cleaning_data_NA_Fre.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Các bước tiền xử lý\n",
    "1. Loại bỏ cột, đổi tên\n",
    "2. Đồng nhất tên quận, huyện\n",
    "3. Xem quận/huyện nào tiêu thụ nhiều nhất (chọn top4)\n",
    "4. Vẽ biểu đồ tròn của tát cả thuốc trong top4\n",
    "5. Tạo pivot (cột là mã thuốc, hàng là thời gian, giá trị là lượng thuốc)\n",
    "6. Loại bỏ những thuốc có 0 lớn hơn 6 và outlier lớn hơn 2\n",
    "7. Xuất file data mới gồm 16 thuốc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Hiển thị full data frame:\n",
    "pd.set_option('display.max_columns', None) \n",
    "pd.set_option('display.max_rows', None) \n",
    "pd.set_option('display.max_colwidth', None) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('D:\\BME\\data\\drug.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Mã đơn hàng</th>\n",
       "      <th>Mã KH</th>\n",
       "      <th>Tên KH</th>\n",
       "      <th>Tỉnh</th>\n",
       "      <th>Quận/huyện</th>\n",
       "      <th>Xã/phường</th>\n",
       "      <th>Ngày</th>\n",
       "      <th>SDT</th>\n",
       "      <th>Tên nhân viên</th>\n",
       "      <th>Chi nhánh</th>\n",
       "      <th>Mã thuốc</th>\n",
       "      <th>Tên thuốc</th>\n",
       "      <th>Đơn vị</th>\n",
       "      <th>Số lượng</th>\n",
       "      <th>Đơn giá</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0c705e68-cbff-4c25-b860-881c51d7b291</td>\n",
       "      <td>N00146</td>\n",
       "      <td>Nhà thuốc Bệnh viện Nam Thăng Long</td>\n",
       "      <td>Thành phố Hà Nội</td>\n",
       "      <td>Quận Bắc Từ Liêm</td>\n",
       "      <td>Phường Xuân Đỉnh</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>983855018.0</td>\n",
       "      <td>Phạm Thu Trang</td>\n",
       "      <td>Chi nhánh Hà Nội</td>\n",
       "      <td>G00817</td>\n",
       "      <td>Golistin-enema for children</td>\n",
       "      <td>LO</td>\n",
       "      <td>50.0</td>\n",
       "      <td>26667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0c705e68-cbff-4c25-b860-881c51d7b291</td>\n",
       "      <td>N00146</td>\n",
       "      <td>Nhà thuốc Bệnh viện Nam Thăng Long</td>\n",
       "      <td>Thành phố Hà Nội</td>\n",
       "      <td>Quận Bắc Từ Liêm</td>\n",
       "      <td>Phường Xuân Đỉnh</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>983855018.0</td>\n",
       "      <td>Phạm Thu Trang</td>\n",
       "      <td>Chi nhánh Hà Nội</td>\n",
       "      <td>G00821</td>\n",
       "      <td>Golistin-enema</td>\n",
       "      <td>LO</td>\n",
       "      <td>30.0</td>\n",
       "      <td>35238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0c705e68-cbff-4c25-b860-881c51d7b291</td>\n",
       "      <td>N00146</td>\n",
       "      <td>Nhà thuốc Bệnh viện Nam Thăng Long</td>\n",
       "      <td>Thành phố Hà Nội</td>\n",
       "      <td>Quận Bắc Từ Liêm</td>\n",
       "      <td>Phường Xuân Đỉnh</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>983855018.0</td>\n",
       "      <td>Phạm Thu Trang</td>\n",
       "      <td>Chi nhánh Hà Nội</td>\n",
       "      <td>P01191</td>\n",
       "      <td>Phenhalal</td>\n",
       "      <td>ONG</td>\n",
       "      <td>200.0</td>\n",
       "      <td>3950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>196999A6-37EE-405C-8B9E-33E48A0455DC</td>\n",
       "      <td>P00641</td>\n",
       "      <td>Phòng khám đa khoa Hoàng Dũng - Quảng Trị</td>\n",
       "      <td>Tỉnh Quảng Trị</td>\n",
       "      <td>Thành phố Đông Hà</td>\n",
       "      <td>Phường 1</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>977519929.0</td>\n",
       "      <td>Lưu Hữu Công</td>\n",
       "      <td>Chi nhánh Đà Nẵng</td>\n",
       "      <td>Z00209</td>\n",
       "      <td>Zentokid - H10</td>\n",
       "      <td>ONG</td>\n",
       "      <td>400.0</td>\n",
       "      <td>6500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5a1d6b35-5755-44d7-ad79-14f60dd4f0ef</td>\n",
       "      <td>B00670</td>\n",
       "      <td>Bệnh Viện Đa Khoa Huyện Tuyên Hóa ( Nhà Thuốc)</td>\n",
       "      <td>Tỉnh Quảng Bình</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thị Trấn Đồng Lê -Huyện Tuyên Hoá -Tỉnh Quảng Bình</td>\n",
       "      <td>2018-10-04</td>\n",
       "      <td>911561101.0</td>\n",
       "      <td>Ngô Minh Anh Văn</td>\n",
       "      <td>Chi nhánh Đà Nẵng</td>\n",
       "      <td>H00852</td>\n",
       "      <td>Hemotocin</td>\n",
       "      <td>LO</td>\n",
       "      <td>10.0</td>\n",
       "      <td>235700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Mã đơn hàng   Mã KH  \\\n",
       "0  0c705e68-cbff-4c25-b860-881c51d7b291  N00146   \n",
       "1  0c705e68-cbff-4c25-b860-881c51d7b291  N00146   \n",
       "2  0c705e68-cbff-4c25-b860-881c51d7b291  N00146   \n",
       "3  196999A6-37EE-405C-8B9E-33E48A0455DC  P00641   \n",
       "4  5a1d6b35-5755-44d7-ad79-14f60dd4f0ef  B00670   \n",
       "\n",
       "                                           Tên KH              Tỉnh  \\\n",
       "0             Nhà thuốc Bệnh viện Nam Thăng Long   Thành phố Hà Nội   \n",
       "1             Nhà thuốc Bệnh viện Nam Thăng Long   Thành phố Hà Nội   \n",
       "2             Nhà thuốc Bệnh viện Nam Thăng Long   Thành phố Hà Nội   \n",
       "3       Phòng khám đa khoa Hoàng Dũng - Quảng Trị    Tỉnh Quảng Trị   \n",
       "4  Bệnh Viện Đa Khoa Huyện Tuyên Hóa ( Nhà Thuốc)   Tỉnh Quảng Bình   \n",
       "\n",
       "          Quận/huyện                                           Xã/phường  \\\n",
       "0   Quận Bắc Từ Liêm                                    Phường Xuân Đỉnh   \n",
       "1   Quận Bắc Từ Liêm                                    Phường Xuân Đỉnh   \n",
       "2   Quận Bắc Từ Liêm                                    Phường Xuân Đỉnh   \n",
       "3  Thành phố Đông Hà                                            Phường 1   \n",
       "4                NaN  Thị Trấn Đồng Lê -Huyện Tuyên Hoá -Tỉnh Quảng Bình   \n",
       "\n",
       "       Ngày           SDT     Tên nhân viên          Chi nhánh Mã thuốc  \\\n",
       "0 2018-10-04  983855018.0    Phạm Thu Trang   Chi nhánh Hà Nội   G00817   \n",
       "1 2018-10-04  983855018.0    Phạm Thu Trang   Chi nhánh Hà Nội   G00821   \n",
       "2 2018-10-04  983855018.0    Phạm Thu Trang   Chi nhánh Hà Nội   P01191   \n",
       "3 2018-10-04  977519929.0      Lưu Hữu Công  Chi nhánh Đà Nẵng   Z00209   \n",
       "4 2018-10-04  911561101.0  Ngô Minh Anh Văn  Chi nhánh Đà Nẵng   H00852   \n",
       "\n",
       "                     Tên thuốc Đơn vị  Số lượng   Đơn giá  \n",
       "0  Golistin-enema for children     LO      50.0   26667.0  \n",
       "1              Golistin-enema      LO      30.0   35238.0  \n",
       "2                   Phenhalal     ONG     200.0    3950.0  \n",
       "3               Zentokid - H10    ONG     400.0    6500.0  \n",
       "4                   Hemotocin      LO      10.0  235700.0  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x = data.copy()\n",
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Loại bỏ cột, dổi tên, lọc dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['Mã KH','Tên KH', 'Xã/phường', 'SDT', 'Tên nhân viên','Chi nhánh'], axis=1)\n",
    "\n",
    "data.columns = ['orderID', 'province', 'district', 'date', 'drugsID','drugsName', 'unit', 'order_numbers', 'unit_price']\n",
