bike_sharing_analysis_part_3_data_wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20aadaee-3c47-45e6-8bfd-4d4ffecba1fd",
   "metadata": {},
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80452283-627a-44f2-b86b-bd477a3eb2ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "03fe8a4e-b4bf-40fd-8622-5f6a57499169",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dt1 = pd.read_csv('data_sets/dc_bikes_2.csv', index_col=0)\n",
    "df_dt1.index = pd.to_datetime(df_dt1.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "764828e6-b776-40aa-bccf-e49369f7b6c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
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       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
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       "      <th>2011-01-01 00:00:00</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "                     season  holiday  workingday  weather  temp   atemp  \\\n",
       "datetime                                                                  \n",
       "2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1  9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1  9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  count  year  \\\n",
       "datetime                                                                    \n",
       "2011-01-01 00:00:00        81        0.0     3.0        13.0     16  2011   \n",
       "2011-01-01 01:00:00        80        0.0     8.0        32.0     40  2011   \n",
       "2011-01-01 02:00:00        80        0.0     5.0        27.0     32  2011   \n",
       "2011-01-01 03:00:00        75        0.0     3.0        10.0     13  2011   \n",
       "2011-01-01 04:00:00        75        0.0     0.0         1.0      1  2011   \n",
       "\n",
       "                     month_number    month  day_of_month  week_day  hour_day  \\\n",
       "datetime                                                                       \n",
       "2011-01-01 00:00:00             1  January             1  Saturday         0   \n",
       "2011-01-01 01:00:00             1  January             1  Saturday         1   \n",
       "2011-01-01 02:00:00             1  January             1  Saturday         2   \n",
       "2011-01-01 03:00:00             1  January             1  Saturday         3   \n",
       "2011-01-01 04:00:00             1  January             1  Saturday         4   \n",
       "\n",
       "                    season_name  hour_of_day part_of_day  \n",
       "datetime                                                  \n",
       "2011-01-01 00:00:00      winter            0       night  \n",
       "2011-01-01 01:00:00      winter            1       night  \n",
       "2011-01-01 02:00:00      winter            2       night  \n",
       "2011-01-01 03:00:00      winter            3       night  \n",
       "2011-01-01 04:00:00      winter            4       night  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dt1.head()"
   ]
  },
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   "id": "91d41dc1-4a1f-49d2-81c2-e7c981050512",
   "metadata": {
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   "outputs": [
    {
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       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>156</td>\n",
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       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>winter</td>\n",
       "      <td>19</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>104</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
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       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>winter</td>\n",
       "      <td>21</td>\n",
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       "      <th>2012-12-31 22:00:00</th>\n",
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       "      <td>2012</td>\n",
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       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>winter</td>\n",
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       "      <td>28</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>winter</td>\n",
       "      <td>23</td>\n",
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       "                     count  year  month_number     month  day_of_month  \\\n",
       "datetime                                                                 \n",
       "2011-01-01 00:00:00     16  2011             1   January             1   \n",
       "2011-01-01 01:00:00     40  2011             1   January             1   \n",
       "2011-01-01 02:00:00     32  2011             1   January             1   \n",
       "2011-01-01 03:00:00     13  2011             1   January             1   \n",
