    0.49   \n",
       "2                  0.59                 0.17                    0.49   \n",
       "3                  0.57                 0.21                    0.45   \n",
       "4                  0.66                 0.19                    0.45   \n",
       "5                   nan                  nan                     nan   \n",
       "\n",
       "  pos_relationship_private pos_relationship_academic pos_relationship_civil  \\\n",
       "0                     0.71                      0.84                   0.71   \n",
       "1                     0.75                      0.66                   0.58   \n",
       "2                     0.73                      0.63                   0.66   \n",
       "3                     0.62                      0.60                   0.70   \n",
       "4                     0.60                      0.62                   0.66   \n",
       "5                      nan                       nan                    nan   \n",
       "\n",
       "   num_tweets  max_likes  polarity_mean  polarity_std  subjectivity_mean  \\\n",
       "0       10701       3872           0.57          0.13                NaN   \n",
       "1       10211        157           0.59          0.15                NaN   \n",
       "2        9480       1100           0.60          0.13                NaN   \n",
       "3        4906        160           0.59          0.14                NaN   \n",
       "4        3002         36           0.53          0.09                NaN   \n",
       "5        9330         83           0.51          0.08                NaN   \n",
       "\n",
       "   subjectivity_std  negative_mean  negative_std  neutral_mean  neutral_std  \\\n",
       "0               NaN            NaN           NaN           NaN          NaN   \n",
       "1               NaN            NaN           NaN           NaN          NaN   \n",
       "2               NaN            NaN           NaN           NaN          NaN   \n",
       "3               NaN            NaN           NaN           NaN          NaN   \n",
       "4               NaN            NaN           NaN           NaN          NaN   \n",
       "5               NaN            NaN           NaN           NaN          NaN   \n",
       "\n",
       "   positive_mean  positive_std  tweet_len_mean  tweet_len_std  num_words_mean  \\\n",
       "0            NaN           NaN             NaN            NaN             NaN   \n",
       "1            NaN           NaN             NaN            NaN             NaN   \n",
       "2            NaN           NaN             NaN            NaN             NaN   \n",
       "3            NaN           NaN             NaN            NaN             NaN   \n",
       "4            NaN           NaN             NaN            NaN             NaN   \n",
       "5            NaN           NaN             NaN            NaN             NaN   \n",
       "\n",
       "   num_words_std  num_sentences_mean  num_sentences_std  avg_word_len_mean  \\\n",
       "0            NaN                 NaN                NaN                NaN   \n",
       "1            NaN                 NaN                NaN                NaN   \n",
       "2            NaN                 NaN                NaN                NaN   \n",
       "3            NaN                 NaN                NaN                NaN   \n",
       "4            NaN                 NaN                NaN                NaN   \n",
       "5            NaN                 NaN                NaN                NaN   \n",
       "\n",
       "   avg_word_len_std  num_token_mean  num_token_std  total_tokens  \\\n",
       "0               NaN             NaN            NaN           NaN   \n",
       "1               NaN             NaN            NaN           NaN   \n",
       "2               NaN             NaN            NaN           NaN   \n",
       "3               NaN             NaN            NaN           NaN   \n",
       "4               NaN             NaN            NaN           NaN   \n",
       "5               NaN             NaN            NaN           NaN   \n",
       "\n",
       "   stopwords_mean  stopwords_std  punctuation_mean  punctuation_std  \\\n",
       "0            4.68           4.68             11.22             4.68   \n",
       "1            6.87           4.73             10.13             5.14   \n",
       "2            5.17           4.41             11.05             4.97   \n",
       "3            4.66           3.97             10.30             4.83   \n",
       "4            2.72           4.25             10.39             5.12   \n",
       "5            2.21           2.78             10.70             6.55   \n",
       "\n",
       "   hashtags_mean  hashtags_std  numerics_mean  numerics_std  upper_mean  \\\n",
       "0           1.68          1.76           0.17          0.49        0.64   \n",
       "1           1.68          1.78           0.24          0.56        0.96   \n",
       "2           1.18          1.67           0.11          0.36        0.46   \n",
       "3           0.90          1.35           0.14          0.43        0.82   \n",
       "4           0.53          1.07           0.16          0.48        0.42   \n",
       "5           0.51          1.21           0.11          0.36        0.35   \n",
