Data analytics and business intelligence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f448f70e",
   "metadata": {},
   "source": [
    "#### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b68657f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tableone import TableOne, load_dataset\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "461b4d52",
   "metadata": {},
   "source": [
    "#### Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "d542606f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('SeniorDataAnalystChallenge2.xlsx', sheet_name = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e5dab5",
   "metadata": {},
   "source": [
    "#### Overview of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ba75bb44",
   "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>order delivery time</th>\n",
       "      <th>order id</th>\n",
       "      <th>customer order rating</th>\n",
       "      <th>type of issue reported</th>\n",
       "      <th>region</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-06-02 04:23:16 UTC</td>\n",
       "      <td>233599337</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-06-02 03:57:50 UTC</td>\n",
       "      <td>233599376</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-06-02 02:52:38 UTC</td>\n",
       "      <td>233599328</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-06-02 02:52:04 UTC</td>\n",
       "      <td>233599070</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-02 02:41:43 UTC</td>\n",
       "      <td>233599100</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-06-02 02:21:17 UTC</td>\n",
       "      <td>233599256</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-06-02 02:19:38 UTC</td>\n",
       "      <td>233599234</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-06-02 02:11:52 UTC</td>\n",
       "      <td>233599182</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-06-02 02:10:30 UTC</td>\n",
       "      <td>233599154</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-06-02 02:08:17 UTC</td>\n",
       "      <td>233599142</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       order delivery time   order id  customer order rating  \\\n",
       "0  2014-06-02 04:23:16 UTC  233599337                      5   \n",
       "1  2014-06-02 03:57:50 UTC  233599376                      5   \n",
       "2  2014-06-02 02:52:38 UTC  233599328                      5   \n",
       "3  2014-06-02 02:52:04 UTC  233599070                      5   \n",
       "4  2014-06-02 02:41:43 UTC  233599100                      5   \n",
       "5  2014-06-02 02:21:17 UTC  233599256                      5   \n",
       "6  2014-06-02 02:19:38 UTC  233599234                      5   \n",
       "7  2014-06-02 02:11:52 UTC  233599182                      5   \n",
       "8  2014-06-02 02:10:30 UTC  233599154                      5   \n",
       "9  2014-06-02 02:08:17 UTC  233599142                      5   \n",
       "\n",
       "  type of issue reported region  Unnamed: 5  \n",
       "0                    NaN    chi         NaN  \n",
       "1                    NaN    chi         NaN  \n",
       "2                    NaN    chi         NaN  \n",
       "3                    NaN    chi         NaN  \n",
       "4                    NaN    chi         NaN  \n",
       "5                    NaN    chi         NaN  \n",
       "6                    NaN    chi         NaN  \n",
       "7                    NaN    chi         NaN  \n",
       "8                    NaN    chi         NaN  \n",
       "9                    NaN    chi         NaN  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8b0064e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Select only the required columns\n",
    "df = df.iloc[0:,0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "40b30d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert delivery time to datetime (to remove the UTC and use a ISO-8601 format)\n",
    "df['order delivery time'] = pd.to_datetime(df['order delivery time'])\n",
    "#Collect month\n",
    "df['month'] = df['order delivery time'].dt.month\n",
    "#Collect week\n",
    "df['week'] = df['order delivery time'].dt.isocalendar().week\n",
    "#collect hour\n",
    "df['hour'] = df['order delivery time'].dt.hour\n",
    "\n",
    "#Fill the type of issue column eiyh \"no-issue\" for non issue purchases\n",
    "df['type of issue reported'] = df['type of issue reported'].fillna('No-Issue')\n",
    "#Get day of the week\n",
    "df['day'] = df['order delivery time'].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cdff6dc5",
   "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>order delivery time</th>\n",
       "      <th>order id</th>\n",
       "      <th>customer order rating</th>\n",
       "      <th>type of issue reported</th>\n",
       "      <th>region</th>\n",
       "      <th>month</th>\n",
       "      <th>week</th>\n",
       "      <th>hour</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-06-02 04:23:16+00:00</td>\n",
       "      <td>233599337</td>\n",
       "      <td>5</td>\n",
       "      <td>No-Issue</td>\n",
       "      <td>chi</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-06-02 03:57:50+00:00</td>\n",
       "      <td>233599376</td>\n",
       "      <td>5</td>\n",
       "      <td>No-Issue</td>\n",
       "      <td>chi</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-06-02 02:52:38+00:00</td>\n",
       "      <td>233599328</td>\n",
       "      <td>5</td>\n",
       "      <td>No-Issue</td>\n",
       "      <td>chi</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-06-02 02:52:04+00:00</td>\n",
       "      <td>233599070</td>\n",
       "      <td>5</td>\n",
       "      <td>No-Issue</td>\n",
       "      <td>chi</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-02 02:41:43+00:00</td>\n",