       "2011-01-01 04:00:00      1  2011             1   January             1   \n",
       "...                    ...   ...           ...       ...           ...   \n",
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       "2012-12-31 21:00:00     67  2012            12  December            31   \n",
       "2012-12-31 22:00:00     43  2012            12  December            31   \n",
       "2012-12-31 23:00:00     28  2012            12  December            31   \n",
       "\n",
       "                     week_day season_name  hour_of_day  \n",
       "datetime                                                \n",
       "2011-01-01 00:00:00  Saturday      winter            0  \n",
       "2011-01-01 01:00:00  Saturday      winter            1  \n",
       "2011-01-01 02:00:00  Saturday      winter            2  \n",
       "2011-01-01 03:00:00  Saturday      winter            3  \n",
       "2011-01-01 04:00:00  Saturday      winter            4  \n",
       "...                       ...         ...          ...  \n",
       "2012-12-31 19:00:00    Monday      winter           19  \n",
       "2012-12-31 20:00:00    Monday      winter           20  \n",
       "2012-12-31 21:00:00    Monday      winter           21  \n",
       "2012-12-31 22:00:00    Monday      winter           22  \n",
       "2012-12-31 23:00:00    Monday      winter           23  \n",
       "\n",
       "[17379 rows x 8 columns]"
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     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "# Drop option to reduce the table\n",
    "\n",
    "df_dt2 = df_dt1.drop(['season', 'holiday','workingday','weather','temp','atemp','humidity','windspeed','casual','registered','hour_day','part_of_day'], axis=1)\n",
    "df_dt2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d7ee1833-f6c7-4810-b35d-5cdacafa3298",
   "metadata": {
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   },
   "outputs": [
    {
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       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>156</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>104</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>67</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>43</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>28</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  year     month  day_of_month  week_day  \\\n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00     16  2011   January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011   January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011   January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011   January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011   January             1  Saturday   \n",
       "...                    ...   ...       ...           ...       ...   \n",
       "2012-12-31 19:00:00    156  2012  December            31    Monday   \n",
       "2012-12-31 20:00:00    104  2012  December            31    Monday   \n",
       "2012-12-31 21:00:00     67  2012  December            31    Monday   \n",
       "2012-12-31 22:00:00     43  2012  December            31    Monday   \n",
       "2012-12-31 23:00:00     28  2012  December            31    Monday   \n",
       "\n",
       "                     month_number  \n",
       "datetime                           \n",
       "2011-01-01 00:00:00             1  \n",
       "2011-01-01 01:00:00             1  \n",
       "2011-01-01 02:00:00             1  \n",
       "2011-01-01 03:00:00             1  \n",
       "2011-01-01 04:00:00             1  \n",
       "...                           ...  \n",
       "2012-12-31 19:00:00            12  \n",
       "2012-12-31 20:00:00            12  \n",
       "2012-12-31 21:00:00            12  \n",
       "2012-12-31 22:00:00            12  \n",
       "2012-12-31 23:00:00            12  \n",
       "\n",
       "[17379 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting columns option to reduce the table\n",
    "\n",
    "df_dt2_2= df_dt1[['count','year','month','day_of_month','week_day','month_number']]\n",
    "df_dt2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fccabd8-001d-4257-b749-6ee9af51f9e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>week_day</th>\n",
       "      <th>month_number</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
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       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>40</td>\n",
       "      <td>2011</td>\n",
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       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  year    month  day_of_month  week_day  \\\n",
       "datetime                                                            \n",