       "\n",
       "   upper_std  sma_bow  civ_bow  inf_bow  sus_bow  gov_bow  ent_bow  \n",
       "0       0.89     6399     6432     2390     1297     4884     5120  \n",
       "1       1.26     6805     6071     4220      886     6060     3648  \n",
       "2       0.74     5121     4225     6863     3012     2923     2764  \n",
       "3       1.02     2167     1972      610      732     2217     1164  \n",
       "4       0.71     1055     1100      355      636      443      917  \n",
       "5       0.67     1036     1477      779      292     2825      780  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stakeholders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. collect data from topological analysis of graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "networks = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/SNA/network_measures.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>network</th>\n",
       "      <th>num_tweets</th>\n",
       "      <th>num_followers</th>\n",
       "      <th>max_likes</th>\n",
       "      <th>num_nodes</th>\n",
       "      <th>degree_mean</th>\n",
       "      <th>degree_std</th>\n",
       "      <th>degree_min</th>\n",
       "      <th>degree_max</th>\n",
       "      <th>deg_centrality_mean</th>\n",
       "      <th>deg_centrality_std</th>\n",
       "      <th>deg_centrality_min</th>\n",
       "      <th>deg_centrality_max</th>\n",
       "      <th>closeness_mean</th>\n",
       "      <th>closeness_std</th>\n",
       "      <th>closeness_max</th>\n",
       "      <th>closeness_min</th>\n",
       "      <th>eigenvector_mean</th>\n",
       "      <th>eigenvector_std</th>\n",
       "      <th>eigenvector_min</th>\n",
       "      <th>eigenvector_max</th>\n",
       "      <th>betweenness_mean</th>\n",
       "      <th>betweenness_std</th>\n",
       "      <th>betweenness_min</th>\n",
       "      <th>betweenness_max</th>\n",
       "      <th>load_mean</th>\n",
       "      <th>load_std</th>\n",
       "      <th>load_min</th>\n",
       "      <th>load_max</th>\n",
       "      <th>num_edges</th>\n",
       "      <th>num_nonedges</th>\n",
       "      <th>size</th>\n",
       "      <th>density</th>\n",
       "      <th>transitivity</th>\n",
       "      <th>edge_connectivity</th>\n",
       "      <th>is_connected?</th>\n",
       "      <th>num_connected_components</th>\n",
       "      <th>lstsub_nodes</th>\n",
       "      <th>lstsub_edges</th>\n",
       "      <th>lstsub_avg_clustering</th>\n",
       "      <th>lstsub_transitivity</th>\n",
       "      <th>lstsub_diameter</th>\n",
       "      <th>lstsub_avg_distance</th>\n",
       "      <th>node_max_deg_centrality</th>\n",
       "      <th>max_deg_centrality</th>\n",
       "      <th>node_max_closeness</th>\n",
       "      <th>max_closeness</th>\n",
       "      <th>node_max_betweenness</th>\n",
       "      <th>max_betweenness</th>\n",
       "      <th>greedy_communities</th>\n",
       "      <th>louvain_communities</th>\n",
       "      <th>louvain_modularity</th>\n",
       "      <th>cliques_communities</th>\n",
       "      <th>max_cliques</th>\n",
       "      <th>lpa_communities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>public</td>\n",
       "      <td>10701</td>\n",
       "      <td>307537</td>\n",
       "      <td>406</td>\n",
       "      <td>2431</td>\n",
       "      <td>2.012341</td>\n",
       "      <td>36.892936</td>\n",
       "      <td>1</td>\n",
       "      <td>1748</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>0.015182</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.719342</td>\n",
       "      <td>0.317292</td>\n",
       "      <td>0.103807</td>\n",
       "      <td>0.642636</td>\n",
       "      <td>0.054836</td>\n",
       "      <td>0.012410</td>\n",
       "      <td>0.016046</td>\n",
       "      <td>-6.938894e-18</td>\n",
       "      <td>0.711278</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.016457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.760753</td>\n",
       "      <td>0.000532</td>\n",
       "      <td>0.016457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.760753</td>\n",
       "      <td>2446</td>\n",
       "      <td>2951222</td>\n",
       "      <td>2446</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2164</td>\n",
       "      <td>2179</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>2.615358</td>\n",
       "      <td>startupestonia</td>\n",
       "      <td>0.808137</td>\n",
       "      <td>startupestonia</td>\n",
       "      <td>0.721963</td>\n",
       "      <td>startupestonia</td>\n",
       "      <td>0.960207</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.442173</td>\n",
       "      <td>2443</td>\n",
       "      <td>2443</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corpo</td>\n",
       "      <td>10211</td>\n",
       "      <td>903200</td>\n",
       "      <td>568</td>\n",
       "      <td>7927</td>\n",
       "      <td>2.016652</td>\n",
       "      <td>54.149930</td>\n",
       "      <td>1</td>\n",
       "      <td>3712</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>0.006832</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.468332</td>\n",
       "      <td>0.308119</td>\n",
       "      <td>0.020524</td>\n",
