06- Digikala.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a90fbbe6-137a-4198-b789-186293b23973",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf2ba8d4-7c8e-476d-9ce8-b62ed662cb20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID_Order</th>\n",
       "      <th>ID_Customer</th>\n",
       "      <th>ID_Item</th>\n",
       "      <th>DateTime_CartFinalize</th>\n",
       "      <th>Amount_Gross_Order</th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>Quantity_item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2714054</td>\n",
       "      <td>469662</td>\n",
       "      <td>21386</td>\n",
       "      <td>2015-10-15 08:50:56.000</td>\n",
       "      <td>597982.0</td>\n",
       "      <td>محمود آباد</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11104039</td>\n",
       "      <td>3063877</td>\n",
       "      <td>248497</td>\n",
       "      <td>2018-02-11 00:29:26.000</td>\n",
       "      <td>980000.0</td>\n",
       "      <td>خرمدره</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4228130</td>\n",
       "      <td>3184893</td>\n",
       "      <td>50144</td>\n",
       "      <td>2016-06-14 00:30:08.000</td>\n",
       "      <td>229358.0</td>\n",
       "      <td>قرچک</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22225624</td>\n",
       "      <td>6888562</td>\n",
       "      <td>70208</td>\n",
       "      <td>2018-09-03 14:37:19.000</td>\n",
       "      <td>16514.0</td>\n",
       "      <td>قم</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4068771</td>\n",
       "      <td>2533490</td>\n",
       "      <td>67627</td>\n",
       "      <td>2016-05-21 11:51:02.000</td>\n",
       "      <td>133028.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11479246</td>\n",
       "      <td>2841640</td>\n",
       "      <td>312932</td>\n",
       "      <td>2018-03-01 12:07:15.000</td>\n",
       "      <td>290000.0</td>\n",
       "      <td>مشهد</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8267421</td>\n",
       "      <td>5235886</td>\n",
       "      <td>42365</td>\n",
       "      <td>2017-08-19 13:30:24.000</td>\n",
       "      <td>55046.0</td>\n",
       "      <td>اصفهان</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11588978</td>\n",
       "      <td>5574955</td>\n",
       "      <td>715680</td>\n",
       "      <td>2018-03-04 19:19:02.000</td>\n",
       "      <td>2790000.0</td>\n",
       "      <td>رشت</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9916301</td>\n",
       "      <td>1145709</td>\n",
       "      <td>102588</td>\n",
       "      <td>2017-12-11 19:33:59.000</td>\n",
       "      <td>34862.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10740652</td>\n",
       "      <td>4928230</td>\n",
       "      <td>658519</td>\n",
       "      <td>2018-01-23 06:11:34.000</td>\n",
       "      <td>2600000.0</td>\n",
       "      <td>خرمشهر</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4605740</td>\n",
       "      <td>637880</td>\n",
       "      <td>138296</td>\n",
       "      <td>2016-08-02 10:48:18.000</td>\n",
       "      <td>469358.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5623610</td>\n",
       "      <td>2827566</td>\n",
       "      <td>23927</td>\n",
       "      <td>2016-12-04 03:12:59.000</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>لالی</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13469523</td>\n",
       "      <td>3819595</td>\n",
       "      <td>564993</td>\n",
       "      <td>2018-06-13 12:28:55.000</td>\n",
       "      <td>525000.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24233238</td>\n",
       "      <td>500391</td>\n",
       "      <td>1707686</td>\n",
       "      <td>2018-11-21 07:44:36.000</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4851441</td>\n",
       "      <td>3105577</td>\n",
       "      <td>183721</td>\n",
       "      <td>2016-08-31 13:27:44.000</td>\n",
       "      <td>23853.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7759076</td>\n",
       "      <td>3185842</td>\n",
       "      <td>376625</td>\n",
       "      <td>2017-07-05 09:37:45.000</td>\n",
       "      <td>708440.0</td>\n",
       "      <td>اصفهان</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23062506</td>\n",
       "      <td>7193109</td>\n",
       "      <td>1656648</td>\n",
       "      <td>2018-10-04 17:48:12.000</td>\n",
       "      <td>229000.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9934875</td>\n",
       "      <td>2876902</td>\n",
       "      <td>140331</td>\n",
       "      <td>2017-12-13 11:28:20.000</td>\n",
       "      <td>717798.0</td>\n",
       "      <td>تنکابن</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23693949</td>\n",
       "      <td>3665022</td>\n",
       "      <td>1671745</td>\n",
       "      <td>2018-10-30 22:15:15.000</td>\n",
       "      <td>73211.0</td>\n",
       "      <td>کرج</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2589206</td>\n",
       "      <td>1351072</td>\n",
       "      <td>36298</td>\n",
       "      <td>2015-09-20 09:25:01.000</td>\n",
       "      <td>523761.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID_Order  ID_Customer  ID_Item    DateTime_CartFinalize  \\\n",
       "0    2714054       469662    21386  2015-10-15 08:50:56.000   \n",
       "1   11104039      3063877   248497  2018-02-11 00:29:26.000   \n",
       "2    4228130      3184893    50144  2016-06-14 00:30:08.000   \n",
       "3   22225624      6888562    70208  2018-09-03 14:37:19.000   \n",
       "4    4068771      2533490    67627  2016-05-21 11:51:02.000   \n",
       "5   11479246      2841640   312932  2018-03-01 12:07:15.000   \n",
       "6    8267421      5235886    42365  2017-08-19 13:30:24.000   \n",
       "7   11588978      5574955   715680  2018-03-04 19:19:02.000   \n",
       "8    9916301      1145709   102588  2017-12-11 19:33:59.000   \n",
       "9   10740652      4928230   658519  2018-01-23 06:11:34.000   \n",
       "10   4605740       637880   138296  2016-08-02 10:48:18.000   \n",
       "11   5623610      2827566    23927  2016-12-04 03:12:59.000   \n",
       "12  13469523      3819595   564993  2018-06-13 12:28:55.000   \n",
       "13  24233238       500391  1707686  2018-11-21 07:44:36.000   \n",
       "14   4851441      3105577   183721  2016-08-31 13:27:44.000   \n",