    "\n",
    "data = data[(data.province == 'Tỉnh Nghệ An') | (data.province == \"Nghệ An\")]\n",
    "data.province=data.province.replace(['Nghệ An', 'Tỉnh Nghệ An'], 'NA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data.copy()\n",
    "# data = data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA    31611\n",
       "Name: province, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.province.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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",
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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>orderID</th>\n",
       "      <th>province</th>\n",
       "      <th>district</th>\n",
       "      <th>date</th>\n",
       "      <th>drugsID</th>\n",
       "      <th>drugsName</th>\n",
       "      <th>unit</th>\n",
       "      <th>order_numbers</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>206ac048-29e8-4601-a9b3-d0a91b72d128</td>\n",
       "      <td>NA</td>\n",
       "      <td>TT Nam Đàn</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>G00898</td>\n",
       "      <td>Guacanyl - Hộp 4 vỉ x 5 ống 5ml</td>\n",
       "      <td>ONG</td>\n",
       "      <td>60.0</td>\n",
       "      <td>3800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>874F745A-568A-4CBE-8490-36B6B7284552</td>\n",
       "      <td>NA</td>\n",
       "      <td>Huyện Quỳnh Lưu</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>G00898</td>\n",
       "      <td>Guacanyl - Hộp 4 vỉ x 5 ống 5ml</td>\n",
       "      <td>ONG</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>09B56B0F-2C71-44E3-A83A-0244FB3C1DA4</td>\n",
       "      <td>NA</td>\n",
       "      <td>Diễn Châu</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>Zensalbu nebules 2.5</td>\n",
       "      <td>ONG</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>85655F04-A7E4-4218-87A5-497D1BA4399D</td>\n",
       "      <td>NA</td>\n",
       "      <td>Huyện Yên Thành</td>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>D01085</td>\n",
       "      <td>Domuvar - H20</td>\n",
       "      <td>ONG</td>\n",
       "      <td>600.0</td>\n",
       "      <td>2653.922459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>349B15D6-46CA-4229-ADE5-79B3B67C798F</td>\n",
       "      <td>NA</td>\n",
       "      <td>Thành phố Vinh</td>\n",
       "      <td>2018-10-22</td>\n",
       "      <td>Z00197</td>\n",
       "      <td>Zensalbu nebules 5.0</td>\n",
       "      <td>ONG</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  orderID province         district  \\\n",
       "54   206ac048-29e8-4601-a9b3-d0a91b72d128       NA       TT Nam Đàn   \n",
       "59   874F745A-568A-4CBE-8490-36B6B7284552       NA  Huyện Quỳnh Lưu   \n",
       "89   09B56B0F-2C71-44E3-A83A-0244FB3C1DA4       NA        Diễn Châu   \n",
       "112  85655F04-A7E4-4218-87A5-497D1BA4399D       NA  Huyện Yên Thành   \n",
       "124  349B15D6-46CA-4229-ADE5-79B3B67C798F       NA   Thành phố Vinh   \n",
       "\n",
       "          date drugsID                        drugsName unit  order_numbers  \\\n",
       "54  2018-10-17  G00898  Guacanyl - Hộp 4 vỉ x 5 ống 5ml  ONG           60.0   \n",
       "59  2018-10-17  G00898  Guacanyl - Hộp 4 vỉ x 5 ống 5ml  ONG          400.0   \n",
       "89  2018-10-19  Z00200             Zensalbu nebules 2.5  ONG           40.0   \n",
       "112 2018-10-22  D01085                    Domuvar - H20  ONG          600.0   \n",
       "124 2018-10-22  Z00197             Zensalbu nebules 5.0  ONG           20.0   \n",
       "\n",
       "      unit_price  \n",
       "54   3800.000000  \n",
       "59   2850.000000  \n",
       "89   4200.000000  \n",
       "112  2653.922459  \n",
       "124  8000.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Đồng nhất tên thành phố và huyện"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Thành phố Vinh                                                12903\n",
       "Huyện Quỳnh Lưu                                                3745\n",
       "Huyện Diễn Châu                                                3136\n",
       "Huyện Nghi Lộc                                                 1909\n",
       "Huyện Yên Thành                                                1119\n",
       "Thị xã Hoàng Mai                                                910\n",
       "Thị xã Thái Hòa                                                 886\n",
       "Huyện Nghĩa Đàn                                                 750\n",
       "Huyện Thanh Chương                                              640\n",
       "Huyện Đô Lương                                                  484\n",
       "Huyện Nam Đàn                                                   446\n",
       "Huyện Hưng Nguyên                                               398\n",
       "Thị xã Cửa Lò                                                   380\n",
       "Huyện Anh Sơn                                                   320\n",
       "Huyện Quỳ Hợp                                                   296\n",
       "Thị xã Thái Hoà                                                 246\n",
       "Huyện Tân Kỳ                                                    175\n",
       "Huyện Con Cuông                                                 161\n",
       "TP Vinh                                                         148\n",
       "tp vinh                                                         120\n",
       "quỳnh lưu                                                        94\n",
       "Quỳnh Lưu                                                        80\n",
       "Huyện Quỳ Châu                                                   73\n",
       "Huyện Quế Phong                                                  65\n",
       "tp Vinh                                                          44\n",
       "diễn chậ                                                         40\n",
       "nam đàn                                                          40\n",
       "Tp Vinh                                                          35\n",
       "TP vinh                                                          34\n",
       "xã sơn hải,huyện quỳnh lưu                                       21\n",
       "yên thành                                                        21\n",
       "hưng phúc                                                        21\n",
       "Nam Đàn                                                          20\n",
       "nghĩa đàn                                                        20\n",
       "Huyện Tương Dương                                                18\n",
       "quỳnh vinh, tx hoàng mai,nghệ an                                 18\n",
       "TP Vinh - Nghệ An                                                18\n",
       "Tp vinh                                                          17\n",
       "TX Cửa Lò                                                        17\n",
       "0913 386216                                                      16\n",
       "nghi phú, TP vinh                                                15\n",
       "Huyện Kỳ Sơn                                                     14\n",
       "diễn châu                                                        14\n",
       "phường Vinh Tân                                                  12\n",
