cleaning_data_NA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('D:\\BME\\data\\drug.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x = data.copy()\n",
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loại bỏ cột, dổi tên, lọc dữ liệu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data.copy()\n",
    "# data = data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA    31611\n",
       "Name: province, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.province.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Đồng nhất tên thành phố và huyện"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.district.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['district'] = data['district'].str.lower()\n",
    "data['district'] = data['district'].str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.district.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.district.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.district = data.district.fillna('vinh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chia thời gian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.rename(columns={'date':'time'})\n",
    "data3 = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new columns for date, month, quarter, and year\n",
    "data['day'] = data['time'].dt.day\n",
    "data['month'] = data['time'].dt.month\n",
    "data['quarter'] = data['time'].dt.quarter\n",
    "data['year'] = data['time'].dt.year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 31611 entries, 54 to 791532\n",
      "Data columns (total 13 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   time           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",
      " 9   day            31611 non-null  int64         \n",
      " 10  month          31611 non-null  int64         \n",
      " 11  quarter        31611 non-null  int64         \n",
      " 12  year           31611 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(2), int64(4), object(6)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cate_cols = ['district', 'drugsID', 'unit']\n",
    "# for c in cate_cols:\n",
    "#     data[c] = data[c].astype('category')\n",
    "# data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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   time           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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kiểm tra tên thuốc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "240"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.drugsID.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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   time           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": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "uni_drugs = data.iloc[:,4:6]\n",
    "uni_drugs = uni_drugs.drop_duplicates() # a table to check name of drugs\n",
    "data = data.drop('drugsName', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "302"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(uni_drugs) # does not equal 234 because drugs have more than 1 name or are grammatically incorrect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_xx = data.copy()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tạo dữ liệu theo các khoảng thời  gian"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tạo dữ liệu theo tháng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>time</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</td>\n",
       "      <td>A01237</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>B01141</td>\n",
       "      <td>940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>D01063</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>D01085</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>G00817</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>G00821</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>G00898</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>M01076</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>N00875</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>O00583</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      time drugsID  order_numbers\n",
       "0  2018-10  A01237          200.0\n",
       "1  2018-10  B01141          940.0\n",
       "2  2018-10  D01063          400.0\n",
       "3  2018-10  D01085         1400.0\n",
       "4  2018-10  G00817           30.0\n",
       "5  2018-10  G00821           50.0\n",
       "6  2018-10  G00898          460.0\n",
       "7  2018-10  M01076           30.0\n",
       "8  2018-10  N00875          400.0\n",
       "9  2018-10  O00583          150.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month = data.groupby([pd.to_datetime(data['time']).dt.strftime('%Y-%m'), 'drugsID'])['order_numbers'].sum().reset_index()\n",
    "df_month.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min date from train set: 2018-10\n",
      "Max date from train set: 2022-07\n"
     ]
    }
   ],
   "source": [
    "print('Min date from train set: %s' % df_month['time'].min())\n",