       "      <td>0.500063</td>\n",
       "      <td>0.246141</td>\n",
       "      <td>0.005620</td>\n",
       "      <td>0.009725</td>\n",
       "      <td>1.944144e-04</td>\n",
       "      <td>0.709748</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.010938</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.712492</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.010937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.712153</td>\n",
       "      <td>7993</td>\n",
       "      <td>31406711</td>\n",
       "      <td>7993</td>\n",
       "      <td>0.000254</td>\n",
       "      <td>5.166839e-07</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>7927</td>\n",
       "      <td>7993</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>5.166839e-07</td>\n",
       "      <td>5</td>\n",
       "      <td>3.259507</td>\n",
       "      <td>CheckPointSW</td>\n",
       "      <td>0.468332</td>\n",
       "      <td>ReutersTech, ForbesTech, BBCTech, thenextweb</td>\n",
       "      <td>0.500063</td>\n",
       "      <td>CheckPointSW</td>\n",
       "      <td>0.712492</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.628311</td>\n",
       "      <td>7987</td>\n",
       "      <td>7987</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>startup</td>\n",
       "      <td>9480</td>\n",
       "      <td>20473</td>\n",
       "      <td>457</td>\n",
       "      <td>8087</td>\n",
       "      <td>2.026215</td>\n",
       "      <td>66.467154</td>\n",
       "      <td>1</td>\n",
       "      <td>5566</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.008220</td>\n",
       "      <td>0.000124</td>\n",
       "      <td>0.688350</td>\n",
       "      <td>0.351296</td>\n",
       "      <td>0.047911</td>\n",
       "      <td>0.615935</td>\n",
       "      <td>0.258067</td>\n",
       "      <td>0.006645</td>\n",
       "      <td>0.008917</td>\n",
       "      <td>4.749810e-05</td>\n",
       "      <td>0.709295</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.011207</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.897104</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.011206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.897022</td>\n",
       "      <td>8193</td>\n",
       "      <td>32687551</td>\n",
       "      <td>8193</td>\n",
       "      <td>0.000251</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>8087</td>\n",
       "      <td>8193</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>2.906858</td>\n",
       "      <td>moovit</td>\n",
       "      <td>0.688350</td>\n",
       "      <td>moovit</td>\n",
       "      <td>0.615935</td>\n",
       "      <td>moovit</td>\n",
       "      <td>0.897104</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.457451</td>\n",
       "      <td>8190</td>\n",
       "      <td>8190</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>academic</td>\n",
       "      <td>4906</td>\n",
       "      <td>12457</td>\n",
       "      <td>303</td>\n",
       "      <td>4248</td>\n",
       "      <td>2.018362</td>\n",
       "      <td>56.147764</td>\n",
       "      <td>1</td>\n",
       "      <td>3631</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.013221</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.854956</td>\n",
       "      <td>0.411331</td>\n",
       "      <td>0.062585</td>\n",
       "      <td>0.774859</td>\n",
       "      <td>0.259517</td>\n",
       "      <td>0.010165</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>6.692745e-05</td>\n",
       "      <td>0.710011</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.015313</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.977466</td>\n",
       "      <td>0.000357</td>\n",
       "      <td>0.015309</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.977174</td>\n",
       "      <td>4287</td>\n",
       "      <td>9016344</td>\n",
       "      <td>4287</td>\n",
       "      <td>0.000475</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>4248</td>\n",
       "      <td>4287</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>2.514393</td>\n",
       "      <td>TelAvivUni</td>\n",
       "      <td>0.854956</td>\n",
       "      <td>TelAvivUni</td>\n",
       "      <td>0.977466</td>\n",
       "      <td>TelAvivUni</td>\n",
       "      <td>0.977466</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.268946</td>\n",
       "      <td>4284</td>\n",
       "      <td>4284</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>civil</td>\n",
       "      <td>3001</td>\n",
       "      <td>12069</td>\n",
       "      <td>399</td>\n",
       "      <td>2480</td>\n",
       "      <td>2.007258</td>\n",
       "      <td>38.102206</td>\n",
       "      <td>1</td>\n",
       "      <td>1806</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.015370</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>0.728520</td>\n",
       "      <td>0.362339</td>\n",
       "      <td>0.051542</td>\n",
       "      <td>0.647766</td>\n",
       "      <td>0.255593</td>\n",
       "      <td>0.012336</td>\n",
       "      <td>0.015848</td>\n",
       "      <td>2.697901e-05</td>\n",
       "      <td>0.711251</td>\n",
       "      <td>0.000737</td>\n",
       "      <td>0.020920</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.924982</td>\n",