       "quỳ hợp                                                          12\n",
       "Nghi Lộc                                                         12\n",
       "nghĩa hồng,nghĩa đàn                                             11\n",
       "quán bàu                                                         10\n",
       "nghi lộc                                                         10\n",
       "quỳnh Lưu                                                        10\n",
       "Thị Trấn Quỳ Hợp,Huyền Quỳ Hợp                                    9\n",
       "TT Nam Đàn                                                        8\n",
       "nghệ an                                                           7\n",
       "Quỳ Hợp                                                           7\n",
       "Trường Thi, tp Vinh                                               6\n",
       "TT cầu giát, huyện quỳnh lưu                                      6\n",
       "nghi liên                                                         6\n",
       "Xã Diễn Yên huyện Diễn Châu - Nghệ An                             6\n",
       "Hoàng Mai                                                         6\n",
       "phường hưng dũng                                                  6\n",
       "TP vinh                                                           6\n",
       "quỳnh văn quỳnh lưu ,nghệ an                                      5\n",
       "Khối Thinhh Mỹ Phường Quỳnh Thiện - TX Hoàng Mai - Nghệ An        5\n",
       "Quỳ Châu                                                          5\n",
       "TP VINH                                                           4\n",
       "tt thanh hà                                                       4\n",
       "Cửa Lò                                                            4\n",
       "Nghĩa Đàn                                                         3\n",
       "phường Hưng Phúc - Tp Vinh                                        3\n",
       "xã sơn hải, huyện quỳnh lưu                                       3\n",
       "hưng dũng, TP Vinh                                                3\n",
       "Thị Xã Cửa Lò                                                     3\n",
       "Diễn Châu                                                         3\n",
       "Nghĩa Xuân,Quỳ Hợp                                                3\n",
       "Tam Hợp,Quỳ Hợp                                                   3\n",
       "Trường Thi                                                        3\n",
       "quỳnh lưu ,nghệ an                                                3\n",
       "cửa hội                                                           3\n",
       "thị xã hoàng mai,nghệ an                                          3\n",
       "TP Vinh                                                           2\n",
       "Xóm 20 xã Nghi Phú, TP vinh                                       2\n",
       "hưng nguyên                                                       2\n",
       "Hưng Nguyên                                                       2\n",
       "hà huy tập                                                        2\n",
       "thị trấn yên thành                                                2\n",
       "Tp Vinh                                                           2\n",
       "Đô Lương                                                          2\n",
       "Huyện đô lương                                                    2\n",
       "0974383686                                                        2\n",
       "quỳnh thiện,Tx hoàng mai                                          2\n",
       "P. Lê Mao TP Vinh                                                 1\n",
       "Tương Dương                                                       1\n",
       "huyện hưng lộc_ tp vinh                                           1\n",
       "thị trấn Tân Kỳ                                                   1\n",
       "bệnh viện tây bắc                                                 1\n",
       "phường hưng bình _ tp vinh                                        1\n",
       "quỳnh vinh,tx hoàng mai,nghệ an                                   1\n",
       "TT Dùng, Thanh Chương                                             1\n",
       "khoa nội -bệnh viên tây bắc                                       1\n",
       "phường hưng bình                                                  1\n",
       "Thị trấn Đô Lương                                                 1\n",
       "Nghi Lộc                                                          1\n",
       "Huyện Đô lương_ Nghệ an                                           1\n",
       "thị trấn cầu giát , huyện quỳnh lưu                               1\n",
       "Đường lê viết thuật_ xã hưng lộc                                  1\n",
       "Name: district, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.district.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['district'] = data['district'].str.lower()\n",
    "data['district'] = data['district'].str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thànhphốvinh                                      12903\n",
       "huyệnquỳnhlưu                                      3745\n",
       "huyệndiễnchâu                                      3136\n",
       "huyệnnghilộc                                       1909\n",
       "huyệnyênthành                                      1119\n",
       "thịxãhoàngmai                                       910\n",
       "thịxãtháihòa                                        886\n",
       "huyệnnghĩađàn                                       750\n",
       "huyệnthanhchương                                    640\n",
       "huyệnđôlương                                        486\n",
       "huyệnnamđàn                                         446\n",
       "tpvinh                                              412\n",
       "huyệnhưngnguyên                                     398\n",
       "thịxãcửalò                                          383\n",
       "huyệnanhsơn                                         320\n",
       "huyệnquỳhợp                                         296\n",
       "thịxãtháihoà                                        246\n",
       "quỳnhlưu                                            184\n",
       "huyệntânkỳ                                          175\n",
       "huyệnconcuông                                       161\n",
       "huyệnquỳchâu                                         73\n",
       "huyệnquếphong                                        65\n",
       "namđàn                                               60\n",
       "diễnchậ                                              40\n",
       "xãsơnhải,huyệnquỳnhlưu                               24\n",
       "nghilộc                                              23\n",
       "nghĩađàn                                             23\n",
       "hưngphúc                                             21\n",
       "yênthành                                             21\n",
       "quỳnhvinh,txhoàngmai,nghệan                          19\n",
       "quỳhợp                                               19\n",
       "huyệntươngdương                                      18\n",
       "tpvinh-nghệan                                        18\n",