    "print('Max date from train set: %s' % df_month['time'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>940.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7270.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>3850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3180.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53600.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>5550.0</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24500.0</td>\n",
       "      <td>8640.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7550.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6600.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22920.0</td>\n",
       "      <td>1270.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2520.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>5280.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>5080.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1180.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27700.0</td>\n",
       "      <td>2190.0</td>\n",
       "      <td>2060.0</td>\n",
       "      <td>4450.0</td>\n",
       "      <td>3790.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2640.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16600.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1430.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>4400.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>8600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20300.0</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>6650.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13800.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>14400.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3090.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11340.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>7920.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19250.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>2310.0</td>\n",
       "      <td>10020.0</td>\n",
       "      <td>2420.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-07</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>6580.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>4300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11520.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>609.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>5910.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>3320.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>2730.0</td>\n",
       "      <td>4150.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "drugsID  A01194  A01217  A01237  A01252  A01259  A01264  A01268  A01272  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0   200.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0   200.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0   600.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0   200.0     0.0     0.0     0.0   600.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0  4200.0     0.0   \n",
       "2019-03     0.0     0.0   700.0     0.0     0.0     0.0     0.0     5.0   \n",
       "2019-04     0.0     0.0  1200.0     0.0    30.0     0.0     0.0    15.0   \n",
       "2019-05     0.0     0.0  1400.0     0.0     0.0   400.0     0.0     5.0   \n",
       "2019-06     1.0     0.0  1600.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     0.0   800.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  A01303  A01338  A01341  A01348  A01379  A01401  A01408  A01411  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01   500.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03   200.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04  1000.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05   900.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06   600.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07  1110.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  A01414  A01500  B01113  B01141  B01163  B01166  B01178  B01235  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0   940.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0   300.0     0.0     0.0    10.0    60.0   \n",
       "2018-12     0.0     0.0     0.0  3200.0     0.0     0.0     5.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0    50.0     0.0   100.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0   560.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0   100.0  2040.0     0.0    30.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0  2520.0     0.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0   100.0  1020.0     0.0     0.0   \n",
       "2019-06     0.0     0.0     0.0     0.0   100.0  1560.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0   200.0   400.0    30.0     0.0   \n",
       "\n",
       "drugsID  B01244  B01247  B01304  B01308  B01332  B01338  B01347  B01373  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0    10.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0     0.0    40.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0   530.0     0.0   100.0     0.0     0.0     0.0   \n",