       "2011-01-01 00:00:00     16  2011  January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011  January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011  January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011  January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011  January             1  Saturday   \n",
       "\n",
       "                     month_number  \n",
       "datetime                           \n",
       "2011-01-01 00:00:00             1  \n",
       "2011-01-01 01:00:00             1  \n",
       "2011-01-01 02:00:00             1  \n",
       "2011-01-01 03:00:00             1  \n",
       "2011-01-01 04:00:00             1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dt2_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3da226aa-a652-4c13-806a-5586f20a2484",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_10299/303696026.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['year']= df_dt2_2['year'].astype(str)\n"
     ]
    }
   ],
   "source": [
    "# Transform 'year' column type from 'int' to 'str' to later concatinate.\n",
    "\n",
    "df_dt2_2['year']= df_dt2_2['year'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4bde8134-ed93-431a-9428-baca5eae20fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 17379 entries, 2011-01-01 00:00:00 to 2012-12-31 23:00:00\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   count         17379 non-null  int64 \n",
      " 1   year          17379 non-null  object\n",
      " 2   month         17379 non-null  object\n",
      " 3   day_of_month  17379 non-null  int64 \n",
      " 4   week_day      17379 non-null  object\n",
      " 5   month_number  17379 non-null  int64 \n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 950.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_dt2_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36eb1069-e5fd-4254-ae85-76d08a2dea51",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_10299/566047724.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['month_number']= df_dt2_2['month_number'].astype(str)\n"
     ]
    }
   ],
   "source": [
    "# Transform 'month_number' column type from 'int' to 'str' to later concatinate.\n",
    "\n",
    "df_dt2_2['month_number']= df_dt2_2['month_number'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e29d0dc-f0c4-4e53-b207-b601313a2cab",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 17379 entries, 2011-01-01 00:00:00 to 2012-12-31 23:00:00\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   count         17379 non-null  int64 \n",
      " 1   year          17379 non-null  object\n",
      " 2   month         17379 non-null  object\n",
      " 3   day_of_month  17379 non-null  int64 \n",
      " 4   week_day      17379 non-null  object\n",
      " 5   month_number  17379 non-null  object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 950.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_dt2_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07dd1a9f-597d-4a20-9346-5aa4b0f14501",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_10299/823846508.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['date'] = pd.to_datetime(df_dt2_2['year'] + '/' + df_dt2_2['month_number'])\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>16</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
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       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
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       "      <td>13</td>\n",
       "      <td>2011</td>\n",
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       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>January</td>\n",
       "      <td>1</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>2012-12-31 19:00:00</th>\n",
       "      <td>156</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2012-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 20:00:00</th>\n",
       "      <td>104</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2012-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 21:00:00</th>\n",
       "      <td>67</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2012-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 22:00:00</th>\n",
       "      <td>43</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2012-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 23:00:00</th>\n",
       "      <td>28</td>\n",
       "      <td>2012</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>12</td>\n",
       "      <td>2012-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  year     month  day_of_month  week_day  \\\n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00     16  2011   January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011   January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011   January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011   January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011   January             1  Saturday   \n",