       "      <td>0.000737</td>\n",
       "      <td>0.020919</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.924876</td>\n",
       "      <td>2489</td>\n",
       "      <td>3071474</td>\n",
       "      <td>2489</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>2480</td>\n",
       "      <td>2489</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>2.826796</td>\n",
       "      <td>hasadna</td>\n",
       "      <td>0.728520</td>\n",
       "      <td>hasadna</td>\n",
       "      <td>0.647766</td>\n",
       "      <td>hasadna</td>\n",
       "      <td>0.924982</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.415913</td>\n",
       "      <td>2486</td>\n",
       "      <td>2486</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>media</td>\n",
       "      <td>9330</td>\n",
       "      <td>92266</td>\n",
       "      <td>0</td>\n",
       "      <td>2604</td>\n",
       "      <td>2.012289</td>\n",
       "      <td>38.486331</td>\n",
       "      <td>1</td>\n",
       "      <td>1784</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.014785</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.685363</td>\n",
       "      <td>0.352244</td>\n",
       "      <td>0.048025</td>\n",
       "      <td>0.612821</td>\n",
       "      <td>0.002597</td>\n",
       "      <td>0.011763</td>\n",
       "      <td>0.015676</td>\n",
       "      <td>3.469447e-18</td>\n",
       "      <td>0.711170</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.020278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.889164</td>\n",
       "      <td>0.000703</td>\n",
       "      <td>0.020278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.889164</td>\n",
       "      <td>2620</td>\n",
       "      <td>3386488</td>\n",
       "      <td>2620</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>2590</td>\n",
       "      <td>2607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4</td>\n",
       "      <td>2.848599</td>\n",
       "      <td>TaiwanNews886</td>\n",
       "      <td>0.689069</td>\n",
       "      <td>TaiwanNews886</td>\n",
       "      <td>0.616135</td>\n",
       "      <td>TaiwanNews886</td>\n",
       "      <td>0.898809</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.432975</td>\n",
       "      <td>2618</td>\n",
       "      <td>2618</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    network  num_tweets  num_followers  max_likes  num_nodes  degree_mean  \\\n",
       "3    public       10701         307537        406       2431     2.012341   \n",
       "4     corpo       10211         903200        568       7927     2.016652   \n",
       "5   startup        9480          20473        457       8087     2.026215   \n",
       "6  academic        4906          12457        303       4248     2.018362   \n",
       "7     civil        3001          12069        399       2480     2.007258   \n",
       "8     media        9330          92266          0       2604     2.012289   \n",
       "\n",
       "   degree_std  degree_min  degree_max  deg_centrality_mean  \\\n",
       "3   36.892936           1        1748             0.000828   \n",
       "4   54.149930           1        3712             0.000254   \n",
       "5   66.467154           1        5566             0.000251   \n",
       "6   56.147764           1        3631             0.000475   \n",
       "7   38.102206           1        1806             0.000810   \n",
       "8   38.486331           1        1784             0.000773   \n",
       "\n",
       "   deg_centrality_std  deg_centrality_min  deg_centrality_max  closeness_mean  \\\n",
       "3            0.015182            0.000412            0.719342        0.317292   \n",
       "4            0.006832            0.000126            0.468332        0.308119   \n",
       "5            0.008220            0.000124            0.688350        0.351296   \n",
       "6            0.013221            0.000235            0.854956        0.411331   \n",
       "7            0.015370            0.000403            0.728520        0.362339   \n",
       "8            0.014785            0.000384            0.685363        0.352244   \n",
       "\n",
       "   closeness_std  closeness_max  closeness_min  eigenvector_mean  \\\n",
       "3       0.103807       0.642636       0.054836          0.012410   \n",
       "4       0.020524       0.500063       0.246141          0.005620   \n",
       "5       0.047911       0.615935       0.258067          0.006645   \n",
       "6       0.062585       0.774859       0.259517          0.010165   \n",
       "7       0.051542       0.647766       0.255593          0.012336   \n",
       "8       0.048025       0.612821       0.002597          0.011763   \n",
       "\n",
       "   eigenvector_std  eigenvector_min  eigenvector_max  betweenness_mean  \\\n",
       "3         0.016046    -6.938894e-18         0.711278          0.000532   \n",
       "4         0.009725     1.944144e-04         0.709748          0.000285   \n",
       "5         0.008917     4.749810e-05         0.709295          0.000236   \n",
       "6         0.011494     6.692745e-05         0.710011          0.000357   \n",
       "7         0.015848     2.697901e-05         0.711251          0.000737   \n",