       "2019-05     0.0     0.0    20.0     0.0   600.0     0.0     0.0     0.0   \n",
       "2019-06     0.0     0.0    20.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07   200.0     0.0   150.0     0.0  1000.0    10.0     0.0     0.0   \n",
       "\n",
       "drugsID  B01414  B01463  B01472  B01514  B01570  B01585  B01586  C01829  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0   500.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0  1000.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0  1400.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0     0.0     0.0   200.0   \n",
       "2019-06     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0     0.0     0.0   800.0   \n",
       "\n",
       "drugsID   C01842  C01942  C02136  C02283  D01057  D01063  D01078   D01085  \\\n",
       "time                                                                        \n",
       "2018-10      0.0     0.0     0.0     0.0     0.0   400.0     0.0   1400.0   \n",
       "2018-11      0.0     0.0     0.0     0.0     0.0     0.0     0.0    600.0   \n",
       "2018-12      0.0     0.0     0.0     0.0     0.0  1100.0     0.0   1600.0   \n",
       "2019-01  10000.0     0.0     0.0     0.0   500.0     0.0     0.0    600.0   \n",
       "2019-02   5100.0     0.0     0.0     0.0  3100.0     0.0     0.0   2000.0   \n",
       "2019-03      0.0     0.0     0.0     0.0  6800.0     0.0     0.0   6600.0   \n",
       "2019-04   5280.0     0.0     0.0     0.0  1800.0     0.0     0.0   4800.0   \n",
       "2019-05   2640.0     0.0     0.0     0.0   800.0     0.0     0.0  16600.0   \n",
       "2019-06   4120.0     0.0     0.0     0.0   800.0    30.0     0.0  13800.0   \n",
       "2019-07   6580.0     0.0     0.0     0.0  1540.0    30.0    25.0   6000.0   \n",
       "\n",
       "drugsID  D01107  D01116  D01149  D01160  D01169  D01183  D01198  D01215  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01  3850.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0  1000.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03  1800.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04   900.0     0.0   800.0   300.0     0.0     0.0     0.0     0.0   \n",
       "2019-05  4000.0     0.0  1430.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06  1700.0     0.0   800.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07  4300.0     0.0   480.0   300.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  D01260  D01304  D01311  D01335  D01349  D01355  D01357  D01366  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12   600.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03   300.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05   300.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06   300.0   140.0    50.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0    20.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  D01375  D01425  D01449  D01502  D01510  D01519  D01522  D01533  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  D01599  D01643  E00471  F00440  F00443   F00464  F00472  F00482  \\\n",
       "time                                                                       \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0      0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0    800.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0    800.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0   400.0   1600.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0   600.0     0.0   2840.0   500.0     0.0   \n",
       "2019-03     0.0     0.0     0.0   600.0   600.0   2240.0  7500.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0   800.0   5080.0  4000.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0   200.0  10800.0     0.0     0.0   \n",
       "2019-06     0.0     0.0     0.0     0.0   800.0  14400.0   500.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0  11520.0   500.0     0.0   \n",
       "\n",
       "drugsID  F00490  F00507  F00512  F00550  F00598  F00609  G00817  G00821  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0    30.0    50.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0    65.0    50.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0    10.0   150.0   \n",
       "2019-01     0.0   400.0     0.0     0.0     0.0     0.0     0.0    50.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0    60.0    70.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0    40.0   110.0   \n",
       "2019-04     0.0  1120.0     0.0     0.0     0.0     0.0   330.0   274.0   \n",
       "2019-05     0.0  1200.0     0.0     0.0     0.0     0.0    34.0   100.0   \n",
       "2019-06     0.0   400.0     0.0     0.0     0.0     0.0    20.0    70.0   \n",
       "2019-07     0.0   600.0     0.0     0.0     0.0     0.0    30.0   290.0   \n",
       "\n",