       "txcửalò                                              17\n",
       "diễnchâu                                             17\n",
       "0913386216                                           16\n",
       "nghiphú,tpvinh                                       15\n",
       "huyệnkỳsơn                                           14\n",
       "phườngvinhtân                                        12\n",
       "nghĩahồng,nghĩađàn                                   11\n",
       "quánbàu                                              10\n",
       "thịtrấnquỳhợp,huyềnquỳhợp                             9\n",
       "ttnamđàn                                              8\n",
       "nghệan                                                7\n",
       "trườngthi,tpvinh                                      6\n",
       "phườnghưngdũng                                        6\n",
       "nghiliên                                              6\n",
       "hoàngmai                                              6\n",
       "xãdiễnyênhuyệndiễnchâu-nghệan                         6\n",
       "ttcầugiát,huyệnquỳnhlưu                               6\n",
       "quỳchâu                                               5\n",
       "quỳnhvănquỳnhlưu,nghệan                               5\n",
       "khốithinhhmỹphườngquỳnhthiện-txhoàngmai-nghệan        5\n",
       "cửalò                                                 4\n",
       "ttthanhhà                                             4\n",
       "hưngnguyên                                            4\n",
       "tamhợp,quỳhợp                                         3\n",
       "trườngthi                                             3\n",
       "hưngdũng,tpvinh                                       3\n",
       "phườnghưngphúc-tpvinh                                 3\n",
       "thịxãhoàngmai,nghệan                                  3\n",
       "quỳnhlưu,nghệan                                       3\n",
       "cửahội                                                3\n",
       "nghĩaxuân,quỳhợp                                      3\n",
       "xóm20xãnghiphú,tpvinh                                 2\n",
       "hàhuytập                                              2\n",
       "đôlương                                               2\n",
       "0974383686                                            2\n",
       "thịtrấnyênthành                                       2\n",
       "quỳnhthiện,txhoàngmai                                 2\n",
       "đườnglêviếtthuật_xãhưnglộc                            1\n",
       "thịtrấnđôlương                                        1\n",
       "tươngdương                                            1\n",
       "huyệnhưnglộc_tpvinh                                   1\n",
       "phườnghưngbình_tpvinh                                 1\n",
       "p.lêmaotpvinh                                         1\n",
       "thịtrấncầugiát,huyệnquỳnhlưu                          1\n",
       "huyệnđôlương_nghệan                                   1\n",
       "bệnhviệntâybắc                                        1\n",
       "phườnghưngbình                                        1\n",
       "thịtrấntânkỳ                                          1\n",
       "khoanội-bệnhviêntâybắc                                1\n",
       "ttdùng,thanhchương                                    1\n",
       "Name: district, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.district.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.district=data.district.replace(['thànhphốvinh', 'tpvinh','Vinh', \n",
    "                                     'quỳnhvinh,txhoàngmai,nghệan ',\n",
    "                                     'tpvinh-nghệan', ' tpvinh-nghệan','trườngthi,tpvinh',\n",
    "                                     'hưngdũng,tpvinh','phườnghưngphúc-tpvinh',\n",
    "                                     'đườnglêviếtthuật_xãhưnglộc','phườnghưngbình_tpvinh','phườnghưngbình', 'huyệnhưnglộc_tpvinh'\n",
    "                                     ,'phườnghưngdũng', '0913386216','nghiphú,tpvinh','phườngvinhtân','quánbàu', 'nghệan', 'nghiliên','trườngthi'\n",
    "                                     ,'0974383686','xóm20xãnghiphú,tpvinh','khoanội-bệnhviêntâybắc','bệnhviệntâybắc',\n",
    "                                      'hàhuytập','p.lêmaotpvinh' ], \n",
    "                                     'vinh')\n",
    "\n",
    "data.district=data.district.replace(['huyệnquỳnhlưu', 'quỳnhlưu', \n",
    "                                     'xãsơnhải,huyệnquỳnhlưu', 'ttcầugiát,huyệnquỳnhlưu', \n",
    "                                     'quỳnhvănquỳnhlưu,nghệan', 'quỳnhlưu,nghệan','thịtrấncầugiát,huyệnquỳnhlưu'], \n",
    "                                     'quynh_luu')\n",
    "\n",
    "data.district=data.district.replace(['huyệndiễnchâu','diễnchậ','diễnchâu','xãdiễnyênhuyệndiễnchâu-nghệan'],'dien_chau')\n",
    "\n",
    "data.district=data.district.replace(['huyệnnghilộc','nghilộc'],'nghi_loc')\n",
    "\n",
    "data.district=data.district.replace(['huyệnyênthành','yênthành','thịtrấnyênthành'], 'yen_thanh')\n",
    "\n",
    "data.district=data.district.replace(['huyệnnghĩađàn','nghĩađàn','nghĩahồng,nghĩađàn'],'nghia_dan')\n",
    "\n",
    "data.district=data.district.replace(['huyệnthanhchương','ttdùng,thanhchương','ttthanhhà'], 'thanh_chuong')\n",
    "\n",
    "data.district=data.district.replace(['huyệnđôlương','đôlương','thịtrấnđôlương','huyệnđôlương_nghệan'],'do_luong')\n",
    "\n",
    "data.district=data.district.replace(['huyệnhưngnguyên','hưngphúc','hưngnguyên'], 'hung_nguyen')\n",
    "data.district=data.district.replace(['huyệnnamđàn','namđàn','ttnamđàn'],'nam_dan')\n",
    "data.district=data.district.replace(['huyệnanhsơn'],'anh_son')\n",
    "data.district=data.district.replace(['huyệnquỳhợp','quỳhợp','thịtrấnquỳhợp,huyềnquỳhợp','tamhợp,quỳhợp','nghĩaxuân,quỳhợp'], 'quy_hop')\n",
    "data.district=data.district.replace(['huyệntânkỳ','thịtrấntânkỳ'], 'tan_ky')\n",
    "data.district=data.district.replace(['huyệnconcuông'],'con_cuong')\n",
    "data.district=data.district.replace(['huyệnquỳchâu','quỳchâu'],'quy_chau')\n",
    "data.district=data.district.replace(['huyệnquếphong'],'que_phong')\n",
    "data.district=data.district.replace(['huyệntươngdương','tươngdương'],'tuong_duong')\n",
    "data.district=data.district.replace(['huyệnkỳsơn'],'ky_son')\n",
    "data.district=data.district.replace(['thịxãhoàngmai','quỳnhvinh,txhoàngmai,nghệan','hoàngmai','khốithinhhmỹphườngquỳnhthiện-txhoàngmai-nghệan','thịxãhoàngmai,nghệan','quỳnhthiện,txhoàngmai'],'tx_hoang_mai')\n",
    "data.district=data.district.replace(['thịxãtháihòa','thịxãtháihoà'],'tx_thai_hoa')\n",
    "data.district=data.district.replace(['thịxãcửalò','txcửalò','cửalò','cửahội'], 'tx_cua_lo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vinh            13433\n",
       "quynh_luu        3968\n",
       "dien_chau        3199\n",
       "nghi_loc         1932\n",
       "yen_thanh        1142\n",
       "tx_thai_hoa      1132\n",
       "tx_hoang_mai      945\n",
       "nghia_dan         784\n",
       "thanh_chuong      645\n",
       "nam_dan           514\n",
       "do_luong          490\n",
       "hung_nguyen       423\n",
       "tx_cua_lo         407\n",
       "quy_hop           330\n",
       "anh_son           320\n",
       "tan_ky            176\n",
       "con_cuong         161\n",