       "      <td>233599100</td>\n",
       "      <td>5</td>\n",
       "      <td>No-Issue</td>\n",
       "      <td>chi</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        order delivery time   order id  customer order rating  \\\n",
       "0 2014-06-02 04:23:16+00:00  233599337                      5   \n",
       "1 2014-06-02 03:57:50+00:00  233599376                      5   \n",
       "2 2014-06-02 02:52:38+00:00  233599328                      5   \n",
       "3 2014-06-02 02:52:04+00:00  233599070                      5   \n",
       "4 2014-06-02 02:41:43+00:00  233599100                      5   \n",
       "\n",
       "  type of issue reported region  month  week  hour     day  \n",
       "0               No-Issue    chi      6    23     4  Monday  \n",
       "1               No-Issue    chi      6    23     3  Monday  \n",
       "2               No-Issue    chi      6    23     2  Monday  \n",
       "3               No-Issue    chi      6    23     2  Monday  \n",
       "4               No-Issue    chi      6    23     2  Monday  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f034e1bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "groupby = ['type of issue reported']\n",
    "nonnormal = ['order id']\n",
    "\n",
    "stats_table = TableOne(df,columns = ['type of issue reported', 'customer order rating'],\n",
    "                   groupby=groupby, pval=True, tukey_test=True)\n",
    "stats_table.to_excel('issues and ratings.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "fb5a0012",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">Grouped by type of issue reported</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Damaged or spoiled</th>\n",
       "      <th>Item charged incorrectly</th>\n",
       "      <th>Item missing</th>\n",
       "      <th>No-Issue</th>\n",
       "      <th>Other Order Issue</th>\n",
       "      <th>Poor replacement</th>\n",
       "      <th>Poor service</th>\n",
       "      <th>Wrong item</th>\n",
       "      <th>P-Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>14957</td>\n",
       "      <td>310</td>\n",
       "      <td>21</td>\n",
       "      <td>178</td>\n",
       "      <td>13870</td>\n",
       "      <td>21</td>\n",
       "      <td>54</td>\n",
       "      <td>129</td>\n",
       "      <td>374</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">customer order rating, n (%)</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>154 (1.0)</td>\n",
       "      <td>2 (0.6)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>151 (1.1)</td>\n",
       "      <td></td>\n",
       "      <td>1 (1.9)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>373 (2.5)</td>\n",
       "      <td>49 (15.8)</td>\n",
       "      <td>1 (4.8)</td>\n",
       "      <td>32 (18.0)</td>\n",
       "      <td>204 (1.5)</td>\n",
       "      <td>3 (14.3)</td>\n",
       "      <td>15 (27.8)</td>\n",
       "      <td>39 (30.2)</td>\n",
       "      <td>30 (8.0)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>370 (2.5)</td>\n",
       "      <td>52 (16.8)</td>\n",
       "      <td>1 (4.8)</td>\n",
       "      <td>41 (23.0)</td>\n",
       "      <td>176 (1.3)</td>\n",
       "      <td>2 (9.5)</td>\n",
       "      <td>7 (13.0)</td>\n",
       "      <td>23 (17.8)</td>\n",
       "      <td>68 (18.2)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>778 (5.2)</td>\n",
       "      <td>103 (33.2)</td>\n",
       "      <td></td>\n",
       "      <td>40 (22.5)</td>\n",
       "      <td>449 (3.2)</td>\n",
       "      <td>7 (33.3)</td>\n",
       "      <td>21 (38.9)</td>\n",
       "      <td>33 (25.6)</td>\n",
       "      <td>125 (33.4)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>1680 (11.2)</td>\n",
       "      <td>75 (24.2)</td>\n",
       "      <td>10 (47.6)</td>\n",
       "      <td>40 (22.5)</td>\n",
       "      <td>1395 (10.1)</td>\n",
       "      <td>6 (28.6)</td>\n",
       "      <td>8 (14.8)</td>\n",
       "      <td>26 (20.2)</td>\n",
       "      <td>120 (32.1)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>11602 (77.6)</td>\n",
       "      <td>29 (9.4)</td>\n",
       "      <td>9 (42.9)</td>\n",
       "      <td>25 (14.0)</td>\n",
       "      <td>11495 (82.9)</td>\n",
       "      <td>3 (14.3)</td>\n",
       "      <td>2 (3.7)</td>\n",
       "      <td>8 (6.2)</td>\n",
       "      <td>31 (8.3)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />[1] Chi-squared tests for the following variables may be invalid due to the low number of observations: customer order rating.<br />"
      ],
      "text/plain": [
       "                               Grouped by type of issue reported                                                                                                                                                         \n",
       "                                                         Missing       Overall Damaged or spoiled Item charged incorrectly Item missing      No-Issue Other Order Issue Poor replacement Poor service  Wrong item P-Value\n",
       "n                                                                        14957                310                       21          178         13870                21               54          129         374        \n",
       "customer order rating, n (%) 0                                 0     154 (1.0)            2 (0.6)                                           151 (1.1)                            1 (1.9)                           <0.001\n",
       "                             1                                       373 (2.5)          49 (15.8)                  1 (4.8)    32 (18.0)     204 (1.5)          3 (14.3)        15 (27.8)    39 (30.2)    30 (8.0)        \n",