       "drugsID  G00862  G00869  G00898  G00910  G00911  G00939  G00943  G00951  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0   460.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0   165.0   460.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12  2080.0    80.0   400.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01  3180.0    50.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02  1500.0   200.0    60.0   200.0   200.0     0.0     0.0     0.0   \n",
       "2019-03  2600.0   300.0   920.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04  1690.0   212.0  2300.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05  1890.0     0.0  1840.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06  3090.0   214.0  1840.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07   700.0   334.0   760.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  G00955  G00971  G00973  G00999  G01006  G01021  H00852  H00902  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0   180.0   500.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0   150.0   500.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0   210.0   900.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0   155.0   700.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0     0.0   150.0  1400.0   \n",
       "2019-06     0.0     0.0     0.0     0.0     0.0     0.0    30.0  1000.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0     0.0     0.0  1800.0   \n",
       "\n",
       "drugsID  H00905  H00920  H00979  H01031  H01036  H01039  H01068  H01121  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04   200.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05   200.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0     0.0     0.0   300.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0     0.0     0.0  3300.0   \n",
       "\n",
       "drugsID  H01210  H01224  H01251  H01913  H01921  H01924  H01930  H02087  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  H02098  H02101   K00511  K00628   L00726  L00750  L00763  L00784  \\\n",
       "time                                                                        \n",
       "2018-10     0.0     0.0      0.0     0.0      0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0      0.0     0.0      0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0      0.0     0.0      0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0  18900.0     0.0  53600.0  6000.0   200.0     0.0   \n",
       "2019-02     0.0     0.0   5400.0     0.0  24500.0  8640.0   300.0    20.0   \n",
       "2019-03     0.0     0.0   8700.0     0.0  45400.0     0.0   800.0     0.0   \n",
       "2019-04     0.0     0.0  28200.0     0.0  68300.0     0.0   700.0     0.0   \n",
       "2019-05     0.0     0.0   7500.0     0.0  34300.0     0.0     0.0     0.0   \n",
       "2019-06     0.0     0.0  11340.0     0.0  23900.0     0.0     0.0    10.0   \n",
       "2019-07     0.0     0.0   9100.0     0.0  60500.0     0.0   800.0    10.0   \n",
       "\n",
       "drugsID  L00793  L00801  L00822  L00837  L00847  L00854  L00877  L00905  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0    10.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0    20.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0   180.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03   400.0   392.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0   344.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05     0.0   520.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0   500.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0   609.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  L00908  L00993  L01005  L01012  L01021  L01026  L01038  L01052  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  L01053  L01057  L01058  M01045  M01070  M01076  M01090  M01096  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0    30.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0    30.0    30.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0    50.0     0.0    90.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0   200.0     5.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0   350.0    47.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0    80.0  1430.0    93.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0  1300.0    20.0     0.0   \n",
       "2019-06     0.0     0.0     0.0     0.0    30.0  1730.0    20.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0    20.0  1420.0    65.0     0.0   \n",
       "\n",
       "drugsID  M01215  M01225  M01327  M01331  M01476  M01596  M01605  M01656  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0    50.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     5.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0    55.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0   110.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05     0.0    40.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0    30.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0    45.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  N00520  N00826  N00845  N00864  N00875  N00922  N00928  N00941  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0   400.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0    60.0   650.0   \n",