       "8         0.015676     3.469447e-18         0.711170          0.000703   \n",
       "\n",
       "   betweenness_std  betweenness_min  betweenness_max  load_mean  load_std  \\\n",
       "3         0.016457              0.0         0.760753   0.000532  0.016457   \n",
       "4         0.010938              0.0         0.712492   0.000285  0.010937   \n",
       "5         0.011207              0.0         0.897104   0.000236  0.011206   \n",
       "6         0.015313              0.0         0.977466   0.000357  0.015309   \n",
       "7         0.020920              0.0         0.924982   0.000737  0.020919   \n",
       "8         0.020278              0.0         0.889164   0.000703  0.020278   \n",
       "\n",
       "   load_min  load_max  num_edges  num_nonedges  size   density  transitivity  \\\n",
       "3       0.0  0.760753       2446       2951222  2446  0.000828  0.000000e+00   \n",
       "4       0.0  0.712153       7993      31406711  7993  0.000254  5.166839e-07   \n",
       "5       0.0  0.897022       8193      32687551  8193  0.000251  0.000000e+00   \n",
       "6       0.0  0.977174       4287       9016344  4287  0.000475  0.000000e+00   \n",
       "7       0.0  0.924876       2489       3071474  2489  0.000810  0.000000e+00   \n",
       "8       0.0  0.889164       2620       3386488  2620  0.000773  0.000000e+00   \n",
       "\n",
       "   edge_connectivity  is_connected?  num_connected_components  lstsub_nodes  \\\n",
       "3                  0          False                         2          2164   \n",
       "4                  1           True                         1          7927   \n",
       "5                  1           True                         1          8087   \n",
       "6                  1           True                         1          4248   \n",
       "7                  1           True                         1          2480   \n",
       "8                  0          False                         2          2590   \n",
       "\n",
       "   lstsub_edges  lstsub_avg_clustering  lstsub_transitivity  lstsub_diameter  \\\n",
       "3          2179               0.000000         0.000000e+00                4   \n",
       "4          7993               0.000294         5.166839e-07                5   \n",
       "5          8193               0.000000         0.000000e+00                4   \n",
       "6          4287               0.000000         0.000000e+00                4   \n",
       "7          2489               0.000000         0.000000e+00                4   \n",
       "8          2607               0.000000         0.000000e+00                4   \n",
       "\n",
       "   lstsub_avg_distance node_max_deg_centrality  max_deg_centrality  \\\n",
       "3             2.615358          startupestonia            0.808137   \n",
       "4             3.259507            CheckPointSW            0.468332   \n",
       "5             2.906858                  moovit            0.688350   \n",
       "6             2.514393              TelAvivUni            0.854956   \n",
       "7             2.826796                 hasadna            0.728520   \n",
       "8             2.848599           TaiwanNews886            0.689069   \n",
       "\n",
       "                             node_max_closeness  max_closeness  \\\n",
       "3                                startupestonia       0.721963   \n",
       "4  ReutersTech, ForbesTech, BBCTech, thenextweb       0.500063   \n",
       "5                                        moovit       0.615935   \n",
       "6                                    TelAvivUni       0.977466   \n",
       "7                                       hasadna       0.647766   \n",
       "8                                 TaiwanNews886       0.616135   \n",
       "\n",
       "  node_max_betweenness  max_betweenness  greedy_communities  \\\n",
       "3       startupestonia         0.960207                   3   \n",
       "4         CheckPointSW         0.712492                   3   \n",
       "5               moovit         0.897104                   3   \n",
       "6           TelAvivUni         0.977466                   3   \n",
       "7              hasadna         0.924982                   3   \n",
       "8        TaiwanNews886         0.898809                   3   \n",
       "\n",
       "   louvain_communities  louvain_modularity  cliques_communities  max_cliques  \\\n",
       "3                    3            0.442173                 2443         2443   \n",
       "4                    3            0.628311                 7987         7987   \n",
       "5                    3            0.457451                 8190         8190   \n",
       "6                    3            0.268946                 4284         4284   \n",
       "7                    3            0.415913                 2486         2486   \n",
       "8                    3            0.432975                 2618         2618   \n",
       "\n",
       "   lpa_communities  \n",
       "3                3  \n",
       "4                3  \n",
       "5                3  \n",
       "6                3  \n",
       "7                3  \n",