       "                             2                                       370 (2.5)          52 (16.8)                  1 (4.8)    41 (23.0)     176 (1.3)           2 (9.5)         7 (13.0)    23 (17.8)   68 (18.2)        \n",
       "                             3                                       778 (5.2)         103 (33.2)                             40 (22.5)     449 (3.2)          7 (33.3)        21 (38.9)    33 (25.6)  125 (33.4)        \n",
       "                             4                                     1680 (11.2)          75 (24.2)                10 (47.6)    40 (22.5)   1395 (10.1)          6 (28.6)         8 (14.8)    26 (20.2)  120 (32.1)        \n",
       "                             5                                    11602 (77.6)           29 (9.4)                 9 (42.9)    25 (14.0)  11495 (82.9)          3 (14.3)          2 (3.7)      8 (6.2)    31 (8.3)        \n",
       "[1] Chi-squared tests for the following variables may be invalid due to the low number of observations: customer order rating."
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c634b44a",
   "metadata": {},
   "source": [
    "##### Number of items per customer rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "79a93d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x=\"customer order rating\",  data=df)\n",
    "plt.title('Number of items per customer rating')\n",
    "plt.ylabel('Number of items')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccae0904",
   "metadata": {},
   "source": [
    "#### Number of items per issue type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "097ba0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x=\"type of issue reported\",  data=df)\n",
    "plt.title('Number of items per issue type')\n",
    "plt.ylabel('Number of items')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5cb0a8b",
   "metadata": {},
   "source": [
    "##### Distribution of issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5ee12fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df['type of issue reported'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "51205739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,20))\n",
    "data.plot.pie(autopct=\"%.1f%%\");\n",
    "plt.title('Distribution of issues')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b368fd",
   "metadata": {},
   "source": [
    "#### Explore ratings by region and issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "abc1a854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['chi', 'nyc', 'sf', 'sf '], dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.region.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8cb1b5ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove whitespace from region name\n",
    "df['region'] = df.region.replace('sf ', 'sf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2bd7e806",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['chi', 'nyc', 'sf'], dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.region.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "3c57e92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x=\"region\",  data=df)\n",
    "plt.title('Number of items per region')\n",
    "plt.ylabel('Number of items')\n",
    "plt.xticks(rotation = 90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "522211dd",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"11\" halign=\"left\">Grouped by type of issue reported</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Damaged or spoiled</th>\n",
       "      <th>Item charged incorrectly</th>\n",
       "      <th>Item missing</th>\n",
       "      <th>No-Issue</th>\n",
       "      <th>Other Order Issue</th>\n",
       "      <th>Poor replacement</th>\n",
       "      <th>Poor service</th>\n",
       "      <th>Wrong item</th>\n",
       "      <th>P-Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>14957</td>\n",
       "      <td>310</td>\n",
       "      <td>21</td>\n",
       "      <td>178</td>\n",
       "      <td>13870</td>\n",
       "      <td>21</td>\n",
       "      <td>54</td>\n",
       "      <td>129</td>\n",
       "      <td>374</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">region, n (%)</th>\n",
       "      <th>chi</th>\n",
       "      <td>0</td>\n",
       "      <td>6430 (43.0)</td>\n",
       "      <td>102 (32.9)</td>\n",
       "      <td>13 (61.9)</td>\n",
       "      <td>49 (27.5)</td>\n",
       "      <td>6158 (44.4)</td>\n",
       "      <td>7 (33.3)</td>\n",
       "      <td>4 (7.4)</td>\n",
       "      <td>15 (11.6)</td>\n",
       "      <td>82 (21.9)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nyc</th>\n",
       "      <td></td>\n",
       "      <td>1290 (8.6)</td>\n",
       "      <td>53 (17.1)</td>\n",
       "      <td>3 (14.3)</td>\n",
       "      <td>10 (5.6)</td>\n",
       "      <td>1168 (8.4)</td>\n",
       "      <td>4 (19.0)</td>\n",
       "      <td>9 (16.7)</td>\n",
       "      <td>15 (11.6)</td>\n",
       "      <td>28 (7.5)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf</th>\n",
       "      <td></td>\n",
       "      <td>7237 (48.4)</td>\n",
       "      <td>155 (50.0)</td>\n",
       "      <td>5 (23.8)</td>\n",
       "      <td>119 (66.9)</td>\n",
       "      <td>6544 (47.2)</td>\n",
       "      <td>10 (47.6)</td>\n",
       "      <td>41 (75.9)</td>\n",
       "      <td>99 (76.7)</td>\n",
       "      <td>264 (70.6)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">customer order rating, n (%)</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>154 (1.0)</td>\n",
       "      <td>2 (0.6)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>151 (1.1)</td>\n",
       "      <td></td>\n",
       "      <td>1 (1.9)</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>373 (2.5)</td>\n",
       "      <td>49 (15.8)</td>\n",
       "      <td>1 (4.8)</td>\n",
       "      <td>32 (18.0)</td>\n",
       "      <td>204 (1.5)</td>\n",