       "2018-12     0.0   250.0     0.0   600.0    20.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0    50.0  3800.0   770.0     0.0     0.0     0.0   \n",
       "2019-02     0.0   100.0     0.0  2500.0    30.0     0.0     0.0     0.0   \n",
       "2019-03     0.0  1000.0     0.0     0.0  2100.0     0.0     0.0     0.0   \n",
       "2019-04     0.0  3100.0  2000.0   900.0  3760.0     0.0     0.0    75.0   \n",
       "2019-05     0.0   300.0  1200.0  1800.0  4400.0  1320.0     0.0    50.0   \n",
       "2019-06     0.0    50.0     0.0   600.0  4900.0   600.0     0.0   150.0   \n",
       "2019-07     0.0   500.0   500.0  1800.0  5910.0  1800.0     0.0     0.0   \n",
       "\n",
       "drugsID  N00979  N00994  N01007  N01010  N01013  N01041  N01080  N01084  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01   500.0     0.0     5.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0    15.0     0.0    10.0     0.0     0.0     0.0     0.0   \n",
       "2019-04   560.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05   500.0    10.0    10.0    10.0     0.0     0.0     0.0     0.0   \n",
       "2019-06   560.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     7.0     0.0    20.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  N01108  N01165  N01195  N01200  N01257  N01260  N01299  O00583  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0   150.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0   100.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0   100.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0   100.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0   150.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0   200.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0   150.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0     0.0     0.0   100.0   \n",
       "2019-06     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0     0.0     0.0   100.0   \n",
       "\n",
       "drugsID  P01083  P01087  P01146  P01191  P01260  P01275  P01278  P01291  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0   200.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0  1400.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0   100.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0  1600.0   840.0  1100.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0   800.0  1180.0  5200.0     0.0     0.0     0.0     0.0   \n",
       "2019-05     0.0  1000.0  1720.0  8600.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0   600.0  1700.0  7920.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0  1600.0  3320.0   800.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  P01299  P01336  P01349  P01364  P01394  P01411  P01452  P01475  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05   100.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06   100.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07   100.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  P01478  P01481  P01487  P01532  P01605  Q00087  Q00103  Q00108  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0   200.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0   500.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0  1350.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0  1350.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0  3100.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0  3500.0     0.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0  2900.0     0.0     0.0   \n",
       "2019-06     0.0     0.0     0.0     0.0     0.0  1350.0     0.0     0.0   \n",
       "2019-07     0.0     0.0     0.0     0.0     0.0  1900.0     0.0     0.0   \n",
       "\n",
       "drugsID  R00329  R00342  R00365  R00376  S01123  S01242  S01434  S01447  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0   200.0     0.0     0.0   900.0     0.0     0.0     0.0   \n",
       "2019-02     0.0   100.0     0.0     0.0   180.0     0.0     0.0     0.0   \n",
       "2019-03     0.0   200.0     0.0     0.0   300.0     0.0     0.0     0.0   \n",
       "2019-04     0.0   150.0     0.0     0.0   180.0     0.0     0.0     0.0   \n",
       "2019-05     0.0   110.0     0.0     0.0   900.0     0.0     0.0     0.0   \n",
       "2019-06     0.0   700.0     0.0     0.0  1200.0     0.0     0.0     0.0   \n",
       "2019-07     0.0   400.0     0.0     0.0  4260.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  T01684  T01701  T01711  T01855  T01864  T02071  T02722  U00134  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-03     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-04     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-05     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-06     0.0    11.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-07     0.0    62.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "drugsID  V01000  V01101  V01106  V01114   Z00197  Z00200  Z00209   Z00219  \\\n",
       "time                                                                        \n",