       "...                    ...   ...       ...           ...       ...   \n",
       "2012-12-31 19:00:00    156  2012  December            31    Monday   \n",
       "2012-12-31 20:00:00    104  2012  December            31    Monday   \n",
       "2012-12-31 21:00:00     67  2012  December            31    Monday   \n",
       "2012-12-31 22:00:00     43  2012  December            31    Monday   \n",
       "2012-12-31 23:00:00     28  2012  December            31    Monday   \n",
       "\n",
       "                    month_number       date  \n",
       "datetime                                     \n",
       "2011-01-01 00:00:00            1 2011-01-01  \n",
       "2011-01-01 01:00:00            1 2011-01-01  \n",
       "2011-01-01 02:00:00            1 2011-01-01  \n",
       "2011-01-01 03:00:00            1 2011-01-01  \n",
       "2011-01-01 04:00:00            1 2011-01-01  \n",
       "...                          ...        ...  \n",
       "2012-12-31 19:00:00           12 2012-12-01  \n",
       "2012-12-31 20:00:00           12 2012-12-01  \n",
       "2012-12-31 21:00:00           12 2012-12-01  \n",
       "2012-12-31 22:00:00           12 2012-12-01  \n",
       "2012-12-31 23:00:00           12 2012-12-01  \n",
       "\n",
       "[17379 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatinate 'year' and 'month_number' to create a new column called 'date' as \n",
    "# string type.\n",
    "\n",
    "df_dt2_2['date'] = pd.to_datetime(df_dt2_2['year'] + '/' + df_dt2_2['month_number'])\n",
    "df_dt2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47888fd3-8302-42b8-8117-e71f6ee3d2f3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/yq/6pm1lcds6ln5qlsjc5y8fly00000gn/T/ipykernel_10299/2812342474.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_dt2_2['date'] = pd.to_datetime(df_dt2_2['date'])\n"
     ]
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       "                     count  year     month  day_of_month  week_day  \\\n",
       "datetime                                                             \n",
       "2011-01-01 00:00:00     16  2011   January             1  Saturday   \n",
       "2011-01-01 01:00:00     40  2011   January             1  Saturday   \n",
       "2011-01-01 02:00:00     32  2011   January             1  Saturday   \n",
       "2011-01-01 03:00:00     13  2011   January             1  Saturday   \n",
       "2011-01-01 04:00:00      1  2011   January             1  Saturday   \n",
       "...                    ...   ...       ...           ...       ...   \n",
       "2012-12-31 19:00:00    156  2012  December            31    Monday   \n",
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       "2012-12-31 23:00:00     28  2012  December            31    Monday   \n",
       "\n",
       "                    month_number       date  \n",
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       "2011-01-01 00:00:00            1 2011-01-01  \n",
       "2011-01-01 01:00:00            1 2011-01-01  \n",
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       "2011-01-01 03:00:00            1 2011-01-01  \n",
       "2011-01-01 04:00:00            1 2011-01-01  \n",
       "...                          ...        ...  \n",
       "2012-12-31 19:00:00           12 2012-12-01  \n",
       "2012-12-31 20:00:00           12 2012-12-01  \n",
       "2012-12-31 21:00:00           12 2012-12-01  \n",
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       "2012-12-31 23:00:00           12 2012-12-01  \n",
       "\n",
       "[17379 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform 'date' column into a date.time type.\n",
    "\n",
    "df_dt2_2['date'] = pd.to_datetime(df_dt2_2['date'])\n",
    "df_dt2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a92d18db-dc78-4dcc-89c5-9d371cc0e62a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 17379 entries, 2011-01-01 00:00:00 to 2012-12-31 23:00:00\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   count         17379 non-null  int64         \n",
      " 1   year          17379 non-null  object        \n",
      " 2   month         17379 non-null  object        \n",
      " 3   day_of_month  17379 non-null  int64         \n",
      " 4   week_day      17379 non-null  object        \n",
      " 5   month_number  17379 non-null  object        \n",
      " 6   date          17379 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), int64(2), object(4)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_dt2_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d378640d-3828-445d-bfde-d7993ef45d1c",