       "      <td>3 (14.3)</td>\n",
       "      <td>15 (27.8)</td>\n",
       "      <td>39 (30.2)</td>\n",
       "      <td>30 (8.0)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>370 (2.5)</td>\n",
       "      <td>52 (16.8)</td>\n",
       "      <td>1 (4.8)</td>\n",
       "      <td>41 (23.0)</td>\n",
       "      <td>176 (1.3)</td>\n",
       "      <td>2 (9.5)</td>\n",
       "      <td>7 (13.0)</td>\n",
       "      <td>23 (17.8)</td>\n",
       "      <td>68 (18.2)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>778 (5.2)</td>\n",
       "      <td>103 (33.2)</td>\n",
       "      <td></td>\n",
       "      <td>40 (22.5)</td>\n",
       "      <td>449 (3.2)</td>\n",
       "      <td>7 (33.3)</td>\n",
       "      <td>21 (38.9)</td>\n",
       "      <td>33 (25.6)</td>\n",
       "      <td>125 (33.4)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>1680 (11.2)</td>\n",
       "      <td>75 (24.2)</td>\n",
       "      <td>10 (47.6)</td>\n",
       "      <td>40 (22.5)</td>\n",
       "      <td>1395 (10.1)</td>\n",
       "      <td>6 (28.6)</td>\n",
       "      <td>8 (14.8)</td>\n",
       "      <td>26 (20.2)</td>\n",
       "      <td>120 (32.1)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>11602 (77.6)</td>\n",
       "      <td>29 (9.4)</td>\n",
       "      <td>9 (42.9)</td>\n",
       "      <td>25 (14.0)</td>\n",
       "      <td>11495 (82.9)</td>\n",
       "      <td>3 (14.3)</td>\n",
       "      <td>2 (3.7)</td>\n",
       "      <td>8 (6.2)</td>\n",
       "      <td>31 (8.3)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />[1] Chi-squared tests for the following variables may be invalid due to the low number of observations: region, customer order rating.<br />"
      ],
      "text/plain": [
       "                                 Grouped by type of issue reported                                                                                                                                                         \n",
       "                                                           Missing       Overall Damaged or spoiled Item charged incorrectly Item missing      No-Issue Other Order Issue Poor replacement Poor service  Wrong item P-Value\n",
       "n                                                                          14957                310                       21          178         13870                21               54          129         374        \n",
       "region, n (%)                chi                                 0   6430 (43.0)         102 (32.9)                13 (61.9)    49 (27.5)   6158 (44.4)          7 (33.3)          4 (7.4)    15 (11.6)   82 (21.9)  <0.001\n",
       "                             nyc                                      1290 (8.6)          53 (17.1)                 3 (14.3)     10 (5.6)    1168 (8.4)          4 (19.0)         9 (16.7)    15 (11.6)    28 (7.5)        \n",
       "                             sf                                      7237 (48.4)         155 (50.0)                 5 (23.8)   119 (66.9)   6544 (47.2)         10 (47.6)        41 (75.9)    99 (76.7)  264 (70.6)        \n",
       "customer order rating, n (%) 0                                   0     154 (1.0)            2 (0.6)                                           151 (1.1)                            1 (1.9)                           <0.001\n",
       "                             1                                         373 (2.5)          49 (15.8)                  1 (4.8)    32 (18.0)     204 (1.5)          3 (14.3)        15 (27.8)    39 (30.2)    30 (8.0)        \n",
       "                             2                                         370 (2.5)          52 (16.8)                  1 (4.8)    41 (23.0)     176 (1.3)           2 (9.5)         7 (13.0)    23 (17.8)   68 (18.2)        \n",
       "                             3                                         778 (5.2)         103 (33.2)                             40 (22.5)     449 (3.2)          7 (33.3)        21 (38.9)    33 (25.6)  125 (33.4)        \n",
       "                             4                                       1680 (11.2)          75 (24.2)                10 (47.6)    40 (22.5)   1395 (10.1)          6 (28.6)         8 (14.8)    26 (20.2)  120 (32.1)        \n",
       "                             5                                      11602 (77.6)           29 (9.4)                 9 (42.9)    25 (14.0)  11495 (82.9)          3 (14.3)          2 (3.7)      8 (6.2)    31 (8.3)        \n",
       "[1] Chi-squared tests for the following variables may be invalid due to the low number of observations: region, customer order rating."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby = ['type of issue reported']\n",
    "stats_table = TableOne(df,columns = ['region', 'customer order rating'],\n",
    "                   groupby=groupby, pval=True, tukey_test=True)\n",
    "stats_table.to_excel('region, issues and ratings.xlsx')\n",
    "display(stats_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feee7a53",
   "metadata": {},
   "source": [
    "#### Explore ratings by region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "eedd9d34",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Grouped by region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>chi</th>\n",
       "      <th>nyc</th>\n",
       "      <th>sf</th>\n",
       "      <th>P-Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>14957</td>\n",
       "      <td>6430</td>\n",
       "      <td>1290</td>\n",
       "      <td>7237</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">customer order rating, n (%)</th>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>154 (1.0)</td>\n",
       "      <td>50 (0.8)</td>\n",
       "      <td>8 (0.6)</td>\n",
       "      <td>96 (1.3)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>373 (2.5)</td>\n",