       "quy_chau           78\n",
       "que_phong          65\n",
       "tuong_duong        19\n",
       "ky_son             14\n",
       "Name: district, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.district.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.district = data.district.fillna('vinh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 31611 entries, 54 to 791532\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   orderID        31611 non-null  object        \n",
      " 1   province       31611 non-null  object        \n",
      " 2   district       31611 non-null  object        \n",
      " 3   date           31611 non-null  datetime64[ns]\n",
      " 4   drugsID        31611 non-null  object        \n",
      " 5   drugsName      31611 non-null  object        \n",
      " 6   unit           31611 non-null  object        \n",
      " 7   order_numbers  31611 non-null  float64       \n",
      " 8   unit_price     31611 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(2), object(6)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Xem quận/huyện nào tiêu thụ nhiều nhất (chọn top4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top = data['district'].value_counts()\n",
    "top = top.head(20)\n",
    "TOP = pd.DataFrame(top)\n",
    "TOP= TOP.reset_index()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='index', y='district', data=TOP)\n",
    "# adjust the x-label rotation\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "# add labels and title\n",
    "plt.xlabel('District')\n",
    "plt.ylabel('Population')\n",
    "plt.title('Population by City/district (Top20)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['quynh_luu', 'dien_chau', 'vinh', 'nghi_loc'], dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Get the value counts\n",
    "value_counts = data['district'].value_counts()\n",
    "# Step 2: Get the top 7 values\n",
    "top_values = value_counts.nlargest(4).index.tolist()\n",
    "\n",
    "# Step 3: Create a boolean mask for 7 locals\n",
    "mask = data['district'].isin(top_values)\n",
    "\n",
    "# Step 4: Select the rows that contain the top 7 values\n",
    "df_top4 = data.loc[mask]\n",
    "df_top4.district.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Vẽ biểu đồ tròn của tát cả thuốc trong top4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Index: 235 entries, P01146 to M01605\n",
      "Series name: drugsID\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "235 non-null    int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 3.7+ KB\n"
     ]
    }
   ],
   "source": [
    "drug_50 = df_top4['drugsID'].value_counts()\n",
    "drug_50.info()\n",
    "top_50drug = drug_50.nlargest(50).index.tolist()\n",
    "\n",
    "# Create a boolean mask for 50 drugID\n",
    "mask1 = df_top4['drugsID'].isin(top_50drug) \n",
    "\n",
    "df2 = df_top4.loc[mask1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N01041: 4131240.0\n",
      "N00845: 3069100.0\n",
      "L00726: 1274955.0\n",
      "Z00197: 392460.0\n",
      "K00511: 388020.0\n",
      "F00464: 332805.0\n",
      "Z00226: 227784.0\n",
      "C01842: 175430.0\n",
      "Z00219: 161485.0\n",
      "Z00200: 160765.0\n",
      "G00898: 116940.0\n",
      "A01237: 99260.0\n",
      "B01166: 96150.0\n",
      "N00864: 86075.0\n",
      "P01146: 62380.0\n",
      "P01481: 58881.0\n",
      "N00875: 57210.0\n",
      "B01373: 54711.0\n",
      "D01375: 44480.0\n",
      "D01304: 32120.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drug_sales = df2.groupby('drugsID')['order_numbers'].sum().reset_index()\n",
    "# drug_sales = drug_sales.sort_values(by='order_numbers', ascending=False)\n",
    "# plt.figure(figsize=(10, 10))\n",
    "# plt.pie(drug_sales['order_numbers'], labels=drug_sales['drugsID'], autopct='%1.1f%%', startangle=140)\n",
    "# plt.title(\"Drug Sales Percentage in Top 64 Provinces\")\n",
    "# plt.axis('equal')\n",
    "\n",
    "# # Create a new column 'sales_percentage' in the DataFrame\n",
    "total_sales = drug_sales['order_numbers'].sum()\n",
    "\n",
    "# Create a new column 'sales_percentage' in the DataFrame\n",
    "drug_sales['sales_percentage'] = (drug_sales['order_numbers'] / total_sales) * 100\n",
    "\n",
    "# Sort the DataFrame by 'sales_percentage' in descending order\n",
    "drug_sales = drug_sales.sort_values(by='sales_percentage', ascending=False)\n",
    "\n",
    "# Select the top 20 provinces\n",
    "top_20_drug = drug_sales.head(20)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.pie(drug_sales['order_numbers'], labels=drug_sales['drugsID'], autopct='%1.1f%%', startangle=140)\n",
    "plt.title(\"Drug Sales Percentage in Top 64 Provinces\")\n",
    "plt.axis('equal')\n",
    "\n",
    "# Print the top 20 provinces and their sales percentages\n",
    "for index, row in top_20_drug.iterrows():\n",
    "    province = row['drugsID']\n",
    "    sales = row['order_numbers']\n",
    "    print(f\"{province}: {sales}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Tạo pivot (cột là mã thuốc, hàng là thời gian, giá trị là lượng thuốc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>orderID</th>\n",
       "      <th>province</th>\n",
       "      <th>district</th>\n",
       "      <th>date</th>\n",
       "      <th>drugsID</th>\n",
       "      <th>drugsName</th>\n",
       "      <th>unit</th>\n",
       "      <th>order_numbers</th>\n",
       "      <th>unit_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>874F745A-568A-4CBE-8490-36B6B7284552</td>\n",
       "      <td>NA</td>\n",
       "      <td>quynh_luu</td>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>G00898</td>\n",
       "      <td>Guacanyl - Hộp 4 vỉ x 5 ống 5ml</td>\n",
       "      <td>ONG</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>09B56B0F-2C71-44E3-A83A-0244FB3C1DA4</td>\n",
       "      <td>NA</td>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>Zensalbu nebules 2.5</td>\n",
       "      <td>ONG</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 orderID province   district       date  \\\n",
       "59  874F745A-568A-4CBE-8490-36B6B7284552       NA  quynh_luu 2018-10-17   \n",
       "89  09B56B0F-2C71-44E3-A83A-0244FB3C1DA4       NA  dien_chau 2018-10-19   \n",
       "\n",
       "   drugsID                        drugsName unit  order_numbers  unit_price  \n",
       "59  G00898  Guacanyl - Hộp 4 vỉ x 5 ống 5ml  ONG          400.0      2850.0  \n",
       "89  Z00200             Zensalbu nebules 2.5  ONG           40.0      4200.0  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>district</th>\n",
       "      <th>date</th>\n",
       "      <th>drugsID</th>\n",
       "      <th>order_numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2018-10</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2018-11</td>\n",
       "      <td>G00898</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2018-12</td>\n",
       "      <td>G00898</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>K00511</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>L00726</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-01</td>\n",
       "      <td>Z00197</td>\n",
       "      <td>3150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>N00875</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>Z00197</td>\n",
