online_store_analysis_part_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "242b2242-6f8c-4da7-a213-2090cab8a3df",
   "metadata": {},
   "source": [
    "# Averages per distance groups calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ceffb8e0-cfb7-4125-884a-a971443b424a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da9c0b99-e91a-4242-828c-237ae7458218",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_unique_id</th>\n",
       "      <th>zip_code_prefix_customer</th>\n",
       "      <th>customer_city</th>\n",
       "      <th>sc_state_customer</th>\n",
       "      <th>geolocation_lat_customer</th>\n",
       "      <th>geolocation_lng_customer</th>\n",
       "      <th>state_customer</th>\n",
       "      <th>region_customer</th>\n",
       "      <th>order_id</th>\n",
       "      <th>...</th>\n",
       "      <th>shipping_limit_date_hour</th>\n",
       "      <th>zip_code_prefix_seller</th>\n",
       "      <th>seller_city</th>\n",
       "      <th>sc_state_seller</th>\n",
       "      <th>geolocation_lat_seller</th>\n",
       "      <th>geolocation_lng_seller</th>\n",
       "      <th>state_seller</th>\n",
       "      <th>region_seller</th>\n",
       "      <th>dist_in_km</th>\n",
       "      <th>distance_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>06b8999e2fba1a1fbc88172c00ba8bc7</td>\n",
       "      <td>861eff4711a542e4b93843c6dd7febb0</td>\n",
       "      <td>14409</td>\n",
       "      <td>franca</td>\n",
       "      <td>SP</td>\n",
       "      <td>-20.498489</td>\n",
       "      <td>-47.396929</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>00e7ee1b050b8499577073aeb2a297a1</td>\n",
       "      <td>...</td>\n",
       "      <td>15:22:12</td>\n",
       "      <td>8577</td>\n",
       "      <td>itaquaquecetuba</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.486111</td>\n",
       "      <td>-46.366721</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>254.302</td>\n",
       "      <td>group_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18955e83d337fd6b2def6b18a428ac77</td>\n",
       "      <td>290c77bc529b7ac935b93aa66c333dc3</td>\n",
       "      <td>9790</td>\n",
       "      <td>sao bernardo do campo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.727992</td>\n",
       "      <td>-46.542848</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>29150127e6685892b6eab3eec79f59c7</td>\n",
       "      <td>...</td>\n",
       "      <td>20:58:32</td>\n",
       "      <td>88303</td>\n",
       "      <td>itajai</td>\n",
       "      <td>SC</td>\n",
       "      <td>-26.912574</td>\n",
       "      <td>-48.673980</td>\n",
       "      <td>Santa Catarina</td>\n",
       "      <td>south</td>\n",
       "      <td>336.323</td>\n",
       "      <td>group_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4e7b3e00288586ebd08712fdd0374a03</td>\n",
       "      <td>060e732b5b29e8181a18229c7b0b2b5e</td>\n",
       "      <td>1151</td>\n",
       "      <td>sao paulo</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.531642</td>\n",
       "      <td>-46.656289</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>b2059ed67ce144a36e2aa97d2c9e9ad2</td>\n",
       "      <td>...</td>\n",
       "      <td>16:19:10</td>\n",
       "      <td>8577</td>\n",
       "      <td>itaquaquecetuba</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.486111</td>\n",
       "      <td>-46.366721</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>32.386</td>\n",
       "      <td>group_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b2b6027bc5c5109e529d4dc6358b12c3</td>\n",
       "      <td>259dac757896d24d7702b9acbbff3f3c</td>\n",
       "      <td>8775</td>\n",
       "      <td>mogi das cruzes</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.499702</td>\n",
       "      <td>-46.185233</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>951670f92359f4fe4a63112aa7306eba</td>\n",
       "      <td>...</td>\n",
       "      <td>16:31:16</td>\n",
       "      <td>8577</td>\n",
       "      <td>itaquaquecetuba</td>\n",
       "      <td>SP</td>\n",
       "      <td>-23.486111</td>\n",
       "      <td>-46.366721</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>20.208</td>\n",
       "      <td>group_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4f2d8ab171c80ec8364f7c12e35b23ad</td>\n",
       "      <td>345ecd01c38d18a9036ed96c73b8d066</td>\n",
       "      <td>13056</td>\n",
       "      <td>campinas</td>\n",
       "      <td>SP</td>\n",
       "      <td>-22.975100</td>\n",
       "      <td>-47.142925</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>6b7d50bd145f6fc7f33cebabd7e49d0f</td>\n",
       "      <td>...</td>\n",
       "      <td>10:10:09</td>\n",
       "      <td>14940</td>\n",
       "      <td>ibitinga</td>\n",
       "      <td>SP</td>\n",
       "      <td>-21.757321</td>\n",
       "      <td>-48.829744</td>\n",
       "      <td>São Paulo</td>\n",
       "      <td>southeast</td>\n",
       "      <td>208.307</td>\n",
       "      <td>group_2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        customer_id                customer_unique_id  \\\n",
       "0  06b8999e2fba1a1fbc88172c00ba8bc7  861eff4711a542e4b93843c6dd7febb0   \n",
       "1  18955e83d337fd6b2def6b18a428ac77  290c77bc529b7ac935b93aa66c333dc3   \n",
       "2  4e7b3e00288586ebd08712fdd0374a03  060e732b5b29e8181a18229c7b0b2b5e   \n",