   "metadata": {
    "tags": []
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       "      <td>...</td>\n",
       "      <td>2012</td>\n",
       "      <td>12</td>\n",
       "      <td>December</td>\n",
       "      <td>31</td>\n",
       "      <td>Monday</td>\n",
       "      <td>23</td>\n",
       "      <td>winter</td>\n",
       "      <td>23</td>\n",
       "      <td>evening</td>\n",
       "      <td>2012-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17379 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     season  holiday  workingday  weather   temp   atemp  \\\n",
       "datetime                                                                   \n",
       "2011-01-01 00:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 01:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 02:00:00       1        0           0        1   9.02  13.635   \n",
       "2011-01-01 03:00:00       1        0           0        1   9.84  14.395   \n",
       "2011-01-01 04:00:00       1        0           0        1   9.84  14.395   \n",
       "...                     ...      ...         ...      ...    ...     ...   \n",
       "2012-12-31 19:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 20:00:00       1        0           1        2  10.66  12.880   \n",
       "2012-12-31 21:00:00       1        0           1        1  10.66  12.880   \n",
       "2012-12-31 22:00:00       1        0           1        1  10.66  13.635   \n",
       "2012-12-31 23:00:00       1        0           1        1  10.66  13.635   \n",
       "\n",
       "                     humidity  windspeed  casual  registered  ...  year  \\\n",
       "datetime                                                      ...         \n",
       "2011-01-01 00:00:00        81     0.0000     3.0        13.0  ...  2011   \n",
       "2011-01-01 01:00:00        80     0.0000     8.0        32.0  ...  2011   \n",
       "2011-01-01 02:00:00        80     0.0000     5.0        27.0  ...  2011   \n",
       "2011-01-01 03:00:00        75     0.0000     3.0        10.0  ...  2011   \n",
       "2011-01-01 04:00:00        75     0.0000     0.0         1.0  ...  2011   \n",
       "...                       ...        ...     ...         ...  ...   ...   \n",
       "2012-12-31 19:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 20:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 21:00:00        60    11.0014     NaN         NaN  ...  2012   \n",
       "2012-12-31 22:00:00        56     8.9981     NaN         NaN  ...  2012   \n",
       "2012-12-31 23:00:00        65     8.9981     NaN         NaN  ...  2012   \n",
       "\n",
       "                     month_number     month day_of_month  week_day hour_day  \\\n",
       "datetime                                                                      \n",
       "2011-01-01 00:00:00             1   January            1  Saturday        0   \n",
       "2011-01-01 01:00:00             1   January            1  Saturday        1   \n",
       "2011-01-01 02:00:00             1   January            1  Saturday        2   \n",
       "2011-01-01 03:00:00             1   January            1  Saturday        3   \n",
       "2011-01-01 04:00:00             1   January            1  Saturday        4   \n",
       "...                           ...       ...          ...       ...      ...   \n",
       "2012-12-31 19:00:00            12  December           31    Monday       19   \n",
       "2012-12-31 20:00:00            12  December           31    Monday       20   \n",
       "2012-12-31 21:00:00            12  December           31    Monday       21   \n",
       "2012-12-31 22:00:00            12  December           31    Monday       22   \n",
       "2012-12-31 23:00:00            12  December           31    Monday       23   \n",
       "\n",
       "                     season_name hour_of_day  part_of_day     date  \n",
       "datetime                                                            \n",
       "2011-01-01 00:00:00       winter           0        night  2011-01  \n",
       "2011-01-01 01:00:00       winter           1        night  2011-01  \n",
       "2011-01-01 02:00:00       winter           2        night  2011-01  \n",
       "2011-01-01 03:00:00       winter           3        night  2011-01  \n",
       "2011-01-01 04:00:00       winter           4        night  2011-01  \n",
       "...                          ...         ...          ...      ...  \n",
       "2012-12-31 19:00:00       winter          19      evening  2012-12  \n",
       "2012-12-31 20:00:00       winter          20      evening  2012-12  \n",
       "2012-12-31 21:00:00       winter          21      evening  2012-12  \n",