       "      <td>2050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-02</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-03</td>\n",
       "      <td>G00898</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    district     date drugsID  order_numbers\n",
       "0  dien_chau  2018-10  Z00200           40.0\n",
       "1  dien_chau  2018-11  G00898          200.0\n",
       "2  dien_chau  2018-12  G00898          200.0\n",
       "3  dien_chau  2019-01  K00511          300.0\n",
       "4  dien_chau  2019-01  L00726        17000.0\n",
       "5  dien_chau  2019-01  Z00197         3150.0\n",
       "6  dien_chau  2019-02  N00875           30.0\n",
       "7  dien_chau  2019-02  Z00197         2050.0\n",
       "8  dien_chau  2019-02  Z00200          200.0\n",
       "9  dien_chau  2019-03  G00898          200.0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month = df2.groupby(['district',pd.to_datetime(df2['date']).dt.strftime('%Y-%m'), 'drugsID'])['order_numbers'].sum().reset_index()\n",
    "data_month.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vinh         1435\n",
       "quynh_luu     855\n",
       "nghi_loc      696\n",
       "dien_chau     655\n",
       "Name: district, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_month.district.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_month = pd.pivot_table(data_month, values='order_numbers', index='date', columns='drugsID', aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Loại bỏ những thuốc có 0 lớn hơn 6 và outlier lớn hơn 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\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>drugsID</th>\n",
       "      <th>A01237</th>\n",
       "      <th>A01303</th>\n",
       "      <th>B01166</th>\n",
       "      <th>C01842</th>\n",
       "      <th>F00464</th>\n",
       "      <th>G00869</th>\n",
       "      <th>G00898</th>\n",
       "      <th>H00852</th>\n",
       "      <th>M01090</th>\n",
       "      <th>M01225</th>\n",
       "      <th>N00845</th>\n",
       "      <th>N00864</th>\n",
       "      <th>Z00197</th>\n",
       "      <th>Z00200</th>\n",
       "      <th>Z00209</th>\n",
       "      <th>Z00219</th>\n",
       "      <th>Z00226</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.696370</td>\n",
       "      <td>4.459588</td>\n",
       "      <td>6.355709</td>\n",
       "      <td>7.002621</td>\n",
       "      <td>8.386173</td>\n",
       "      <td>5.534691</td>\n",
       "      <td>7.084896</td>\n",
       "      <td>4.631987</td>\n",
       "      <td>3.530273</td>\n",
       "      <td>4.364689</td>\n",
       "      <td>7.504229</td>\n",
       "      <td>6.334729</td>\n",
       "      <td>8.437083</td>\n",
       "      <td>7.487015</td>\n",
       "      <td>5.585759</td>\n",
       "      <td>7.069640</td>\n",
       "      <td>7.213744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.281386</td>\n",
       "      <td>2.321362</td>\n",
       "      <td>2.720652</td>\n",
       "      <td>2.726390</td>\n",
       "      <td>1.936540</td>\n",
       "      <td>1.513287</td>\n",
       "      <td>1.858736</td>\n",
       "      <td>1.746231</td>\n",
       "      <td>1.488790</td>\n",
       "      <td>1.792168</td>\n",
       "      <td>3.973718</td>\n",
       "      <td>2.661116</td>\n",
       "      <td>1.422201</td>\n",
       "      <td>1.603546</td>\n",
       "      <td>1.856392</td>\n",
       "      <td>2.076917</td>\n",
       "      <td>2.531229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.401197</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.564973</td>\n",
       "      <td>3.637586</td>\n",
       "      <td>6.586101</td>\n",
       "      <td>6.809387</td>\n",
       "      <td>8.665542</td>\n",
       "      <td>5.384799</td>\n",
       "      <td>6.756932</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>3.718325</td>\n",
       "      <td>4.834243</td>\n",
       "      <td>6.551080</td>\n",
       "      <td>7.807819</td>\n",
       "      <td>7.188647</td>\n",
       "      <td>5.504505</td>\n",
       "      <td>6.112380</td>\n",
       "      <td>6.783026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.965312</td>\n",
       "      <td>4.448362</td>\n",
       "      <td>7.098341</td>\n",
       "      <td>7.962061</td>\n",
       "      <td>8.956738</td>\n",
       "      <td>5.980054</td>\n",
       "      <td>7.517521</td>\n",
       "      <td>5.027030</td>\n",
       "      <td>3.583133</td>\n",
       "      <td>4.581624</td>\n",
       "      <td>7.534137</td>\n",
       "      <td>7.222326</td>\n",
       "      <td>8.744943</td>\n",
       "      <td>7.982758</td>\n",
       "      <td>6.076441</td>\n",
       "      <td>7.260886</td>\n",
       "      <td>7.843656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.030960</td>\n",
       "      <td>6.351349</td>\n",
       "      <td>8.048227</td>\n",
       "      <td>8.590423</td>\n",
       "      <td>9.188988</td>\n",
       "      <td>6.316713</td>\n",
       "      <td>8.069339</td>\n",
       "      <td>5.686975</td>\n",
       "      <td>4.838211</td>\n",
       "      <td>5.757838</td>\n",
       "      <td>10.875981</td>\n",
       "      <td>7.847670</td>\n",
       "      <td>9.365002</td>\n",
       "      <td>8.588886</td>\n",
       "      <td>6.613316</td>\n",
       "      <td>8.398341</td>\n",
       "      <td>8.812365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.220291</td>\n",
       "      <td>7.824046</td>\n",
       "      <td>8.884610</td>\n",
       "      <td>10.151519</td>\n",
       "      <td>9.667765</td>\n",
       "      <td>7.643962</td>\n",
       "      <td>9.409191</td>\n",
       "      <td>7.501082</td>\n",
       "      <td>5.351858</td>\n",
       "      <td>7.022868</td>\n",
       "      <td>13.625076</td>\n",
       "      <td>9.229162</td>\n",
       "      <td>10.661954</td>\n",
       "      <td>9.678467</td>\n",
       "      <td>8.480529</td>\n",
       "      <td>9.623112</td>\n",
       "      <td>10.036750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "drugsID     A01237     A01303     B01166     C01842     F00464     G00869  \\\n",
       "count    46.000000  46.000000  46.000000  46.000000  46.000000  46.000000   \n",
       "mean      6.696370   4.459588   6.355709   7.002621   8.386173   5.534691   \n",
       "std       2.281386   2.321362   2.720652   2.726390   1.936540   1.513287   \n",
       "min       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "25%       6.564973   3.637586   6.586101   6.809387   8.665542   5.384799   \n",
       "50%       6.965312   4.448362   7.098341   7.962061   8.956738   5.980054   \n",
       "75%       8.030960   6.351349   8.048227   8.590423   9.188988   6.316713   \n",
       "max       9.220291   7.824046   8.884610  10.151519   9.667765   7.643962   \n",
       "\n",
       "drugsID     G00898     H00852     M01090     M01225     N00845     N00864  \\\n",
       "count    46.000000  46.000000  46.000000  46.000000  46.000000  46.000000   \n",
       "mean      7.084896   4.631987   3.530273   4.364689   7.504229   6.334729   \n",
       "std       1.858736   1.746231   1.488790   1.792168   3.973718   2.661116   \n",
       "min       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "25%       6.756932   4.007333   2.995732   3.718325   4.834243   6.551080   \n",
       "50%       7.517521   5.027030   3.583133   4.581624   7.534137   7.222326   \n",