       "3  b2b6027bc5c5109e529d4dc6358b12c3  259dac757896d24d7702b9acbbff3f3c   \n",
       "4  4f2d8ab171c80ec8364f7c12e35b23ad  345ecd01c38d18a9036ed96c73b8d066   \n",
       "\n",
       "   zip_code_prefix_customer          customer_city sc_state_customer  \\\n",
       "0                     14409                 franca                SP   \n",
       "1                      9790  sao bernardo do campo                SP   \n",
       "2                      1151              sao paulo                SP   \n",
       "3                      8775        mogi das cruzes                SP   \n",
       "4                     13056               campinas                SP   \n",
       "\n",
       "   geolocation_lat_customer  geolocation_lng_customer state_customer  \\\n",
       "0                -20.498489                -47.396929      São Paulo   \n",
       "1                -23.727992                -46.542848      São Paulo   \n",
       "2                -23.531642                -46.656289      São Paulo   \n",
       "3                -23.499702                -46.185233      São Paulo   \n",
       "4                -22.975100                -47.142925      São Paulo   \n",
       "\n",
       "  region_customer                          order_id  ...  \\\n",
       "0       southeast  00e7ee1b050b8499577073aeb2a297a1  ...   \n",
       "1       southeast  29150127e6685892b6eab3eec79f59c7  ...   \n",
       "2       southeast  b2059ed67ce144a36e2aa97d2c9e9ad2  ...   \n",
       "3       southeast  951670f92359f4fe4a63112aa7306eba  ...   \n",
       "4       southeast  6b7d50bd145f6fc7f33cebabd7e49d0f  ...   \n",
       "\n",
       "  shipping_limit_date_hour zip_code_prefix_seller      seller_city  \\\n",
       "0                 15:22:12                   8577  itaquaquecetuba   \n",
       "1                 20:58:32                  88303           itajai   \n",
       "2                 16:19:10                   8577  itaquaquecetuba   \n",
       "3                 16:31:16                   8577  itaquaquecetuba   \n",
       "4                 10:10:09                  14940         ibitinga   \n",
       "\n",
       "  sc_state_seller geolocation_lat_seller geolocation_lng_seller  \\\n",
       "0              SP             -23.486111             -46.366721   \n",
       "1              SC             -26.912574             -48.673980   \n",
       "2              SP             -23.486111             -46.366721   \n",
       "3              SP             -23.486111             -46.366721   \n",
       "4              SP             -21.757321             -48.829744   \n",
       "\n",
       "     state_seller  region_seller  dist_in_km  distance_group  \n",
       "0       São Paulo      southeast     254.302         group_2  \n",
       "1  Santa Catarina          south     336.323         group_3  \n",
       "2       São Paulo      southeast      32.386         group_1  \n",
       "3       São Paulo      southeast      20.208         group_1  \n",
       "4       São Paulo      southeast     208.307         group_2  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f1_olist = pd.read_csv('data_sets/final_datasets/data_f1_olist.csv')\n",
    "data_f1_olist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9ccf654-8faa-4516-9c49-bba0325eff7f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id                                           110180\n",
       "customer_unique_id                                    110180\n",
       "zip_code_prefix_customer                              110180\n",
       "customer_city                                         110180\n",
       "sc_state_customer                                     110180\n",
       "geolocation_lat_customer                              109892\n",
       "geolocation_lng_customer                              109892\n",
       "state_customer                                        110180\n",
       "region_customer                                       110180\n",
       "order_id                                              110180\n",
       "order_status                                          110180\n",
       "order_purchase_timestamp                              110180\n",
       "order_approved_at                                     110180\n",
       "order_delivered_carrier_date                          110180\n",
       "order_delivered_customer_date                         110180\n",
       "order_estimated_delivery_date                         110180\n",
       "delta_purchase_approved                               110180\n",
       "delta_purchase_delivered_carrier                      110180\n",
       "delta_purchase_delivered_customer                     110180\n",
       "delta_delivered_customer_carrier                      110180\n",
       "delta_delivered_customer_order_estimated_delivery_    110180\n",
       "order_item_id                                         110180\n",
       "product_id                                            110180\n",
       "seller_id                                             110180\n",
       "shipping_limit_date                                   110180\n",
       "price                                                 110180\n",
       "freight_value                                         110180\n",
       "shipping_limit_date_only                              110180\n",
       "shipping_limit_date_hour                              110180\n",
       "zip_code_prefix_seller                                110180\n",