       "15   7759076      3185842   376625  2017-07-05 09:37:45.000   \n",
       "16  23062506      7193109  1656648  2018-10-04 17:48:12.000   \n",
       "17   9934875      2876902   140331  2017-12-13 11:28:20.000   \n",
       "18  23693949      3665022  1671745  2018-10-30 22:15:15.000   \n",
       "19   2589206      1351072    36298  2015-09-20 09:25:01.000   \n",
       "\n",
       "    Amount_Gross_Order city_name_fa  Quantity_item  \n",
       "0             597982.0   محمود آباد            1.0  \n",
       "1             980000.0       خرمدره            1.0  \n",
       "2             229358.0         قرچک            1.0  \n",
       "3              16514.0           قم            1.0  \n",
       "4             133028.0        تهران            1.0  \n",
       "5             290000.0         مشهد            1.0  \n",
       "6              55046.0       اصفهان            1.0  \n",
       "7            2790000.0          رشت            1.0  \n",
       "8              34862.0        تهران            1.0  \n",
       "9            2600000.0       خرمشهر            1.0  \n",
       "10            469358.0        تهران            1.0  \n",
       "11            108000.0         لالی            1.0  \n",
       "12            525000.0        تهران            1.0  \n",
       "13             49000.0        تهران            1.0  \n",
       "14             23853.0        تهران            1.0  \n",
       "15            708440.0       اصفهان            1.0  \n",
       "16            229000.0        تهران            2.0  \n",
       "17            717798.0       تنکابن            1.0  \n",
       "18             73211.0          کرج            3.0  \n",
       "19            523761.0        تهران            1.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame = pd.read_csv('digikala-orders.csv')\n",
    "data_frame.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c920f41d-64ca-458e-ac77-476ed7a143e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame['DateTime_CartFinalize'] = pd.to_datetime(data_frame['DateTime_CartFinalize'])\n",
    "data_frame['YearMonth'] = data_frame['DateTime_CartFinalize'].map(lambda date : 100 * date.year + date.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35efa9fc-1e05-4367-bce3-f618ef09a804",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearMonth</th>\n",
       "      <th>Quantity_item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201309</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201310</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201311</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201312</td>\n",
       "      <td>125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201401</td>\n",
       "      <td>156.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YearMonth  Quantity_item\n",
       "0     201309            7.0\n",
       "1     201310           77.0\n",
       "2     201311          104.0\n",
       "3     201312          125.0\n",
       "4     201401          156.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_Quantity_item = data_frame.groupby(['YearMonth'])['Quantity_item'].sum().reset_index()\n",
    "data_Quantity_item.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75bf5e8b-0c40-4856-a086-2ee7150e60e5",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1728x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (24, 8))\n",
    "plt.plot(data_Quantity_item['YearMonth'].to_numpy(dtype = str), data_Quantity_item['Quantity_item'], marker = '.')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23fb50eb-e624-40fc-a55c-fcd7c021db04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>ID_Customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>آب بر</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>آبادان</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>آباده</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>آباده طشک</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>آبدان</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>گیوی</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>یاسوج</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>یاسوکند</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>یامچی</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>یزد</td>\n",
       "      <td>1626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>906 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    city_name_fa  ID_Customer\n",
       "0          آب بر           14\n",
       "1         آبادان          320\n",
       "2          آباده           48\n",
       "3      آباده طشک            3\n",
       "4          آبدان            2\n",
       "..           ...          ...\n",
       "901         گیوی           10\n",
       "902        یاسوج          229\n",
       "903      یاسوکند            1\n",
       "904        یامچی            1\n",
       "905          یزد         1626\n",
       "\n",
       "[906 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_city_name = data_frame.groupby('city_name_fa')['ID_Customer'].nunique().reset_index()\n",
    "data_city_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65ba9297-e5ad-453d-abce-e64fe7488ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>ID_Customer</th>\n",
       "      <th>Percent_City_Customers_to_AllCustomers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>آب بر</td>\n",
       "      <td>14</td>\n",
       "      <td>0.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>آبادان</td>\n",
       "      <td>320</td>\n",
       "      <td>0.002082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>آباده</td>\n",
       "      <td>48</td>\n",
       "      <td>0.000312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>آباده طشک</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>آبدان</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>گیوی</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>یاسوج</td>\n",
       "      <td>229</td>\n",
       "      <td>0.001490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>یاسوکند</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>یامچی</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>یزد</td>\n",
       "      <td>1626</td>\n",
       "      <td>0.010578</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>906 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    city_name_fa  ID_Customer  Percent_City_Customers_to_AllCustomers\n",