       "8                3  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networks.drop([0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_followers'] = networks['num_followers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_nodes'] = networks['num_nodes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['deg_centrality_mean'] = networks['deg_centrality_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['deg_centrality_std'] = networks['deg_centrality_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['closeness_mean'] = networks['closeness_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['closeness_std'] = networks['closeness_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['eigenvector_mean'] = networks['eigenvector_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['eigenvector_std'] = networks['eigenvector_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['betweenness_mean'] = networks['betweenness_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['betweenness_std'] = networks['betweenness_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['load_mean'] = networks['load_mean']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['load_std'] = networks['load_std']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_edges'] = networks['num_edges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['num_nonedges'] = networks['num_nonedges']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['density'] = networks['density']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['transitivity'] = networks['transitivity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['diameter'] = networks['lstsub_diameter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['avg_shortest_path_length'] = networks['lstsub_avg_distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['louvain_modularity'] = networks['louvain_modularity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['max_cliques'] = networks['max_cliques']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# edge files\n",
    "public_edge = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/SNA/link prediction files/public_edge.csv')\n",
    "corpo_edge = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/SNA/link prediction files/corpo_edge.csv')\n",
    "startup_edge = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/SNA/link prediction files/startup_edge.csv')\n",
    "academic_edge = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/SNA/link prediction files/academic_edge.csv')\n",
    "civil_edge = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/SNA/link prediction files/civil_edge.csv')\n",
    "media_edge = pd.read_csv('/Users/juliencarbonnell/Desktop/Thèse/DONNÉES/1.Twitter/SNA/link prediction files/media_edge.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>common_neigh</th>\n",
       "      <th>jaccard_coef</th>\n",
       "      <th>ressource_alloc</th>\n",
       "      <th>pref_attachment</th>\n",
       "      <th>isedge?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FilippovEU</td>\n",
       "      <td>Nutiteq</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FilippovEU</td>\n",
       "      <td>ahtih</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FilippovEU</td>\n",
       "      <td>SATUlaboratory</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FilippovEU</td>\n",
       "      <td>TimHarcourt</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FilippovEU</td>\n",
       "      <td>CREAcademy</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0               1  common_neigh  jaccard_coef  ressource_alloc  \\\n",
       "0  FilippovEU         Nutiteq             1           1.0         0.000572   \n",
       "1  FilippovEU           ahtih             1           1.0         0.000572   \n",
       "2  FilippovEU  SATUlaboratory             1           1.0         0.000572   \n",
       "3  FilippovEU     TimHarcourt             0           0.0         0.000000   \n",
       "4  FilippovEU      CREAcademy             1           1.0         0.000572   \n",
       "\n",
       "   pref_attachment  isedge?  \n",
       "0                1        0  \n",
       "1                1        0  \n",
       "2                1        0  \n",
       "3                1        0  \n",
       "4                1        0  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_edge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.691161\n",
       "1    0.999082\n",
       "Name: common_neigh, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_edge.groupby(['isedge?'])['common_neigh'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.369746\n",
       "1    1.000000\n",
       "Name: common_neigh, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpo_edge.groupby(['isedge?'])['common_neigh'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.546187\n",
       "1    0.999634\n",
       "Name: common_neigh, dtype: float64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup_edge.groupby(['isedge?'])['common_neigh'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.742017\n",
       "1    0.999300\n",
       "Name: common_neigh, dtype: float64"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "academic_edge.groupby(['isedge?'])['common_neigh'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.585065\n",
       "1    0.998795\n",
       "Name: common_neigh, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "civil_edge.groupby(['isedge?'])['common_neigh'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.574643\n",