       "seller_city                                           110180\n",
       "sc_state_seller                                       110180\n",
       "geolocation_lat_seller                                109931\n",
       "geolocation_lng_seller                                109931\n",
       "state_seller                                          110180\n",
       "region_seller                                         110180\n",
       "dist_in_km                                            109644\n",
       "distance_group                                        109644\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_f1_olist.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d1dd204-5a7d-4fc2-9144-9412df215eeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 110180 entries, 0 to 110179\n",
      "Data columns (total 38 columns):\n",
      " #   Column                                              Non-Null Count   Dtype  \n",
      "---  ------                                              --------------   -----  \n",
      " 0   customer_id                                         110180 non-null  object \n",
      " 1   customer_unique_id                                  110180 non-null  object \n",
      " 2   zip_code_prefix_customer                            110180 non-null  int64  \n",
      " 3   customer_city                                       110180 non-null  object \n",
      " 4   sc_state_customer                                   110180 non-null  object \n",
      " 5   geolocation_lat_customer                            109892 non-null  float64\n",
      " 6   geolocation_lng_customer                            109892 non-null  float64\n",
      " 7   state_customer                                      110180 non-null  object \n",
      " 8   region_customer                                     110180 non-null  object \n",
      " 9   order_id                                            110180 non-null  object \n",
      " 10  order_status                                        110180 non-null  object \n",
      " 11  order_purchase_timestamp                            110180 non-null  object \n",
      " 12  order_approved_at                                   110180 non-null  object \n",
      " 13  order_delivered_carrier_date                        110180 non-null  object \n",
      " 14  order_delivered_customer_date                       110180 non-null  object \n",
      " 15  order_estimated_delivery_date                       110180 non-null  object \n",
      " 16  delta_purchase_approved                             110180 non-null  float64\n",
      " 17  delta_purchase_delivered_carrier                    110180 non-null  float64\n",
      " 18  delta_purchase_delivered_customer                   110180 non-null  float64\n",
      " 19  delta_delivered_customer_carrier                    110180 non-null  float64\n",
      " 20  delta_delivered_customer_order_estimated_delivery_  110180 non-null  float64\n",
      " 21  order_item_id                                       110180 non-null  int64  \n",
      " 22  product_id                                          110180 non-null  object \n",
      " 23  seller_id                                           110180 non-null  object \n",
      " 24  shipping_limit_date                                 110180 non-null  object \n",
      " 25  price                                               110180 non-null  float64\n",
      " 26  freight_value                                       110180 non-null  float64\n",
      " 27  shipping_limit_date_only                            110180 non-null  object \n",
      " 28  shipping_limit_date_hour                            110180 non-null  object \n",
      " 29  zip_code_prefix_seller                              110180 non-null  int64  \n",
      " 30  seller_city                                         110180 non-null  object \n",
      " 31  sc_state_seller                                     110180 non-null  object \n",
      " 32  geolocation_lat_seller                              109931 non-null  float64\n",
      " 33  geolocation_lng_seller                              109931 non-null  float64\n",
      " 34  state_seller                                        110180 non-null  object \n",
      " 35  region_seller                                       110180 non-null  object \n",
      " 36  dist_in_km                                          109644 non-null  float64\n",
      " 37  distance_group                                      109644 non-null  object \n",
      "dtypes: float64(12), int64(3), object(23)\n",
      "memory usage: 31.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data_f1_olist.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402b96a7-4918-4794-9a05-6ddd2b8dd63c",
   "metadata": {},
   "source": [
    "### Average order price per distance group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "530588e8-8bca-4133-b429-56e3d69cb68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group_1</th>\n",
       "      <td>101.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_2</th>\n",
       "      <td>112.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_3</th>\n",
       "      <td>117.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_4</th>\n",
       "      <td>118.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_5</th>\n",
       "      <td>127.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_6</th>\n",