       "75%       8.069339   5.686975   4.838211   5.757838  10.875981   7.847670   \n",
       "max       9.409191   7.501082   5.351858   7.022868  13.625076   9.229162   \n",
       "\n",
       "drugsID     Z00197     Z00200     Z00209     Z00219     Z00226  \n",
       "count    46.000000  46.000000  46.000000  46.000000  46.000000  \n",
       "mean      8.437083   7.487015   5.585759   7.069640   7.213744  \n",
       "std       1.422201   1.603546   1.856392   2.076917   2.531229  \n",
       "min       3.401197   2.302585   0.000000   0.000000   0.000000  \n",
       "25%       7.807819   7.188647   5.504505   6.112380   6.783026  \n",
       "50%       8.744943   7.982758   6.076441   7.260886   7.843656  \n",
       "75%       9.365002   8.588886   6.613316   8.398341   8.812365  \n",
       "max      10.661954   9.678467   8.480529   9.623112  10.036750  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log transformed data\n",
    "data0_std = np.log(pivot_month)\n",
    "data0_std.describe()\n",
    "# fill NaN == 0\n",
    "data0_std = data0_std.fillna(0)\n",
    "\n",
    "# set threshold for number of zeros\n",
    "threshold = 6\n",
    "\n",
    "# drop columns with more than the threshold number of zeros\n",
    "data1_std = data0_std.loc[:, (data0_std == 0).sum() <= threshold]\n",
    "data1_std.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luong\\AppData\\Local\\Temp\\ipykernel_4596\\1335194123.py:8: FutureWarning: Automatic reindexing on DataFrame vs Series comparisons is deprecated and will raise ValueError in a future version. Do `left, right = left.align(right, axis=1, copy=False)` before e.g. `left == right`\n",
      "  outliers = ((data1_std < (Q1 - threshold * IQR)) | (data1_std > (Q3 + threshold * IQR))).sum()\n",
      "C:\\Users\\luong\\AppData\\Local\\Temp\\ipykernel_4596\\1335194123.py:8: FutureWarning: Automatic reindexing on DataFrame vs Series comparisons is deprecated and will raise ValueError in a future version. Do `left, right = left.align(right, axis=1, copy=False)` before e.g. `left == right`\n",
      "  outliers = ((data1_std < (Q1 - threshold * IQR)) | (data1_std > (Q3 + threshold * IQR))).sum()\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>drugsID</th>\n",
       "      <th>A01237</th>\n",
       "      <th>A01303</th>\n",
       "      <th>B01166</th>\n",
       "      <th>C01842</th>\n",
       "      <th>G00869</th>\n",
       "      <th>G00898</th>\n",
       "      <th>H00852</th>\n",
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       "      <th>M01225</th>\n",
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       "      <th>Z00219</th>\n",
       "      <th>Z00226</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.696370</td>\n",
       "      <td>4.459588</td>\n",
       "      <td>6.355709</td>\n",
       "      <td>7.002621</td>\n",
       "      <td>5.534691</td>\n",
       "      <td>7.084896</td>\n",
       "      <td>4.631987</td>\n",
       "      <td>3.530273</td>\n",
       "      <td>4.364689</td>\n",
       "      <td>7.504229</td>\n",
       "      <td>6.334729</td>\n",
       "      <td>8.437083</td>\n",
       "      <td>7.487015</td>\n",
       "      <td>5.585759</td>\n",
       "      <td>7.069640</td>\n",
       "      <td>7.213744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.281386</td>\n",
       "      <td>2.321362</td>\n",
       "      <td>2.720652</td>\n",
       "      <td>2.726390</td>\n",
       "      <td>1.513287</td>\n",
       "      <td>1.858736</td>\n",
       "      <td>1.746231</td>\n",
       "      <td>1.488790</td>\n",
       "      <td>1.792168</td>\n",
       "      <td>3.973718</td>\n",
       "      <td>2.661116</td>\n",
       "      <td>1.422201</td>\n",
       "      <td>1.603546</td>\n",
       "      <td>1.856392</td>\n",
       "      <td>2.076917</td>\n",
       "      <td>2.531229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.401197</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.564973</td>\n",
       "      <td>3.637586</td>\n",
       "      <td>6.586101</td>\n",
       "      <td>6.809387</td>\n",
       "      <td>5.384799</td>\n",
       "      <td>6.756932</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>3.718325</td>\n",
       "      <td>4.834243</td>\n",
       "      <td>6.551080</td>\n",
       "      <td>7.807819</td>\n",
       "      <td>7.188647</td>\n",
       "      <td>5.504505</td>\n",
       "      <td>6.112380</td>\n",
       "      <td>6.783026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.965312</td>\n",
       "      <td>4.448362</td>\n",
       "      <td>7.098341</td>\n",
       "      <td>7.962061</td>\n",
       "      <td>5.980054</td>\n",
       "      <td>7.517521</td>\n",
       "      <td>5.027030</td>\n",
       "      <td>3.583133</td>\n",
       "      <td>4.581624</td>\n",
       "      <td>7.534137</td>\n",
       "      <td>7.222326</td>\n",
       "      <td>8.744943</td>\n",
       "      <td>7.982758</td>\n",
       "      <td>6.076441</td>\n",
       "      <td>7.260886</td>\n",
       "      <td>7.843656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.030960</td>\n",
       "      <td>6.351349</td>\n",
       "      <td>8.048227</td>\n",
       "      <td>8.590423</td>\n",
       "      <td>6.316713</td>\n",
       "      <td>8.069339</td>\n",
       "      <td>5.686975</td>\n",
       "      <td>4.838211</td>\n",
       "      <td>5.757838</td>\n",
       "      <td>10.875981</td>\n",
       "      <td>7.847670</td>\n",
       "      <td>9.365002</td>\n",
       "      <td>8.588886</td>\n",
       "      <td>6.613316</td>\n",
       "      <td>8.398341</td>\n",
       "      <td>8.812365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.220291</td>\n",
       "      <td>7.824046</td>\n",
       "      <td>8.884610</td>\n",
       "      <td>10.151519</td>\n",
       "      <td>7.643962</td>\n",
       "      <td>9.409191</td>\n",
       "      <td>7.501082</td>\n",
       "      <td>5.351858</td>\n",
       "      <td>7.022868</td>\n",
       "      <td>13.625076</td>\n",
       "      <td>9.229162</td>\n",
       "      <td>10.661954</td>\n",
       "      <td>9.678467</td>\n",
       "      <td>8.480529</td>\n",
       "      <td>9.623112</td>\n",
       "      <td>10.036750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "drugsID     A01237     A01303     B01166     C01842     G00869     G00898  \\\n",
       "count    46.000000  46.000000  46.000000  46.000000  46.000000  46.000000   \n",
       "mean      6.696370   4.459588   6.355709   7.002621   5.534691   7.084896   \n",
       "std       2.281386   2.321362   2.720652   2.726390   1.513287   1.858736   \n",
       "min       0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "25%       6.564973   3.637586   6.586101   6.809387   5.384799   6.756932   \n",
       "50%       6.965312   4.448362   7.098341   7.962061   5.980054   7.517521   \n",
       "75%       8.030960   6.351349   8.048227   8.590423   6.316713   8.069339   \n",
       "max       9.220291   7.824046   8.884610  10.151519   7.643962   9.409191   \n",
       "\n",
       "drugsID     H00852     M01090     M01225     N00845     N00864     Z00197  \\\n",
       "count    46.000000  46.000000  46.000000  46.000000  46.000000  46.000000   \n",
       "mean      4.631987   3.530273   4.364689   7.504229   6.334729   8.437083   \n",