       "2012-12-31 22:00:00       winter          22      evening  2012-12  \n",
       "2012-12-31 23:00:00       winter          23      evening  2012-12  \n",
       "\n",
       "[17379 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column only with year and month\n",
    "\n",
    "df_dt1['date'] = df_dt1.index.to_period('M')\n",
    "df_dt1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df6caf2b-5aa9-4589-a032-da99d0d56028",
   "metadata": {
    "tags": []
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"7\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_day</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2011-01</th>\n",
       "      <td>74.967742</td>\n",
       "      <td>49.333333</td>\n",
       "      <td>48.737288</td>\n",
       "      <td>54.367521</td>\n",
       "      <td>87.544304</td>\n",
       "      <td>68.600000</td>\n",
       "      <td>56.726190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02</th>\n",
       "      <td>101.815217</td>\n",
       "      <td>89.457447</td>\n",
       "      <td>71.572917</td>\n",
       "      <td>100.236559</td>\n",
       "      <td>88.516129</td>\n",
       "      <td>75.147727</td>\n",
       "      <td>81.505376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03</th>\n",
       "      <td>104.297872</td>\n",
       "      <td>98.913043</td>\n",
       "      <td>109.687500</td>\n",
       "      <td>85.956989</td>\n",
       "      <td>70.415254</td>\n",
       "      <td>99.042017</td>\n",
       "      <td>82.898305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04</th>\n",
       "      <td>127.958333</td>\n",
       "      <td>146.147368</td>\n",
       "      <td>129.091667</td>\n",
       "      <td>157.760417</td>\n",
       "      <td>187.885417</td>\n",
       "      <td>115.302083</td>\n",
       "      <td>160.385417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05</th>\n",
       "      <td>230.593750</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>235.968750</td>\n",
       "      <td>197.825000</td>\n",
       "      <td>209.625000</td>\n",
       "      <td>191.533333</td>\n",
       "      <td>172.635417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06</th>\n",
       "      <td>231.416667</td>\n",
       "      <td>202.864583</td>\n",
       "      <td>243.333333</td>\n",
       "      <td>231.708333</td>\n",
       "      <td>206.033333</td>\n",
       "      <td>210.791667</td>\n",
       "      <td>223.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07</th>\n",
       "      <td>228.725000</td>\n",
       "      <td>201.072917</td>\n",
       "      <td>239.625000</td>\n",
       "      <td>220.341667</td>\n",
       "      <td>223.302083</td>\n",
       "      <td>196.354167</td>\n",
       "      <td>215.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08</th>\n",
       "      <td>201.291667</td>\n",
       "      <td>178.300000</td>\n",
       "      <td>201.733333</td>\n",
       "      <td>193.348315</td>\n",
       "      <td>184.854167</td>\n",
       "      <td>192.241667</td>\n",
       "      <td>194.158333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09</th>\n",
       "      <td>194.441667</td>\n",
       "      <td>171.800000</td>\n",
       "      <td>206.312500</td>\n",
       "      <td>207.583333</td>\n",
       "      <td>178.109244</td>\n",
       "      <td>154.652632</td>\n",
       "      <td>144.677083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10</th>\n",
       "      <td>215.791667</td>\n",
       "      <td>186.725000</td>\n",
       "      <td>171.966667</td>\n",
       "      <td>196.541667</td>\n",
       "      <td>220.958333</td>\n",
       "      <td>200.500000</td>\n",
       "      <td>154.242105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11</th>\n",
       "      <td>172.458333</td>\n",
       "      <td>170.147368</td>\n",
       "      <td>174.531250</td>\n",
       "      <td>184.552083</td>\n",
       "      <td>161.604167</td>\n",
       "      <td>149.750000</td>\n",
       "      <td>160.691667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12</th>\n",
       "      <td>142.991667</td>\n",
       "      <td>126.294737</td>\n",
       "      <td>124.283333</td>\n",
       "      <td>115.589474</td>\n",
       "      <td>135.758333</td>\n",
       "      <td>136.604167</td>\n",
       "      <td>93.947368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01</th>\n",
       "      <td>135.906250</td>\n",
       "      <td>79.655462</td>\n",
       "      <td>109.291667</td>\n",
       "      <td>93.025000</td>\n",
       "      <td>137.062500</td>\n",
       "      <td>126.016949</td>\n",
       "      <td>105.927083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02</th>\n",
       "      <td>166.125000</td>\n",
       "      <td>136.063830</td>\n",