       "      <td>145.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_7</th>\n",
       "      <td>146.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_8</th>\n",
       "      <td>151.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_9</th>\n",
       "      <td>217.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price\n",
       "distance_group        \n",
       "group_1         101.23\n",
       "group_2         112.92\n",
       "group_3         117.71\n",
       "group_4         118.88\n",
       "group_5         127.66\n",
       "group_6         145.83\n",
       "group_7         146.61\n",
       "group_8         151.73\n",
       "group_9         217.91"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_price_per_distance_group = round(pd.DataFrame(data_f1_olist.groupby('distance_group')['price'].mean()),2)\n",
    "average_price_per_distance_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4152c15-60b0-456e-b7f0-7e44759476a5",
   "metadata": {},
   "source": [
    "### Average freight value per distance group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1518682-04c0-4aad-b2db-17ca35c3f893",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>freight_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>group_1</th>\n",
       "      <td>11.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_2</th>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_3</th>\n",
       "      <td>20.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_4</th>\n",
       "      <td>20.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_5</th>\n",
       "      <td>22.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_6</th>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_7</th>\n",
       "      <td>30.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_8</th>\n",
       "      <td>34.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_9</th>\n",
       "      <td>42.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                freight_value\n",
       "distance_group               \n",
       "group_1                 11.97\n",
       "group_2                 16.69\n",
       "group_3                 20.01\n",
       "group_4                 20.67\n",
       "group_5                 22.94\n",
       "group_6                 25.18\n",
       "group_7                 30.09\n",
       "group_8                 34.66\n",
       "group_9                 42.94"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_freight_value_per_distance_group = round(pd.DataFrame(data_f1_olist.groupby('distance_group')['freight_value'].mean()),2)\n",
    "average_freight_value_per_distance_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e412c9b-d455-4c12-abec-614eb4fa8cb0",
   "metadata": {},
   "source": [
    "### Average time between the purchase is placed and the order approvement (per distance group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c858608-57ae-4641-9921-4b2fdd1ef0a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "      <th>delta_purchase_approved</th>\n",
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       "      <th>distance_group</th>\n",
       "      <th></th>\n",
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       "      <td>9.66</td>\n",
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       "      <td>9.96</td>\n",
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       "      <td>11.22</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                delta_purchase_approved\n",
       "distance_group                         \n",
       "group_1                            9.56\n",
       "group_2                            9.66\n",
       "group_3                            9.96\n",
       "group_4                           10.44\n",
       "group_5                           10.91\n",
       "group_6                           10.57\n",
       "group_7                           11.04\n",
       "group_8                           11.73\n",
       "group_9                           11.22"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_delta_purchase_approved_per_distance_group = round(pd.DataFrame(data_f1_olist.groupby('distance_group')['delta_purchase_approved'].mean()),2)\n",
    "average_delta_purchase_approved_per_distance_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1801b8d8-3465-4676-a2f8-40f63e64bf40",
   "metadata": {},
   "source": [
    "### Average time between the purchase is placed and the delivery to the carrier (per distance group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "edb5a988-e4a0-4ff2-86f3-a718a9408fe5",
   "metadata": {},
   "outputs": [
    {
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       "      <th>delta_purchase_delivered_carrier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance_group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>group_1</th>\n",
       "      <td>76.57</td>\n",
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       "      <td>75.25</td>\n",
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       "      <th>group_6</th>\n",
       "      <td>82.98</td>\n",
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       "      <td>81.60</td>\n",
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       "      <th>group_9</th>\n",
       "      <td>88.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                delta_purchase_delivered_carrier\n",
       "distance_group                                  \n",