       "      <td>84 (1.3)</td>\n",
       "      <td>45 (3.5)</td>\n",
       "      <td>244 (3.4)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>370 (2.5)</td>\n",
       "      <td>92 (1.4)</td>\n",
       "      <td>43 (3.3)</td>\n",
       "      <td>235 (3.2)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>778 (5.2)</td>\n",
       "      <td>185 (2.9)</td>\n",
       "      <td>95 (7.4)</td>\n",
       "      <td>498 (6.9)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>1680 (11.2)</td>\n",
       "      <td>621 (9.7)</td>\n",
       "      <td>195 (15.1)</td>\n",
       "      <td>864 (11.9)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td></td>\n",
       "      <td>11602 (77.6)</td>\n",
       "      <td>5398 (84.0)</td>\n",
       "      <td>904 (70.1)</td>\n",
       "      <td>5300 (73.2)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />"
      ],
      "text/plain": [
       "                               Grouped by region                                                            \n",
       "                                         Missing       Overall          chi         nyc           sf P-Value\n",
       "n                                                        14957         6430        1290         7237        \n",
       "customer order rating, n (%) 0                 0     154 (1.0)     50 (0.8)     8 (0.6)     96 (1.3)  <0.001\n",
       "                             1                       373 (2.5)     84 (1.3)    45 (3.5)    244 (3.4)        \n",
       "                             2                       370 (2.5)     92 (1.4)    43 (3.3)    235 (3.2)        \n",
       "                             3                       778 (5.2)    185 (2.9)    95 (7.4)    498 (6.9)        \n",
       "                             4                     1680 (11.2)    621 (9.7)  195 (15.1)   864 (11.9)        \n",
       "                             5                    11602 (77.6)  5398 (84.0)  904 (70.1)  5300 (73.2)        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby = ['region']\n",
    "stats_table = TableOne(df,columns = ['region', 'customer order rating'],\n",
    "                   groupby=groupby, pval=True, tukey_test=True)\n",
    "stats_table.to_excel('region and ratings.xlsx')\n",
    "display(stats_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c9f1fa",
   "metadata": {},
   "source": [
    "#### Explore number of items by hour of the day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "8e044c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LshI4HvgAsCzJYcAtwEEAVXVtkmXAdcB9wBFVdX/f1VvormCyOd1JkJ4IKUmSpI1ey6uOvGqCp/aaYP8lwJJx2pcDTx3i0CRJkqTmNpaTISVJkqRZxaAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUwDoF7SQPS7JVq8FIkiRJs8Vag3aSU5NslWRLuluk35Dkj9oPTZIkSZq5pjKjvWtV3QUcCJwD7Awc0nJQkiRJ0kw3laC9WZLN6IL2WVX1C6CajkqSJEma4aYStD8B3ARsCVyc5PHAXS0HJUmSJM10c9e2Q1WdAJww0HRzkhe0G5IkSZI08601aCfZGngtsHCN/Y9qMyRJkiRp5ltr0KY7AfLrwNXAA22HI0mSJM0OUwnaj6yqtzcfiSRJkjSLTOVkyM8meVOSHZNsO/bRfGSSJEnSDDaVGe2fAx8EjuPBy/oV8IRWg5IkSZJmuqkE7bcDT6qqH7YejCRJkjRbTGXpyLXAz1oPRJIkSZpNpjKjfT9wZZJ/Au4da6wqL+8nSZIkTWAqQfvL/YckSZKkKZrKnSGXJtkc2LmqbhjBmCRJkqQZb61rtJO8FLgS+Gr/+OlJzm48LkmSJGlGm8rJkO8Fdgd+AlBVVwK7NBuRJEmSNAtMJWjfV1X/vkZbjbunJEmSJGBqJ0Nek+S/A3OSLAKOAv617bAkSZKkmW0qQftIurtC3gucCnwNeH/LQUmSpIf6/TO/17T/01/uTZ+lYZpK0H5JVR1HF7YBSHIQ8IVmo5IkSZJmuKms0T52im2SJEmSehPOaCfZB9gX2CnJCQNPbQXc13pgkiRJ0kw22dKR24DlwP7A5QPtdwNvazkoSZIkaaabMGhX1VXAVUlOqSpnsCVJkqR1MNnSkWVV9Urgm0kect3sqnpa05FJkiRJM9hkS0eO7j/vN+yiSd4GvJHuxjdXA68HtgBOBxYCNwGvrKof9/sfCxwG3A8cVVVfG/aYJEmSpGGa8KojVbWq/3zzeB/rWzDJTnQ3vVlcVU8F5gAHA8cAF1TVIuCC/jFJdu2f3w3YG/hYkjnrW1+SJEkahalc3q+FucDmSebSzWTfBhwALO2fXwoc2G8fAJxWVfdW1Y3ACmD30Q5XkiRJWjcjD9pV9X3gL4BbgFXAv1fVecAOA7Poq4Dt+5fsBNw60MXKvu0hkhyeZHmS5atXr271FiRJkqS1mjBoJ7mg//xnwyyYZBu6WepdgMcBWyZ5zWQvGaftISdnAlTViVW1uKoWz5s3b8MHK0mSJK2nyU6G3DHJ84H9k5zGGoG3qq5Yz5q/A9xYVasBkpwJ/CZwe5Idq2pVkh2BO/r9VwILBl4/n26piSRJkrTRmixov4fuhMT5wIfXeK6AF65nzVuAPZJsAfwHsBfdjXHuAQ4FPtB/Pqvf/2zg1CQfppsBXwRctp61JUmSpJGY7IY1ZwBnJPmfVfX+YRWsqkuTnAFcQXcr928CJwKPApYlOYwujB/U739tkmXAdf3+R1TV/cMajyRJktTCZDPaAFTV+5PsD+zZN11YVV/ZkKJVdTxw/BrN99LNbo+3/xJgyYbUlCRJkkZprVcdSfKndDevua7/OLpvkyRJkjSBtc5oAy8Bnl5VDwAkWUq33OPYlgOTJEmSZrKpXkd764HtxzQYhyRJkjSrTGVG+0+Bbyb5J7pL/O2Js9mSJEnSpKZyMuTnk1wIPIsuaL+rqn7QemCSJEnSTDaVGe2xW6Kf3XgskiRJ0qwx1TXakiRJktaBQVuSJElqYNKgneRhSa4Z1WAkSZKk2WLSoN1fO/uqJDuPaDySJEnSrDCVkyF3BK5Nchlwz1hjVe3fbFSSJEnSDDeVoP2+5qOQJEmSZpmpXEf7oiSPBxZV1T8k2QKY035okiRJ0sy11quOJHkTcAbwib5pJ+DLDcckSZIkzXhTubzfEcBzgbsAquo7wPYtByVJkiTNdFMJ2vdW1c/HHiSZC1S7IUmSJEkz31ROhrwoyZ8Amyf5XeB/AH/XdliaKS79xH7N+n72H3ylWd+SJEmtTWVG+xhgNXA18AfAOcC7Ww5KkiRJmummctWRB5IsBS6lWzJyQ1W5dESSJEmaxFqDdpKXAH8DfBcIsEuSP6iqc1sPTpIkSZqpprJG+0PAC6pqBUCSJwJ/Dxi0JUmSpAlMZY32HWMhu/c94I5G45EkSZJmhQlntJO8vN+8Nsk5wDK6NdoHAd8YwdgkSZKkGWuypSMvHdi+HXh+v70a2KbZiCRJkqRZYMKgXVWvH+VAJEmSpNlkKlcd2QU4Elg4uH9V7d9uWJIkSdLMNpWrjnwZOInubpAPNB2NJEmSNEtMJWj/Z1Wd0HwkkiRJ0iwylaD9kSTHA+cB9441VtUVzUYlSZIkzXBTCdq/BhwCvJAHl45U/1iSJEnSOKYStF8GPKGqft56MJIkSdJsMZU7Q14FbN14HJIkSdKsMpUZ7R2Abyf5Br+8RtvL+0mSJEkTmErQPr75KCRJkqRZZq1Bu6ouGnbRJFsDnwSeSndi5RuAG4DT6W6McxPwyqr6cb//scBhwP3AUVX1tWGPSZIkSRqmta7RTnJ3krv6j/9Mcn+Suzaw7keAr1bVfwN+HbgeOAa4oKoWARf0j0myK3AwsBuwN/CxJHM2sL4kSZLU1FqDdlU9uqq26j8eCbwC+Oj6FkyyFbAn3d0mqaqfV9VPgAOApf1uS4ED++0DgNOq6t6quhFYAey+vvUlSZKkUZjKVUd+SVV9mQ27hvYTgNXAp5N8M8knk2wJ7FBVq/oaq4Dt+/13Am4deP3Kvk2SJEnaaK11jXaSlw88fBiwmG5d9YbUfAZwZFVdmuQj9MtEJhrCOG3j1k9yOHA4wM4777wBQ5QkSZI2zFSuOvLSge376E5UPGADaq4EVlbVpf3jM+iC9u1JdqyqVUl2BO4Y2H/BwOvnA7eN13FVnQicCLB48eIN+WVAkiRJ2iBTuerI64dZsKp+kOTWJE+pqhuAvYDr+o9DgQ/0n8/qX3I2cGqSDwOPAxYBlw1zTJIkSdKwTRi0k7xnktdVVb1/A+oeCZyS5OHA94DX0y1LWZbkMOAW4KC+0LVJltEF8fuAI6rq/g2oLUmSJDU32Yz2PeO0bUl3PetfAdY7aFfVlXRrvde01wT7LwGWrG89SZIkadQmDNpV9aGx7SSPBo6mm3k+DfjQRK+TJEmStJY12km2Bd4OvJru2tbPGLtboyRJkqSJTbZG+4PAy+mu4vFrVfXTkY1KkiRJmuEmu2HNO+iu8vFu4LaB27DfPYRbsEuSJEmz2mRrtNf5rpGSJEmSOoZpSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIamDvdA5AkaSZ6+Rcvadb3ma94TrO+JY2OM9qSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDcyd7gFI6+rck/Zt1vc+h53TrG9JkrRpmbYZ7SRzknwzyVf6x9smOT/Jd/rP2wzse2ySFUluSPLi6RqzJEmSNFXTuXTkaOD6gcfHABdU1SLggv4xSXYFDgZ2A/YGPpZkzojHKkmSJK2TaQnaSeYDLwE+OdB8ALC0314KHDjQflpV3VtVNwIrgN1HNFRJkiRpvUzXjPb/Bf4YeGCgbYeqWgXQf96+b98JuHVgv5V920MkOTzJ8iTLV69ePfRBS5IkSVM18qCdZD/gjqq6fKovGaetxtuxqk6sqsVVtXjevHnrPUZJkiRpQ03HVUeeC+yfZF/gkcBWST4H3J5kx6palWRH4I5+/5XAgoHXzwduG+mIJUmSpHU08hntqjq2quZX1UK6kxz/sapeA5wNHNrvdihwVr99NnBwkkck2QVYBFw24mFLkiRJ62Rjuo72B4BlSQ4DbgEOAqiqa5MsA64D7gOOqKr7p2+YkiRJ0tpNa9CuqguBC/vtHwF7TbDfEmDJyAYmSZIkbSBvwS5JkiQ1YNCWJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1MDc6R6AJEkb6oAzvtqs77N+b+9mfUua3ZzRliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgJf3k6bglJNf3KzvV7/ua836liRJ08cZbUmSJKkBg7YkSZLUgEFbkiRJamDkQTvJgiT/lOT6JNcmObpv3zbJ+Um+03/eZuA1xyZZkeSGJO0Wy0qSJElDMh0z2vcB76iqXwX2AI5IsitwDHBBVS0CLugf0z93MLAbsDfwsSRzpmHckiRJ0pSNPGhX1aqquqLfvhu4HtgJOABY2u+2FDiw3z4AOK2q7q2qG4EVwO4jHbQkSZK0jqZ1jXaShcBvAJcCO1TVKujCOLB9v9tOwK0DL1vZt43X3+FJlidZvnr16mbjliRJktZm2oJ2kkcBXwT+sKrummzXcdpqvB2r6sSqWlxVi+fNmzeMYUqSJEnrZVqCdpLN6EL2KVV1Zt98e5Id++d3BO7o21cCCwZePh+4bVRjlSRJktbHdFx1JMBJwPVV9eGBp84GDu23DwXOGmg/OMkjkuwCLAIuG9V4JUmSpPUxHbdgfy5wCHB1kiv7tj8BPgAsS3IYcAtwEEBVXZtkGXAd3RVLjqiq+0c+akmSJGkdjDxoV9U/M/66a4C9JnjNEmBJs0FJkiRJQ+adISVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNWDQliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJasCgLUmSJDVg0JYkSZIaMGhLkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5IkSWpg7nQPQJI0u+x3xhea9f2V3zuoWd+SNGzOaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBT4aUNlIf/dyLm/b/1td8rWn/kiRt6pzRliRJkhowaEuSJEkNGLQlSZKkBgzakiRJUgMGbUmSJKkBg7YkSZLUgEFbkiRJamDGBO0keye5IcmKJMdM93gkSZKkycyIoJ1kDvDXwD7ArsCrkuw6vaOSJEmSJjZT7gy5O7Ciqr4HkOQ04ADgumkdlTTL/M9lezft//2v/GrT/tfFvl86vlnf57zsfc36Xlf7ffHkZn1/5RWva9a3JM0GMyVo7wTcOvB4JfDsaRqLpCHb5+z9m/V97v5nN+t7Xb3kzBOa9f33Lz+qWd+SpPWTqpruMaxVkoOAF1fVG/vHhwC7V9WRa+x3OHB4//ApwA3rUW474IcbMNz1sanUnK661rSmNTfuuta0pjU33prTVXcm1Xx8Vc0b74mZMqO9Elgw8Hg+cNuaO1XVicCJG1IoyfKqWrwhfVhz46prTWtac+Oua01rWnPjrTlddWdLzRlxMiTwDWBRkl2SPBw4GNh4/h4sSZIkrWFGzGhX1X1J3gp8DZgDfKqqrp3mYUmSJEkTmhFBG6CqzgHOGUGpDVp6Ys2Nsq41rWnNjbuuNa1pzY235nTVnRU1Z8TJkJIkSdJMM1PWaEuSJEkzikF7wKhv857kU0nuSHJN61oDNRck+ack1ye5NsnRI6j5yCSXJbmqrzmyu3kkmZPkm0m+MqJ6NyW5OsmVSZaPqObWSc5I8u3+3/U5I6j5lP49jn3cleQPR1D3bf330DVJPp/kkSOoeXRf79pW73G8Y0GSg/qaDyQZ+pn3E9T8YP999K0kX0qy9Qhqvr+vd2WS85I8rnXNgefemaSSbDfMmhPVTfLeJN8f+H+zb+uaffuR/c+2a5P8eeuaSU4feI83JblyBDWfnuTrY8feJLuPoOavJ7mkP+b/XZKthlxz3J/XSbZNcn6S7/SftxlBzWbHo0lqNjseTVJz+MejqvKjWz4zB/gu8ATg4cBVwK6Na+4JPAO4ZoTvc0fgGf32o4F/G8H7DPCofnsz4FJgjxG937cDpwJfGVG9m4DtRvXv2ddcCryx3344sPWI688BfkB3HdGWdXYCbgQ27x8vA17XuOZTgWuALejOafkHYFGDOg85FgC/Snc/gAuBxSOq+SJgbr/9Z8CfjaDmVgPbRwF/07pm376A7gT7m1v8n53gvb4XeOewa62l5gv679tH9I+3H8XXd+D5DwHvGcH7PA/Yp9/eF7hwBDW/ATy/334D8P4h1xz35zXw58Axffsxw/x/OknNZsejSWo2Ox5NUnPoxyNntB/0X7d5r6qfA2O3eW+mqi4G7mxZY5yaq6rqin77buB6ugDTsmZV1U/7h5v1H81PDkgyH3gJ8MnWtaZLP4OyJ3ASQFX9vKp+MuJh7AV8t6puHkGtucDmSebShd+HXE9/yH4V+HpV/ayq7gMuAl427CLjHQuq6vqqWp+bbm1IzfP69wnwdbp7FrSuedfAwy0Z8rFhkuPsXwJ/POx6U6jbzAQ13wJ8oKru7fe5YwQ1AUgS4JXA50dQs4CxGeXHMORjwwQ1nwJc3G+fD7xiyDUn+nl9AN0EC/3nA1vXbHk8mqRms+PRJDWHfjwyaD9ovNu8Nw2g0y3JQuA36GaYW9ea0//58A7g/KpqXhP4v3Q/SB8YQa0xBZyX5PJ0dypt7QnAauDT6ZbIfDLJliOoO+hghvyDdDxV9X3gL4BbgFXAv1fVeY3LXgPsmeRXkmxBN1O2YC2vmS3eAJw7ikJJliS5FXg18J4R1Nsf+H5VXdW61jje2v9p+lPD/JP/JJ4M/FaSS5NclORZI6g55reA26vqOyOo9YfAB/vvo78Ajh1BzWuA/fvtg2h4bFjj5/UOVbUKusAIbD+CmiMxSc1mx6M1aw77eGTQflDGaZu1l2RJ8ijgi8AfrvEbXBNVdX9VPZ3uN9Ldkzy1Zb0k+wF3VNXlLeuM47lV9QxgH+CIJHs2rjeX7s+ZH6+q3wDuoftT4kiku4HU/sAXRlBrG7qZnF2AxwFbJnlNy5pVdT3dnyzPB75Kt6TsvklfNAskOY7ufZ4yinpVdVxVLejrvbVlrf4XpuMYQaAfx8eBJwJPp/tl8UMjqDkX2AbYA/gjYFk/0zwKr2IEv4T33gK8rf8+ehv9X/kaewPdcf5yuuUHP29RZNQ/rze2mi2PR+PVHPbxyKD9oCnd5n02SLIZ3TfWKVV15ihr98saLgT2blzqucD+SW6iWwb0wiSfa1yTqrqt/3wH8CW6JUktrQRWDvyF4Ay64D0q+wBXVNXtI6j1O8CNVbW6qn4BnAn8ZuuiVXVSVT2jqvak+9PxKGbnpk2SQ4H9gFdXv1BxhE5lyH9+H8cT6X5Zu6o/PswHrkjy2MZ1qarb+0mHB4C/pf3xAbpjxJn9Er7L6P7CN/STP9fUL+96OXB661q9Q+mOCdD94t/8a1tV366qF1XVM+l+ofjusGtM8PP69iQ79s/vSPeX4tY1m5qoZsvj0RTe51CORwbtB20St3nvZzJOAq6vqg+PqOa8sbOFk2xOF5i+3bJmVR1bVfOraiHdv+U/VlXT2c8kWyZ59Ng23YkcTa8oU1U/AG5N8pS+aS/gupY11zDKGatbgD2SbNF/H+9Ft66uqSTb9593pgsOo3q/I5dkb+BdwP5V9bMR1Vw08HB/2h8brq6q7atqYX98WEl3UtQPWtaF/wpFY15G4+ND78vAC/v6T6Y7YfqHI6j7O8C3q2rlCGpBNzH2/H77hYzgF+KBY8PDgHcDfzPk/if6eX023S8W9J/PGkHNZiaq2fJ4NEnN4R+PJjtTclP7oFt/+W90v5UeN4J6n6f78+Ev6A72h42g5vPolsR8C7iy/9i3cc2nAd/sa17DkM9An0L932YEVx2hWy99Vf9x7Si+h/q6TweW91/fLwPbjKjuFsCPgMeM8N/yff2B7xrgs/RXUmhc8//R/fJyFbBXoxoPORbQBbGVwL3A7cDXRlBzBd25KmPHhmFfAWS8ml/s/z2/Bfwd3QlJTWuu8fxNtLnqyHjv9bPA1f17PRvYcQQ1Hw58rv8aXwG8cBRfX+Bk4M3D/rpO8j6fB1ze/z+9FHjmCGoeTZcZ/g34AP1NAIdYc9yf18CvABfQ/TJxAbDtCGo2Ox5NUrPZ8WiSmkM/HnlnSEmSJKkBl45IkiRJDRi0JUmSpAYM2pIkSVIDBm1JkiSpAYO2JEmS1IBBW5JmuSQLk4zims2SpAEGbUnSOuvv/CdJmoRBW5I2DXOS/G2Sa5Ocl2TzJE9P8vUk30rypSTbACS5MMnifnu7/lblJHldki8k+TvgvOl7K5I0Mxi0JWnTsAj466raDfgJ8ArgM8C7quppdHcsPH4K/TwHOLSqXthqoJI0Wxi0JWnTcGNVXdlvXw48Edi6qi7q25YCe06hn/Or6s4G45OkWcegLUmbhnsHtu8Htp5k3/t48OfDI9d47p4hjkmSZjWDtiRtmv4d+HGS3+ofHwKMzW7fBDyz3/69EY9LkmYNzxqXpE3XocDfJNkC+B7w+r79L4BlSQ4B/nG6BidJM12qarrHIEmSJM06Lh2RJEmSGjBoS5IkSQ0YtCVJkqQGDNqSJElSAwZtSZIkqQGDtiRJktSAQVuSJElqwKAtSZIkNfD/AU2Z6FUweDQeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x=\"hour\",  data=df)\n",
    "plt.title('Number of items by hour')\n",
    "plt.ylabel('Number of items')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ede1ae76",
   "metadata": {},
   "source": [
    "##### Number of items per week of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "c0893c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x=\"week\",  data=df)\n",
    "plt.grid()\n",
    "plt.title('Number of items by week')\n",
    "plt.ylabel('Number of items')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24d82035",