       "2018-10     0.0     0.0     0.0     0.0     30.0   350.0   300.0      0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0   2210.0   950.0  2100.0    100.0   \n",
       "2018-12     0.0     0.0     0.0     0.0   7270.0   520.0  1000.0    200.0   \n",
       "2019-01     0.0     0.0     0.0     0.0  12150.0  1010.0   500.0   5550.0   \n",
       "2019-02     0.0     0.0     0.0     0.0   7550.0   250.0  1020.0   2500.0   \n",
       "2019-03     0.0     0.0     0.0     0.0  22920.0  1270.0   850.0   1350.0   \n",
       "2019-04     0.0     0.0     0.0     0.0  27700.0  2190.0  2060.0   4450.0   \n",
       "2019-05     0.0     0.0     0.0     0.0  20300.0  1150.0  1540.0   6650.0   \n",
       "2019-06     0.0     0.0     0.0     0.0  19250.0  1480.0  2310.0  10020.0   \n",
       "2019-07     0.0     0.0     0.0     0.0   5700.0  1260.0  2730.0   4150.0   \n",
       "\n",
       "drugsID  Z00226  Z00230  Z00236  Z00241  Z00244  Z00253  Z00290  Z00294  \n",
       "time                                                                     \n",
       "2018-10     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "2018-12     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "2019-01  6250.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0  \n",
       "2019-02   700.0     0.0     0.0    10.0     0.0     0.0     0.0     0.0  \n",
       "2019-03  3300.0     0.0     0.0    20.0     0.0     0.0     0.0     0.0  \n",
       "2019-04  3790.0   200.0     0.0    20.0     0.0     0.0     0.0     0.0  \n",
       "2019-05  1550.0     0.0     0.0    30.0     0.0     0.0     0.0     0.0  \n",
       "2019-06  2420.0     0.0     0.0    20.0     0.0     0.0     0.0     0.0  \n",
       "2019-07  1360.0     0.0     0.0    30.0     0.0     0.0     0.0     0.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.pivot_table(df_month, values='order_numbers', index='time', columns='drugsID', aggfunc=np.sum)\n",
    "# df = pd.DataFrame(df\n",
    "df = df.reset_index()\n",
    "df = df.fillna(0)\n",
    "df.set_index('time', inplace=True)\n",
    "#true table (same excel)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "drugsID\n",
       "A01194    43\n",
       "A01217    42\n",
       "A01237     2\n",
       "A01252    44\n",
       "A01259    38\n",
       "A01264    37\n",
       "A01268    28\n",
       "A01272    21\n",
       "A01303     4\n",
       "A01338    16\n",
       "A01341    37\n",
       "A01348    30\n",
       "A01379    31\n",
       "A01401    28\n",
       "A01408    28\n",
       "A01411    27\n",
       "A01414    31\n",
       "A01500    33\n",
       "B01113    43\n",
       "B01141    43\n",
       "B01163    15\n",
       "B01166     6\n",
       "B01178    23\n",
       "B01235    13\n",
       "B01244    34\n",
       "B01247    29\n",
       "B01304     9\n",
       "B01308    26\n",
       "B01332    17\n",
       "B01338    22\n",
       "B01347    21\n",
       "B01373    10\n",
       "B01414    43\n",
       "B01463    33\n",
       "B01472    40\n",
       "B01514    36\n",
       "B01570    40\n",
       "B01585    41\n",
       "B01586    44\n",
       "C01829    11\n",
       "C01842     4\n",
       "C01942    34\n",
       "C02136    36\n",
       "C02283    44\n",
       "D01057     9\n",
       "D01063    24\n",
       "D01078    19\n",
       "D01085     5\n",
       "D01107    32\n",
       "D01116    45\n",
       "D01149    22\n",
       "D01160    41\n",
       "D01169    42\n",
       "D01183    36\n",
       "D01198    34\n",
       "D01215    29\n",
       "D01260    21\n",
       "D01304     9\n",
       "D01311    24\n",
       "D01335    19\n",
       "D01349    19\n",
       "D01355    22\n",
       "D01357    33\n",
       "D01366    36\n",
       "D01375    17\n",
       "D01425    28\n",
       "D01449    38\n",
       "D01502    43\n",
       "D01510    40\n",
       "D01519    33\n",
       "D01522    31\n",
       "D01533    30\n",
       "D01599    40\n",
       "D01643    45\n",
       "E00471    36\n",
       "F00440    44\n",
       "F00443    16\n",
       "F00464     1\n",
       "F00472    19\n",
       "F00482    16\n",
       "F00490    45\n",
       "F00507    17\n",
       "F00512    37\n",
       "F00550    21\n",
       "F00598    37\n",
       "F00609    38\n",
       "G00817     1\n",
       "G00821     0\n",
       "G00862    15\n",
       "G00869     2\n",
       "G00898     2\n",
       "G00910    45\n",
       "G00911    40\n",
       "G00939    37\n",
       "G00943    30\n",
       "G00951    22\n",
       "G00955    36\n",
       "G00971    40\n",
       "G00973    32\n",
       "G00999    34\n",
       "G01006    40\n",
       "G01021    38\n",
       "H00852     4\n",
       "H00902     9\n",
       "H00905    39\n",
       "H00920    38\n",
       "H00979    44\n",
       "H01031    45\n",
       "H01036    20\n",
       "H01039    40\n",
       "H01068    43\n",
       "H01121    14\n",
       "H01210    33\n",
       "H01224    44\n",
       "H01251    42\n",