       "std       1.746231   1.488790   1.792168   3.973718   2.661116   1.422201   \n",
       "min       0.000000   0.000000   0.000000   0.000000   0.000000   3.401197   \n",
       "25%       4.007333   2.995732   3.718325   4.834243   6.551080   7.807819   \n",
       "50%       5.027030   3.583133   4.581624   7.534137   7.222326   8.744943   \n",
       "75%       5.686975   4.838211   5.757838  10.875981   7.847670   9.365002   \n",
       "max       7.501082   5.351858   7.022868  13.625076   9.229162  10.661954   \n",
       "\n",
       "drugsID     Z00200     Z00209     Z00219     Z00226  \n",
       "count    46.000000  46.000000  46.000000  46.000000  \n",
       "mean      7.487015   5.585759   7.069640   7.213744  \n",
       "std       1.603546   1.856392   2.076917   2.531229  \n",
       "min       2.302585   0.000000   0.000000   0.000000  \n",
       "25%       7.188647   5.504505   6.112380   6.783026  \n",
       "50%       7.982758   6.076441   7.260886   7.843656  \n",
       "75%       8.588886   6.613316   8.398341   8.812365  \n",
       "max       9.678467   8.480529   9.623112  10.036750  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine the criteria for identifying outliers, e.g., using IQR\n",
    "Q1 = pivot_month.quantile(0.25)\n",
    "Q3 = pivot_month.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "threshold = 1.5\n",
    "\n",
    "# Calculate the number of outliers in each column\n",
    "outliers = ((data1_std < (Q1 - threshold * IQR)) | (data1_std > (Q3 + threshold * IQR))).sum()\n",
    "outliers\n",
    "# Identify the columns that have more than 3 outliers\n",
    "columns_to_drop = []\n",
    "for col in outliers.index:\n",
    "    if outliers[col] > 2:\n",
    "        columns_to_drop.append(col)\n",
    "\n",
    "# Drop the identified columns\n",
    "data2_std = data1_std.drop(columns_to_drop, axis=1)\n",
    "data2_std.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Xuất file data mới gồm 16 thuốc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2_std.columns.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luong\\AppData\\Local\\Temp\\ipykernel_4596\\339301359.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_model.order_numbers = np.log(data_model.order_numbers)\n"
     ]
    }
   ],
   "source": [
    "column = data2_std.columns\n",
    "\n",
    "data_model = data_month[data_month.drugsID.isin(column)]\n",
    "data_model.order_numbers = np.log(data_model.order_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luong\\AppData\\Local\\Temp\\ipykernel_4596\\3427081541.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_model.rename(columns={\n"
     ]
    }
   ],
   "source": [
    "data_model.rename(columns={\n",
    "    'district': 'city',\n",
    "    'date': 'time_step',\n",
    "    'drugID': 'code',\n",
    "    'quantity':'amount'\n",
    "}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame is written to Excel File successfully.\n"
     ]
    }
   ],
   "source": [
    "file_name = 'data_4_paper.xlsx'\n",
    "  \n",
    "# saving the excel\n",
    "data_model.to_excel(file_name)\n",
    "print('DataFrame is written to Excel File successfully.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7.1 tạo file theo ngày"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>district</th>\n",
       "      <th>date</th>\n",
       "      <th>drugsID</th>\n",
       "      <th>order_numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2018-11-21</td>\n",
       "      <td>G00898</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2018-12-27</td>\n",
       "      <td>G00898</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-01-16</td>\n",
       "      <td>L00726</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>K00511</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>L00726</td>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>Z00197</td>\n",
       "      <td>3150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-02-20</td>\n",
       "      <td>Z00197</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dien_chau</td>\n",
       "      <td>2019-02-23</td>\n",
       "      <td>N00875</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    district        date drugsID  order_numbers\n",
       "0  dien_chau  2018-10-19  Z00200           40.0\n",
       "1  dien_chau  2018-11-21  G00898          200.0\n",
       "2  dien_chau  2018-12-27  G00898          200.0\n",
       "3  dien_chau  2019-01-16  L00726         5000.0\n",
       "4  dien_chau  2019-01-24  K00511          300.0\n",
       "5  dien_chau  2019-01-24  L00726        12000.0\n",
       "6  dien_chau  2019-01-24  Z00197         3150.0\n",
       "7  dien_chau  2019-02-11  Z00200          100.0\n",
       "8  dien_chau  2019-02-20  Z00197         1050.0\n",
       "9  dien_chau  2019-02-23  N00875           30.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_day = df2.groupby(['district',pd.to_datetime(df2['date']).dt.strftime('%Y-%m-%d'), 'drugsID'])['order_numbers'].sum().reset_index()\n",
    "data_day.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>date</th>\n",
       "      <th>district</th>\n",
       "      <th>drugsID</th>\n",
       "      <th>order_numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-10-17</td>\n",
       "      <td>quynh_luu</td>\n",
       "      <td>G00898</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10-18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>dien_chau</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10-20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   district drugsID  order_numbers\n",
       "0 2018-10-17  quynh_luu  G00898          400.0\n",
       "1 2018-10-18          0       0            0.0\n",
       "2 2018-10-19  dien_chau  Z00200           40.0\n",
       "3 2018-10-20          0       0            0.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_day['date'] = pd.to_datetime(data_day['date'])\n",
    "\n",
    "# Generate a full date range covering the entire period of interest\n",
    "full_date_range = pd.date_range(start=data_day['date'].min(), end=data_day['date'].max(), freq='D')\n",
    "full_date_df = pd.DataFrame({'date': full_date_range})\n",
    "merged_data = pd.merge(full_date_df, data_day, on='date', how='left')\n",
    "\n",
    "merged_data = merged_data.fillna(0)\n",
    "merged_data.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame is written to Excel File successfully.\n"
     ]
    }
   ],
   "source": [
    "file_name = 'data_4_paper_day.xlsx'\n",
    "  \n",
    "# saving the excel\n",
    "data_day.to_excel(file_name)\n",
    "print('DataFrame is written to Excel File successfully.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.5 chia nhóm (tạm dừng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivot_drug = pd.pivot_table(data_model, values='order_numbers', index='date', columns='drugsID', aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivot_drug = pivot_drug.fillna(0)\n",
    "# pivot_drug.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation_matrix = pivot_drug.corr()\n",
    "\n",
    "# plt.figure(figsize=(8, 6))  # Adjust the figure size if needed\n",
    "# sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "# plt.title(\"Correlation Heatmap\")\n",
    "# plt.show()"
   ]
  }
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