   "metadata": {},
   "source": [
    "##### Number of items per day of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e1c83367",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x=\"day\",  data=df)\n",
    "plt.title('Number of items by day of the week')\n",
    "plt.ylabel('Number of items')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f98b32dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sunday       2588\n",
       "Monday       2381\n",
       "Saturday     2259\n",
       "Friday       2072\n",
       "Tuesday      2051\n",
       "Thursday     1877\n",
       "Wednesday    1729\n",
       "Name: day, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "399902b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19    3382\n",
       "22    3298\n",
       "20    3296\n",
       "21    3048\n",
       "18    1694\n",
       "23     239\n",
       "Name: week, dtype: Int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['week'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8405e759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = sns.countplot(x=\"month\",  data=df)\n",
    "plt.title('Number of items by month')\n",
    "plt.ylabel('Number of items')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a184b2db",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">Grouped by month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>P-Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>14957</td>\n",
       "      <td>14119</td>\n",
       "      <td>838</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">region, n (%)</th>\n",
       "      <th>chi</th>\n",
       "      <td>0</td>\n",
       "      <td>6430 (43.0)</td>\n",
       "      <td>6055 (42.9)</td>\n",
       "      <td>375 (44.7)</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nyc</th>\n",
       "      <td></td>\n",
       "      <td>1290 (8.6)</td>\n",
       "      <td>1246 (8.8)</td>\n",
       "      <td>44 (5.3)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sf</th>\n",
       "      <td></td>\n",
       "      <td>7237 (48.4)</td>\n",
       "      <td>6818 (48.3)</td>\n",
       "      <td>419 (50.0)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />"
      ],
      "text/plain": [
       "                  Grouped by month                                              \n",
       "                           Missing      Overall            5           6 P-Value\n",
       "n                                         14957        14119         838        \n",
       "region, n (%) chi                0  6430 (43.0)  6055 (42.9)  375 (44.7)   0.002\n",
       "              nyc                    1290 (8.6)   1246 (8.8)    44 (5.3)        \n",
       "              sf                    7237 (48.4)  6818 (48.3)  419 (50.0)        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby = ['month']\n",
    "stats_table = TableOne(df,columns = ['region', 'month'],\n",
    "                   groupby=groupby, pval=True, tukey_test=True)\n",
    "stats_table.to_excel('month and region.xlsx')\n",
    "display(stats_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f2fc07e",
   "metadata": {},
   "source": [
    "##### Region and issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bd319025",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Grouped by region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>chi</th>\n",
       "      <th>nyc</th>\n",
       "      <th>sf</th>\n",
       "      <th>P-Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>14957</td>\n",
       "      <td>6430</td>\n",
       "      <td>1290</td>\n",
       "      <td>7237</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">type of issue reported, n (%)</th>\n",
       "      <th>Damaged or spoiled</th>\n",
       "      <td>0</td>\n",
       "      <td>310 (2.1)</td>\n",
       "      <td>102 (1.6)</td>\n",
       "      <td>53 (4.1)</td>\n",
       "      <td>155 (2.1)</td>\n",
       "      <td>&lt;0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item charged incorrectly</th>\n",
       "      <td></td>\n",
       "      <td>21 (0.1)</td>\n",
       "      <td>13 (0.2)</td>\n",
       "      <td>3 (0.2)</td>\n",
       "      <td>5 (0.1)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item missing</th>\n",
       "      <td></td>\n",
       "      <td>178 (1.2)</td>\n",
       "      <td>49 (0.8)</td>\n",
       "      <td>10 (0.8)</td>\n",
       "      <td>119 (1.6)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No-Issue</th>\n",
       "      <td></td>\n",
       "      <td>13870 (92.7)</td>\n",
       "      <td>6158 (95.8)</td>\n",
       "      <td>1168 (90.5)</td>\n",
       "      <td>6544 (90.4)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other Order Issue</th>\n",
       "      <td></td>\n",
       "      <td>21 (0.1)</td>\n",
       "      <td>7 (0.1)</td>\n",
       "      <td>4 (0.3)</td>\n",
       "      <td>10 (0.1)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poor replacement</th>\n",
       "      <td></td>\n",
       "      <td>54 (0.4)</td>\n",
       "      <td>4 (0.1)</td>\n",
       "      <td>9 (0.7)</td>\n",
       "      <td>41 (0.6)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poor service</th>\n",
       "      <td></td>\n",
       "      <td>129 (0.9)</td>\n",
       "      <td>15 (0.2)</td>\n",
       "      <td>15 (1.2)</td>\n",
       "      <td>99 (1.4)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrong item</th>\n",
       "      <td></td>\n",
       "      <td>374 (2.5)</td>\n",
       "      <td>82 (1.3)</td>\n",
       "      <td>28 (2.2)</td>\n",
       "      <td>264 (3.6)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />[1] Chi-squared tests for the following variables may be invalid due to the low number of observations: type of issue reported.<br />"
      ],
      "text/plain": [
