digikala.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from bidi.algorithm import get_display\n",
    "from arabic_reshaper import reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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",
       "      <th>YearMonth</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</td>\n",
       "      <td>597982.0</td>\n",
       "      <td>محمود آباد</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201510</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</td>\n",
       "      <td>980000.0</td>\n",
       "      <td>خرمدره</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201802</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</td>\n",
       "      <td>229358.0</td>\n",
       "      <td>قرچک</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201606</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</td>\n",
       "      <td>16514.0</td>\n",
       "      <td>قم</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201809</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</td>\n",
       "      <td>133028.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>12172771</td>\n",
       "      <td>4403268</td>\n",
       "      <td>445940</td>\n",
       "      <td>2018-04-03 12:07:01</td>\n",
       "      <td>344037.0</td>\n",
       "      <td>تهران</td>\n",
       "      <td>2.0</td>\n",
       "      <td>201804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199996</th>\n",
       "      <td>4425393</td>\n",
       "      <td>3745774</td>\n",
       "      <td>131645</td>\n",
       "      <td>2016-07-09 19:14:09</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>محمدیه</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>6671889</td>\n",
       "      <td>4845514</td>\n",
       "      <td>215045</td>\n",
       "      <td>2017-03-08 23:47:06</td>\n",
       "      <td>450000.0</td>\n",
       "      <td>اصفهان</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199998</th>\n",
       "      <td>6315995</td>\n",
       "      <td>2541418</td>\n",
       "      <td>264021</td>\n",
       "      <td>2017-02-08 14:59:39</td>\n",
       "      <td>193486.0</td>\n",
       "      <td>همدان</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199999</th>\n",
       "      <td>7584127</td>\n",
       "      <td>1342585</td>\n",
       "      <td>89427</td>\n",
       "      <td>2017-06-17 10:08:06</td>\n",
       "      <td>55046.0</td>\n",
       "      <td>بندر انزلی</td>\n",
       "      <td>1.0</td>\n",
       "      <td>201706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID_Order  ID_Customer  ID_Item DateTime_CartFinalize  \\\n",
       "0         2714054       469662    21386   2015-10-15 08:50:56   \n",
       "1        11104039      3063877   248497   2018-02-11 00:29:26   \n",
       "2         4228130      3184893    50144   2016-06-14 00:30:08   \n",
       "3        22225624      6888562    70208   2018-09-03 14:37:19   \n",
       "4         4068771      2533490    67627   2016-05-21 11:51:02   \n",
       "...           ...          ...      ...                   ...   \n",
       "199995   12172771      4403268   445940   2018-04-03 12:07:01   \n",
       "199996    4425393      3745774   131645   2016-07-09 19:14:09   \n",
       "199997    6671889      4845514   215045   2017-03-08 23:47:06   \n",
       "199998    6315995      2541418   264021   2017-02-08 14:59:39   \n",
       "199999    7584127      1342585    89427   2017-06-17 10:08:06   \n",
       "\n",
       "        Amount_Gross_Order city_name_fa  Quantity_item  YearMonth  \n",
       "0                 597982.0   محمود آباد            1.0     201510  \n",
       "1                 980000.0       خرمدره            1.0     201802  \n",
       "2                 229358.0         قرچک            1.0     201606  \n",
       "3                  16514.0           قم            1.0     201809  \n",
       "4                 133028.0        تهران            1.0     201605  \n",
       "...                    ...          ...            ...        ...  \n",
       "199995            344037.0        تهران            2.0     201804  \n",
       "199996            600000.0       محمدیه            1.0     201607  \n",
       "199997            450000.0       اصفهان            1.0     201703  \n",
       "199998            193486.0        همدان            1.0     201702  \n",
       "199999             55046.0   بندر انزلی            1.0     201706  \n",
       "\n",
       "[200000 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digikala = pd.read_csv(\"deta/digikala-orders.csv\")\n",
    "digikala[\"DateTime_CartFinalize\"] = pd.to_datetime(digikala[\"DateTime_CartFinalize\"])\n",
    "digikala[\"YearMonth\"] = digikala[\"DateTime_CartFinalize\"].map(lambda date: date.year*100 + date.month)\n",
    "digikala"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>201808</td>\n",
       "      <td>7944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>201809</td>\n",
       "      <td>8911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>201810</td>\n",
       "      <td>7311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>201811</td>\n",
       "      <td>7572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>201812</td>\n",
       "      <td>2856.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 2 columns</p>\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\n",
       "..        ...            ...\n",
       "59     201808         7944.0\n",
       "60     201809         8911.0\n",
       "61     201810         7311.0\n",
       "62     201811         7572.0\n",
       "63     201812         2856.0\n",
       "\n",
       "[64 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = digikala.groupby(\"YearMonth\")[\"Quantity_item\"].sum().reset_index()\n",
    "orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_axis = orders[\"YearMonth\"].to_numpy(dtype=str)\n",
    "y_axis = orders[\"Quantity_item\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 10))\n",
    "plt.plot(x_axis, y_axis, marker='o')\n",
    "plt.gcf().autofmt_xdate()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Number_of_Customers</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  Number_of_Customers\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = digikala.groupby(\"city_name_fa\")[\"ID_Customer\"].nunique().reset_index()\n",