       "1    0.999233\n",
       "Name: common_neigh, dtype: float64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_edge.groupby(['isedge?'])['common_neigh'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['nonedge_comm_neighbors_mean'] = [0.69, 0.36, 0.54, 0.74, 0.58, 0.57]\n",
    "stakeholders['edge_comm_neighbors_mean'] = [0.99, 1, 0.99, 0.99, 0.99, 0.99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.684223\n",
       "1    0.000917\n",
       "Name: jaccard_coef, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_edge.groupby(['isedge?'])['jaccard_coef'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.363914\n",
       "1    0.000425\n",
       "Name: jaccard_coef, dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpo_edge.groupby(['isedge?'])['jaccard_coef'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.535566\n",
       "1    0.000366\n",
       "Name: jaccard_coef, dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup_edge.groupby(['isedge?'])['jaccard_coef'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.733696\n",
       "1    0.000698\n",
       "Name: jaccard_coef, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "academic_edge.groupby(['isedge?'])['jaccard_coef'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.582320\n",
       "1    0.001201\n",
       "Name: jaccard_coef, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "civil_edge.groupby(['isedge?'])['jaccard_coef'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.568074\n",
       "1    0.000766\n",
       "Name: jaccard_coef, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_edge.groupby(['isedge?'])['jaccard_coef'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['nonedge_jaccard_mean'] = [0.68, 0.36, 0.53, 0.73, 0.58, 0.56]\n",
    "stakeholders['edge_jaccard_mean'] = [0, 0, 0, 0, 0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.000467\n",
       "1    0.992198\n",
       "Name: ressource_alloc, dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_edge.groupby(['isedge?'])['ressource_alloc'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.000128\n",
       "1    0.991211\n",
       "Name: ressource_alloc, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpo_edge.groupby(['isedge?'])['ressource_alloc'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.000127\n",
       "1    0.986696\n",
       "Name: ressource_alloc, dtype: float64"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup_edge.groupby(['isedge?'])['ressource_alloc'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.000239\n",
       "1    0.990203\n",
       "Name: ressource_alloc, dtype: float64"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "academic_edge.groupby(['isedge?'])['ressource_alloc'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.000405\n",
       "1    0.995179\n",
       "Name: ressource_alloc, dtype: float64"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "civil_edge.groupby(['isedge?'])['ressource_alloc'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0    0.000390\n",
       "1    0.992712\n",
       "Name: ressource_alloc, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_edge.groupby(['isedge?'])['ressource_alloc'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['nonedge_ressource_mean'] = [0, 0, 0, 0, 0, 0]\n",
    "stakeholders['edge_ressource_mean'] = [0.99, 0.99, 0.98, 0.99, 0.99, 0.99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0       1.968248\n",
       "1    2989.600275\n",
       "Name: pref_attachment, dtype: float64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "public_edge.groupby(['isedge?'])['pref_attachment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0       2.945063\n",
       "1    5868.846366\n",
       "Name: pref_attachment, dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpo_edge.groupby(['isedge?'])['pref_attachment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0       2.443048\n",
       "1    8820.081045\n",
       "Name: pref_attachment, dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "startup_edge.groupby(['isedge?'])['pref_attachment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0       1.831020\n",
       "1    6285.609051\n",
       "Name: pref_attachment, dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "academic_edge.groupby(['isedge?'])['pref_attachment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0       2.268410\n",
       "1    2902.711933\n",
       "Name: pref_attachment, dtype: float64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "civil_edge.groupby(['isedge?'])['pref_attachment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isedge?\n",
       "0       2.315575\n",
       "1    2978.786728\n",
       "Name: pref_attachment, dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_edge.groupby(['isedge?'])['pref_attachment'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "stakeholders['nonedge_pref_attachment_mean'] = [1.96, 2.94, 2.44, 1.83, 2.26, 2.31]\n",
    "stakeholders['edge_pref_attachment_mean'] = [2989, 5868, 8820, 6285, 2902, 2978]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# save file\n",
    "stakeholders.to_csv('stakeholders.csv')"
   ]
  }
 ],
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