       "      <td>117.864583</td>\n",
       "      <td>98.885417</td>\n",
       "      <td>157.135417</td>\n",
       "      <td>148.231579</td>\n",
       "      <td>141.478992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03</th>\n",
       "      <td>174.341667</td>\n",
       "      <td>187.145833</td>\n",
       "      <td>192.808333</td>\n",
       "      <td>183.042105</td>\n",
       "      <td>210.625000</td>\n",
       "      <td>163.687500</td>\n",
       "      <td>206.312500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04</th>\n",
       "      <td>255.010417</td>\n",
       "      <td>196.521008</td>\n",
       "      <td>258.135417</td>\n",
       "      <td>215.008333</td>\n",
       "      <td>222.322917</td>\n",
       "      <td>245.708333</td>\n",
       "      <td>220.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05</th>\n",
       "      <td>287.906250</td>\n",
       "      <td>201.333333</td>\n",
       "      <td>304.375000</td>\n",
       "      <td>264.791667</td>\n",
       "      <td>267.541667</td>\n",
       "      <td>218.641667</td>\n",
       "      <td>233.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06</th>\n",
       "      <td>272.100000</td>\n",
       "      <td>247.614583</td>\n",
       "      <td>305.916667</td>\n",
       "      <td>307.322917</td>\n",
       "      <td>302.000000</td>\n",
       "      <td>251.218750</td>\n",
       "      <td>289.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-07</th>\n",
       "      <td>245.156250</td>\n",
       "      <td>247.433333</td>\n",
       "      <td>209.666667</td>\n",
       "      <td>220.850000</td>\n",
       "      <td>268.625000</td>\n",
       "      <td>243.900000</td>\n",
       "      <td>278.343750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-08</th>\n",
       "      <td>258.833333</td>\n",
       "      <td>234.250000</td>\n",
       "      <td>266.187500</td>\n",
       "      <td>207.593750</td>\n",
       "      <td>275.150000</td>\n",
       "      <td>241.343750</td>\n",
       "      <td>271.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09</th>\n",
       "      <td>308.458333</td>\n",
       "      <td>266.364583</td>\n",
       "      <td>298.600000</td>\n",
       "      <td>285.708333</td>\n",
       "      <td>280.468750</td>\n",
       "      <td>252.958333</td>\n",
       "      <td>291.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10</th>\n",
       "      <td>271.250000</td>\n",
       "      <td>238.721649</td>\n",
       "      <td>281.656250</td>\n",
       "      <td>220.645833</td>\n",
       "      <td>284.385417</td>\n",
       "      <td>243.906542</td>\n",
       "      <td>284.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11</th>\n",
       "      <td>234.583333</td>\n",
       "      <td>219.229167</td>\n",
       "      <td>226.510417</td>\n",
       "      <td>224.208333</td>\n",
       "      <td>222.372881</td>\n",
       "      <td>189.718750</td>\n",
       "      <td>221.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12</th>\n",
       "      <td>151.562500</td>\n",
       "      <td>158.336134</td>\n",
       "      <td>155.775000</td>\n",
       "      <td>121.791667</td>\n",
       "      <td>174.593750</td>\n",
       "      <td>201.200000</td>\n",
       "      <td>174.093750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count                                                  \\\n",
       "week_day      Friday      Monday    Saturday      Sunday    Thursday   \n",
       "date                                                                   \n",
       "2011-01    74.967742   49.333333   48.737288   54.367521   87.544304   \n",
       "2011-02   101.815217   89.457447   71.572917  100.236559   88.516129   \n",
       "2011-03   104.297872   98.913043  109.687500   85.956989   70.415254   \n",
       "2011-04   127.958333  146.147368  129.091667  157.760417  187.885417   \n",
       "2011-05   230.593750  186.000000  235.968750  197.825000  209.625000   \n",
       "2011-06   231.416667  202.864583  243.333333  231.708333  206.033333   \n",
       "2011-07   228.725000  201.072917  239.625000  220.341667  223.302083   \n",
       "2011-08   201.291667  178.300000  201.733333  193.348315  184.854167   \n",
       "2011-09   194.441667  171.800000  206.312500  207.583333  178.109244   \n",
       "2011-10   215.791667  186.725000  171.966667  196.541667  220.958333   \n",
       "2011-11   172.458333  170.147368  174.531250  184.552083  161.604167   \n",
       "2011-12   142.991667  126.294737  124.283333  115.589474  135.758333   \n",
       "2012-01   135.906250   79.655462  109.291667   93.025000  137.062500   \n",
       "2012-02   166.125000  136.063830  117.864583   98.885417  157.135417   \n",