       "H01913    37\n",
       "H01921    38\n",
       "H01924    39\n",
       "H01930    44\n",
       "H02087    43\n",
       "H02098    45\n",
       "H02101    45\n",
       "K00511    21\n",
       "K00628    44\n",
       "L00726    21\n",
       "L00750    28\n",
       "L00763    20\n",
       "L00784    31\n",
       "L00793    32\n",
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       "L00822    12\n",
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       "L00847    19\n",
       "L00854    16\n",
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       "L00905    25\n",
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       "L01012    34\n",
       "L01021    38\n",
       "L01026    41\n",
       "L01038    43\n",
       "L01052    45\n",
       "L01053    45\n",
       "L01057    45\n",
       "L01058    45\n",
       "M01045    39\n",
       "M01070    31\n",
       "M01076     4\n",
       "M01090     4\n",
       "M01096    30\n",
       "M01215    42\n",
       "M01225     4\n",
       "M01327    21\n",
       "M01331    21\n",
       "M01476    29\n",
       "M01596    45\n",
       "M01605    45\n",
       "M01656    40\n",
       "N00520    39\n",
       "N00826    23\n",
       "N00845     6\n",
       "N00864     3\n",
       "N00875    10\n",
       "N00922     8\n",
       "N00928    44\n",
       "N00941    21\n",
       "N00979    15\n",
       "N00994    29\n",
       "N01007    18\n",
       "N01010    32\n",
       "N01013    31\n",
       "N01041    20\n",
       "N01080    16\n",
       "N01084    28\n",
       "N01108    42\n",
       "N01165    27\n",
       "N01195    43\n",
       "N01200    32\n",
       "N01257    37\n",
       "N01260    41\n",
       "N01299    45\n",
       "O00583     3\n",
       "P01083    31\n",
       "P01087    28\n",
       "P01146    18\n",
       "P01191    11\n",
       "P01260    41\n",
       "P01275    23\n",
       "P01278    22\n",
       "P01291    45\n",
       "P01299     8\n",
       "P01336    20\n",
       "P01349    44\n",
       "P01364    18\n",
       "P01394    32\n",
       "P01411    32\n",
       "P01452    41\n",
       "P01475    40\n",
       "P01478    33\n",
       "P01481    29\n",
       "P01487    37\n",
       "P01532    42\n",
       "P01605    44\n",
       "Q00087    13\n",
       "Q00103    44\n",
       "Q00108    45\n",
       "R00329    43\n",
       "R00342    18\n",
       "R00365    22\n",
       "R00376    30\n",
       "S01123    17\n",
       "S01242    42\n",
       "S01434    44\n",
       "S01447    36\n",
       "T01684    38\n",
       "T01701    10\n",
       "T01711    31\n",
       "T01855    20\n",
       "T01864    22\n",
       "T02071    21\n",
       "T02722    42\n",
       "U00134    36\n",
       "V01000    23\n",
       "V01101    41\n",
       "V01106    44\n",
       "V01114    45\n",
       "Z00197     0\n",
       "Z00200     0\n",
       "Z00209     0\n",
       "Z00219     1\n",
       "Z00226     3\n",
       "Z00230    21\n",
       "Z00236    42\n",
       "Z00241     8\n",
       "Z00244    34\n",
       "Z00253    27\n",
       "Z00290    40\n",
       "Z00294    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_counts = (df == 0).sum(axis=0)\n",
    "zero_counts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loại bỏ các thuốc có 0 > 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>drugsID</th>\n",
       "      <th>A01237</th>\n",
       "      <th>A01303</th>\n",
       "      <th>B01163</th>\n",
       "      <th>B01166</th>\n",
       "      <th>B01235</th>\n",
       "      <th>B01304</th>\n",
       "      <th>B01373</th>\n",
       "      <th>C01829</th>\n",
       "      <th>C01842</th>\n",
       "      <th>D01057</th>\n",
       "      <th>D01085</th>\n",
       "      <th>D01304</th>\n",
       "      <th>F00464</th>\n",
       "      <th>G00817</th>\n",
       "      <th>G00821</th>\n",
       "      <th>G00862</th>\n",
       "      <th>G00869</th>\n",
       "      <th>G00898</th>\n",
       "      <th>H00852</th>\n",
       "      <th>H00902</th>\n",
       "      <th>H01121</th>\n",
       "      <th>L00822</th>\n",
       "      <th>M01076</th>\n",
       "      <th>M01090</th>\n",
       "      <th>M01225</th>\n",
       "      <th>N00845</th>\n",
       "      <th>N00864</th>\n",
       "      <th>N00875</th>\n",
       "      <th>N00922</th>\n",
       "      <th>N00979</th>\n",
       "      <th>O00583</th>\n",
       "      <th>P01191</th>\n",
       "      <th>P01299</th>\n",
       "      <th>Q00087</th>\n",
       "      <th>T01701</th>\n",
       "      <th>Z00197</th>\n",
       "      <th>Z00200</th>\n",
       "      <th>Z00209</th>\n",
       "      <th>Z00219</th>\n",
       "      <th>Z00226</th>\n",
       "      <th>Z00241</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>46.00000</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",
       "      <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",
       "      <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>2228.26087</td>\n",