       "                                                       Grouped by region                                                             \n",
       "                                                                 Missing       Overall          chi          nyc           sf P-Value\n",
       "n                                                                                14957         6430         1290         7237        \n",
       "type of issue reported, n (%) Damaged or spoiled                       0     310 (2.1)    102 (1.6)     53 (4.1)    155 (2.1)  <0.001\n",
       "                              Item charged incorrectly                        21 (0.1)     13 (0.2)      3 (0.2)      5 (0.1)        \n",
       "                              Item missing                                   178 (1.2)     49 (0.8)     10 (0.8)    119 (1.6)        \n",
       "                              No-Issue                                    13870 (92.7)  6158 (95.8)  1168 (90.5)  6544 (90.4)        \n",
       "                              Other Order Issue                               21 (0.1)      7 (0.1)      4 (0.3)     10 (0.1)        \n",
       "                              Poor replacement                                54 (0.4)      4 (0.1)      9 (0.7)     41 (0.6)        \n",
       "                              Poor service                                   129 (0.9)     15 (0.2)     15 (1.2)     99 (1.4)        \n",
       "                              Wrong item                                     374 (2.5)     82 (1.3)     28 (2.2)    264 (3.6)        \n",
       "[1] Chi-squared tests for the following variables may be invalid due to the low number of observations: type of issue reported."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby = ['region']\n",
    "stats_table = TableOne(df,columns = ['region', 'type of issue reported'],\n",
    "                   groupby=groupby, pval=True, tukey_test=True)\n",
    "stats_table.to_excel('region and issues.xlsx')\n",
    "display(stats_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043d9544",
   "metadata": {},
   "source": [
    "##### Region and day of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7bea404d",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Grouped by region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Missing</th>\n",
       "      <th>Overall</th>\n",
       "      <th>chi</th>\n",
       "      <th>nyc</th>\n",
       "      <th>sf</th>\n",
       "      <th>P-Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <td></td>\n",
       "      <td>14957</td>\n",
       "      <td>6430</td>\n",
       "      <td>1290</td>\n",
       "      <td>7237</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">day, n (%)</th>\n",
       "      <th>Friday</th>\n",
       "      <td>0</td>\n",
       "      <td>2072 (13.9)</td>\n",
       "      <td>879 (13.7)</td>\n",
       "      <td>204 (15.8)</td>\n",
       "      <td>989 (13.7)</td>\n",
       "      <td>0.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td></td>\n",
       "      <td>2381 (15.9)</td>\n",
       "      <td>996 (15.5)</td>\n",
       "      <td>204 (15.8)</td>\n",
       "      <td>1181 (16.3)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td></td>\n",
       "      <td>2259 (15.1)</td>\n",
       "      <td>1028 (16.0)</td>\n",
       "      <td>188 (14.6)</td>\n",
       "      <td>1043 (14.4)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td></td>\n",
       "      <td>2588 (17.3)</td>\n",
       "      <td>1172 (18.2)</td>\n",
       "      <td>183 (14.2)</td>\n",
       "      <td>1233 (17.0)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td></td>\n",
       "      <td>1877 (12.5)</td>\n",
       "      <td>769 (12.0)</td>\n",
       "      <td>177 (13.7)</td>\n",
       "      <td>931 (12.9)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td></td>\n",
       "      <td>2051 (13.7)</td>\n",
       "      <td>882 (13.7)</td>\n",
       "      <td>172 (13.3)</td>\n",
       "      <td>997 (13.8)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td></td>\n",
       "      <td>1729 (11.6)</td>\n",
       "      <td>704 (10.9)</td>\n",
       "      <td>162 (12.6)</td>\n",
       "      <td>863 (11.9)</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br />"
      ],
      "text/plain": [
       "                     Grouped by region                                                           \n",
       "                               Missing      Overall          chi         nyc           sf P-Value\n",
       "n                                             14957         6430        1290         7237        \n",
       "day, n (%) Friday                    0  2072 (13.9)   879 (13.7)  204 (15.8)   989 (13.7)   0.003\n",
       "           Monday                       2381 (15.9)   996 (15.5)  204 (15.8)  1181 (16.3)        \n",
       "           Saturday                     2259 (15.1)  1028 (16.0)  188 (14.6)  1043 (14.4)        \n",
       "           Sunday                       2588 (17.3)  1172 (18.2)  183 (14.2)  1233 (17.0)        \n",
       "           Thursday                     1877 (12.5)   769 (12.0)  177 (13.7)   931 (12.9)        \n",
       "           Tuesday                      2051 (13.7)   882 (13.7)  172 (13.3)   997 (13.8)        \n",
       "           Wednesday                    1729 (11.6)   704 (10.9)  162 (12.6)   863 (11.9)        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby = ['region']\n",
    "stats_table = TableOne(df,columns = ['region', 'day'],\n",
    "                   groupby=groupby, pval=True, tukey_test=True)\n",
    "stats_table.to_excel('region and day.xlsx')\n",
    "display(stats_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4e02eb2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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