       "2012-03   174.341667  187.145833  192.808333  183.042105  210.625000   \n",
       "2012-04   255.010417  196.521008  258.135417  215.008333  222.322917   \n",
       "2012-05   287.906250  201.333333  304.375000  264.791667  267.541667   \n",
       "2012-06   272.100000  247.614583  305.916667  307.322917  302.000000   \n",
       "2012-07   245.156250  247.433333  209.666667  220.850000  268.625000   \n",
       "2012-08   258.833333  234.250000  266.187500  207.593750  275.150000   \n",
       "2012-09   308.458333  266.364583  298.600000  285.708333  280.468750   \n",
       "2012-10   271.250000  238.721649  281.656250  220.645833  284.385417   \n",
       "2012-11   234.583333  219.229167  226.510417  224.208333  222.372881   \n",
       "2012-12   151.562500  158.336134  155.775000  121.791667  174.593750   \n",
       "\n",
       "                                  \n",
       "week_day     Tuesday   Wednesday  \n",
       "date                              \n",
       "2011-01    68.600000   56.726190  \n",
       "2011-02    75.147727   81.505376  \n",
       "2011-03    99.042017   82.898305  \n",
       "2011-04   115.302083  160.385417  \n",
       "2011-05   191.533333  172.635417  \n",
       "2011-06   210.791667  223.041667  \n",
       "2011-07   196.354167  215.375000  \n",
       "2011-08   192.241667  194.158333  \n",
       "2011-09   154.652632  144.677083  \n",
       "2011-10   200.500000  154.242105  \n",
       "2011-11   149.750000  160.691667  \n",
       "2011-12   136.604167   93.947368  \n",
       "2012-01   126.016949  105.927083  \n",
       "2012-02   148.231579  141.478992  \n",
       "2012-03   163.687500  206.312500  \n",
       "2012-04   245.708333  220.631579  \n",
       "2012-05   218.641667  233.066667  \n",
       "2012-06   251.218750  289.000000  \n",
       "2012-07   243.900000  278.343750  \n",
       "2012-08   241.343750  271.033333  \n",
       "2012-09   252.958333  291.281250  \n",
       "2012-10   243.906542  284.366667  \n",
       "2012-11   189.718750  221.916667  \n",
       "2012-12   201.200000  174.093750  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a pivot_table to later plot in a Bar Chart\n",
    "\n",
    "df_dt1.pivot_table(values=['count'],\n",
    "                      columns='week_day',\n",
    "                      index='date',\n",
    "                      aggfunc='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2777eca6-def7-49e7-bd3c-ad4dd8971da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table = df_dt1.pivot_table(values=['count', 'registered','casual'] ,\n",
    "                      columns='week_day',\n",
    "                      aggfunc='mean');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0bc777ec-adcc-4a22-a67c-9a404799e462",
   "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>week_day</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>casual</th>\n",
       "      <td>31.001962</td>\n",
       "      <td>29.843972</td>\n",
       "      <td>63.625000</td>\n",
       "      <td>57.051298</td>\n",
       "      <td>24.007083</td>\n",
       "      <td>22.979207</td>\n",
       "      <td>22.521599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.249698</td>\n",
       "      <td>175.096410</td>\n",
       "      <td>194.396497</td>\n",
       "      <td>182.842126</td>\n",
       "      <td>198.999595</td>\n",
       "      <td>180.951080</td>\n",
       "      <td>187.644040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>registered</th>\n",
       "      <td>166.842381</td>\n",
       "      <td>160.546744</td>\n",
       "      <td>133.040404</td>\n",
       "      <td>123.788474</td>\n",
       "      <td>173.289118</td>\n",
       "      <td>166.744639</td>\n",
       "      <td>165.889749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "week_day        Friday      Monday    Saturday      Sunday    Thursday  \\\n",
       "casual       31.001962   29.843972   63.625000   57.051298   24.007083   \n",
       "count       200.249698  175.096410  194.396497  182.842126  198.999595   \n",
       "registered  166.842381  160.546744  133.040404  123.788474  173.289118   \n",
       "\n",
       "week_day       Tuesday   Wednesday  \n",
       "casual       22.979207   22.521599  \n",
       "count       180.951080  187.644040  \n",
       "registered  166.744639  165.889749  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d737c9b-7a82-499d-9185-7332508471af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dt1.pivot_table(values=['count', 'registered','casual'] ,\n",
    "                      columns='week_day',\n",
    "                      aggfunc='mean').plot(kind='bar');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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