       "0          آب بر           14                                0.000091\n",
       "1         آبادان          320                                0.002082\n",
       "2          آباده           48                                0.000312\n",
       "3      آباده طشک            3                                0.000020\n",
       "4          آبدان            2                                0.000013\n",
       "..           ...          ...                                     ...\n",
       "901         گیوی           10                                0.000065\n",
       "902        یاسوج          229                                0.001490\n",
       "903      یاسوکند            1                                0.000007\n",
       "904        یامچی            1                                0.000007\n",
       "905          یزد         1626                                0.010578\n",
       "\n",
       "[906 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_city_name['Percent_City_Customers_to_AllCustomers'] = data_city_name['ID_Customer'] / data_city_name['ID_Customer'].sum()\n",
    "data_city_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "047ecf84-49e0-4b94-81b0-bf4a882decb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>ID_Customer</th>\n",
       "      <th>Percent_City_Customers_to_AllCustomers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>تهران</td>\n",
       "      <td>81048</td>\n",
       "      <td>0.527282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>کرج</td>\n",
       "      <td>4758</td>\n",
       "      <td>0.030955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>4674</td>\n",
       "      <td>0.030408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>4664</td>\n",
       "      <td>0.030343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>اهواز</td>\n",
       "      <td>3353</td>\n",
       "      <td>0.021814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>شیراز</td>\n",
       "      <td>3076</td>\n",
       "      <td>0.020012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>رشت</td>\n",
       "      <td>2414</td>\n",
       "      <td>0.015705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>2075</td>\n",
       "      <td>0.013500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    city_name_fa  ID_Customer  Percent_City_Customers_to_AllCustomers\n",
       "215        تهران        81048                                0.527282\n",
       "815          کرج         4758                                0.030955\n",
       "654         مشهد         4674                                0.030408\n",
       "76        اصفهان         4664                                0.030343\n",
       "97         اهواز         3353                                0.021814\n",
       "505        شیراز         3076                                0.020012\n",
       "355          رشت         2414                                0.015705\n",
       "202        تبریز         2075                                0.013500"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_data_city_name = data_city_name.sort_values(by=['ID_Customer'], ascending=False)\n",
    "top_city_costumers = sorted_data_city_name.head(8)\n",
    "top_city_costumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea28e79f-945e-472b-bd07-65dba0867ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>ID_Customer</th>\n",
       "      <th>Percent_City_Customers_to_AllCustomers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>تهران</td>\n",
       "      <td>81048</td>\n",
       "      <td>0.527282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>کرج</td>\n",
       "      <td>4758</td>\n",
       "      <td>0.030955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>4674</td>\n",
       "      <td>0.030408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>4664</td>\n",
       "      <td>0.030343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اهواز</td>\n",
       "      <td>3353</td>\n",
       "      <td>0.021814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>شیراز</td>\n",
       "      <td>3076</td>\n",
       "      <td>0.020012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>رشت</td>\n",
       "      <td>2414</td>\n",
       "      <td>0.015705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>2075</td>\n",
       "      <td>0.013500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Other</td>\n",
       "      <td>None</td>\n",
       "      <td>0.309982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa ID_Customer  Percent_City_Customers_to_AllCustomers\n",
       "0        تهران       81048                                0.527282\n",
       "1          کرج        4758                                0.030955\n",
       "2         مشهد        4674                                0.030408\n",
       "3       اصفهان        4664                                0.030343\n",
       "4        اهواز        3353                                0.021814\n",
       "5        شیراز        3076                                0.020012\n",
       "6          رشت        2414                                0.015705\n",
       "7        تبریز        2075                                0.013500\n",
       "8        Other        None                                0.309982"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Other_Percent_City_Customers = sorted_data_city_name.tail(sorted_data_city_name.shape[0] - 8)['Percent_City_Customers_to_AllCustomers'].sum()\n",
    "new_row = {'city_name_fa':'Other', 'ID_Customer':'None', 'Percent_City_Customers_to_AllCustomers':Other_Percent_City_Customers}\n",
    "top_city_costumers = top_city_costumers.append(new_row, ignore_index=True)\n",
    "top_city_costumers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2606bee3-2274-41af-aae1-ae690e4f5df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bidi.algorithm import get_display\n",
    "from arabic_reshaper import reshape\n",
    "\n",
    "labels = top_city_costumers['city_name_fa']\n",
    "labels = [get_display(reshape(label)) for label in labels]\n",
    "data = top_city_costumers['Percent_City_Customers_to_AllCustomers']\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize =(10, 10))\n",
    "ax1.pie(data, labels=labels, autopct='%1.1f%%')\n",
    "ax1.axis('equal')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}