       "group_1                                    76.57\n",
       "group_2                                    75.25\n",
       "group_3                                    78.56\n",
       "group_4                                    78.46\n",
       "group_5                                    80.01\n",
       "group_6                                    82.98\n",
       "group_7                                    82.91\n",
       "group_8                                    81.60\n",
       "group_9                                    88.26"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_delta_purchase_delivered_carrier_per_distance_group = round(pd.DataFrame(data_f1_olist.groupby('distance_group')['delta_purchase_delivered_carrier'].mean()),2)\n",
    "average_delta_purchase_delivered_carrier_per_distance_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b6b603-2b66-453d-a9cb-413d90bb0a2f",
   "metadata": {},
   "source": [
    "### Average product journey  per distance group: Average time between the purchase is place and the delivery to the customer (per distance group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7208f02f-78f0-4129-901f-0f069e0d50c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>533.52</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                delta_purchase_delivered_customer\n",
       "distance_group                                   \n",
       "group_1                                    158.83\n",
       "group_2                                    246.51\n",
       "group_3                                    302.24\n",
       "group_4                                    339.36\n",
       "group_5                                    361.40\n",
       "group_6                                    396.52\n",
       "group_7                                    447.58\n",
       "group_8                                    491.21\n",
       "group_9                                    533.52"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_delta_purchase_delivered_customer_per_distance_group = round(pd.DataFrame(data_f1_olist.groupby('distance_group')['delta_purchase_delivered_customer'].mean()),2)\n",
    "average_delta_purchase_delivered_customer_per_distance_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53e27a2b-4d22-4961-bc7e-228ea002cbd9",
   "metadata": {},
   "source": [
    "### Average time between the delivery to the carrier and the delivery to the customer (per distance group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7fcc5150-8a1d-47c6-bca6-75a7c5a82bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "                delta_delivered_customer_carrier\n",
       "distance_group                                  \n",
       "group_1                                    81.77\n",
       "group_2                                   170.76\n",
       "group_3                                   223.18\n",
       "group_4                                   260.40\n",
       "group_5                                   280.88\n",
       "group_6                                   313.03\n",
       "group_7                                   364.17\n",
       "group_8                                   409.10\n",
       "group_9                                   444.77"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_delta_delivered_customer_carrier_per_distance_group = round(pd.DataFrame(data_f1_olist.groupby('distance_group')['delta_delivered_customer_carrier'].mean()),2)\n",
    "average_delta_delivered_customer_carrier_per_distance_group "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b36de9-7043-4164-9814-42030d30f49e",
   "metadata": {},
   "source": [
    "### Average time between delivery to the customer and the estimated delivery time (per distance group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6da51b3d-97d2-4fef-83b7-bca7e4257e58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>delta_delivered_customer_order_estimated_delivery_</th>\n",
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      ],
      "text/plain": [
       "                delta_delivered_customer_order_estimated_delivery_\n",
       "distance_group                                                    \n",
       "group_1                                                    214.97 \n",
       "group_2                                                    268.57 \n",
       "group_3                                                    285.84 \n",
       "group_4                                                    291.62 \n",
       "group_5                                                    297.81 \n",
       "group_6                                                    300.31 \n",
       "group_7                                                    285.37 \n",
       "group_8                                                    284.68 \n",
       "group_9                                                    334.54 "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_delta_delivered_customer_order_estimated_delivery_per_distance_group = round(pd.DataFrame(data_f1_olist.groupby('distance_group')['delta_delivered_customer_order_estimated_delivery_'].mean()),2)\n",
    "average_delta_delivered_customer_order_estimated_delivery_per_distance_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e86e899d-5f0d-4183-9a8a-72098278f351",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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