    "customers = customers.rename(columns={\"ID_Customer\": \"Number_of_Customers\"})\n",
    "customers\n",
    "# customers = digikala.groupby(\"city_name_fa\")[\"ID_Customer\"].unique().reset_index()\n",
    "# customers[\"Number_of_Customers\"] = customers[\"ID_Customer\"].map(lambda customers_list: len(customers_list))\n",
    "# customers = customers.drop(\"ID_Customer\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Number_of_Customers</th>\n",
       "      <th>Customers Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>تهران</td>\n",
       "      <td>81048</td>\n",
       "      <td>52.728207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>کرج</td>\n",
       "      <td>4758</td>\n",
       "      <td>3.095460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>4674</td>\n",
       "      <td>3.040811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>4664</td>\n",
       "      <td>3.034305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اهواز</td>\n",
       "      <td>3353</td>\n",
       "      <td>2.181395</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>فرخی</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>اسیر</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>پیشین</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>چابهار</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>خاوران</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>906 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    city_name_fa  Number_of_Customers  Customers Ratio\n",
       "0          تهران                81048        52.728207\n",
       "1            کرج                 4758         3.095460\n",
       "2           مشهد                 4674         3.040811\n",
       "3         اصفهان                 4664         3.034305\n",
       "4          اهواز                 3353         2.181395\n",
       "..           ...                  ...              ...\n",
       "901         فرخی                    1         0.000651\n",
       "902         اسیر                    1         0.000651\n",
       "903        پیشین                    1         0.000651\n",
       "904       چابهار                    1         0.000651\n",
       "905       خاوران                    1         0.000651\n",
       "\n",
       "[906 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_customers = customers[\"Number_of_Customers\"].sum()\n",
    "customers[\"Customers Ratio\"] = (customers[\"Number_of_Customers\"] / all_customers) * 100\n",
    "# customers[\"Customers Ratio\"] = round(customers[\"Customers Ratio\"], 2)\n",
    "customers = customers.sort_values([\"Customers Ratio\"], axis=0, ascending=False).reset_index(drop=True)\n",
    "customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Number_of_Customers</th>\n",
       "      <th>Customers Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Others</td>\n",
       "      <td>47647</td>\n",
       "      <td>30.998185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  Number_of_Customers  Customers Ratio\n",
       "0       Others                47647        30.998185"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_city = pd.DataFrame({\"city_name_fa\" : \"Others\",\n",
    "                           \"Number_of_Customers\" : customers[\"Number_of_Customers\"][8:].sum(),\n",
    "                           \"Customers Ratio\" : customers[\"Customers Ratio\"][8:].sum()},\n",
    "                          index=[0])\n",
    "\n",
    "other_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_name_fa</th>\n",
       "      <th>Number_of_Customers</th>\n",
       "      <th>Customers Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>تهران</td>\n",
       "      <td>81048</td>\n",
       "      <td>52.728207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>کرج</td>\n",
       "      <td>4758</td>\n",
       "      <td>3.095460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>مشهد</td>\n",
       "      <td>4674</td>\n",
       "      <td>3.040811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>اصفهان</td>\n",
       "      <td>4664</td>\n",
       "      <td>3.034305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>اهواز</td>\n",
       "      <td>3353</td>\n",
       "      <td>2.181395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>شیراز</td>\n",
       "      <td>3076</td>\n",
       "      <td>2.001184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>رشت</td>\n",
       "      <td>2414</td>\n",
       "      <td>1.570500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>تبریز</td>\n",
       "      <td>2075</td>\n",
       "      <td>1.349953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Others</td>\n",
       "      <td>47647</td>\n",
       "      <td>30.998185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city_name_fa  Number_of_Customers  Customers Ratio\n",
       "0        تهران                81048        52.728207\n",
       "1          کرج                 4758         3.095460\n",
       "2         مشهد                 4674         3.040811\n",
       "3       اصفهان                 4664         3.034305\n",
       "4        اهواز                 3353         2.181395\n",
       "5        شیراز                 3076         2.001184\n",
       "6          رشت                 2414         1.570500\n",
       "7        تبریز                 2075         1.349953\n",
       "8       Others                47647        30.998185"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_city = pd.concat([customers[:8], other_city]).reset_index(drop=True)\n",
    "target_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = target_city[\"city_name_fa\"].values\n",
    "persian_labels = [get_display(reshape(label)) for label in labels]\n",
    "\n",
    "plt.figure(figsize=(10, 10), dpi=80)\n",
    "plt.pie(target_city[\"Number_of_Customers\"], labels=persian_labels, autopct='%1.1f%%', textprops={'fontsize': 8})\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "2e3d5a221850d49c091bfa422729480c31cc2214df6b2f2f1ac4431d31ee6b32"
  },
  "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}