       "      <td>422.478261</td>\n",
       "      <td>156.304348</td>\n",
       "      <td>2791.086957</td>\n",
       "      <td>1075.217391</td>\n",
       "      <td>114.304348</td>\n",
       "      <td>1625.673913</td>\n",
       "      <td>1389.565217</td>\n",
       "      <td>4688.695652</td>\n",
       "      <td>2496.521739</td>\n",
       "      <td>4009.565217</td>\n",
       "      <td>816.086957</td>\n",
       "      <td>8343.478261</td>\n",
       "      <td>154.173913</td>\n",
       "      <td>424.130435</td>\n",
       "      <td>1096.782609</td>\n",
       "      <td>498.369565</td>\n",
       "      <td>3334.782609</td>\n",
       "      <td>276.739130</td>\n",
       "      <td>1641.304348</td>\n",
       "      <td>1018.478261</td>\n",
       "      <td>160.043478</td>\n",
       "      <td>473.826087</td>\n",
       "      <td>90.478261</td>\n",
       "      <td>273.152174</td>\n",
       "      <td>92063.717391</td>\n",
       "      <td>2294.130435</td>\n",
       "      <td>1493.478261</td>\n",
       "      <td>1396.739130</td>\n",
       "      <td>635.000000</td>\n",
       "      <td>140.869565</td>\n",
       "      <td>2400.000000</td>\n",
       "      <td>695.217391</td>\n",
       "      <td>716.956522</td>\n",
       "      <td>48.260870</td>\n",
       "      <td>14253.695652</td>\n",
       "      <td>4652.282609</td>\n",
       "      <td>1314.826087</td>\n",
       "      <td>5866.108696</td>\n",
       "      <td>6027.934783</td>\n",
       "      <td>21.543478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2359.12065</td>\n",
       "      <td>581.139369</td>\n",
       "      <td>184.744372</td>\n",
       "      <td>2399.603882</td>\n",
       "      <td>1760.733104</td>\n",
       "      <td>125.607125</td>\n",
       "      <td>1857.911851</td>\n",
       "      <td>1520.103734</td>\n",
       "      <td>4598.066071</td>\n",
       "      <td>3202.005772</td>\n",
       "      <td>5161.491153</td>\n",
       "      <td>906.878598</td>\n",
       "      <td>3896.018462</td>\n",
       "      <td>137.072617</td>\n",
       "      <td>397.986354</td>\n",
       "      <td>1393.879062</td>\n",
       "      <td>376.309941</td>\n",
       "      <td>3521.780950</td>\n",
       "      <td>318.048245</td>\n",
       "      <td>1717.823699</td>\n",
       "      <td>1024.261181</td>\n",
       "      <td>178.880712</td>\n",
       "      <td>653.418338</td>\n",
       "      <td>93.824124</td>\n",
       "      <td>298.302082</td>\n",
       "      <td>187411.012397</td>\n",
       "      <td>2028.251383</td>\n",
       "      <td>1906.577174</td>\n",
       "      <td>1218.554799</td>\n",
       "      <td>810.620476</td>\n",
       "      <td>71.982284</td>\n",
       "      <td>2919.549433</td>\n",
       "      <td>826.561120</td>\n",
       "      <td>1010.741465</td>\n",
       "      <td>80.393873</td>\n",
       "      <td>11117.449020</td>\n",
       "      <td>3747.073481</td>\n",
       "      <td>932.136025</td>\n",
       "      <td>7655.104433</td>\n",
       "      <td>7157.890525</td>\n",
       "      <td>20.468628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</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>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>30.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>260.000000</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>710.00000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>810.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.250000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>425.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>6050.000000</td>\n",
       "      <td>65.750000</td>\n",
       "      <td>184.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>248.500000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>92.500000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>16.250000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>45.250000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>848.750000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>6560.000000</td>\n",
       "      <td>2160.000000</td>\n",
       "      <td>752.500000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>1570.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1210.00000</td>\n",
       "      <td>98.500000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>2070.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4290.000000</td>\n",
       "      <td>1440.000000</td>\n",
       "      <td>1420.000000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>9420.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>315.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>1195.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>149.500000</td>\n",
       "      <td>1925.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>585.000000</td>\n",
       "      <td>1310.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>10430.000000</td>\n",
       "      <td>4155.000000</td>\n",
       "      <td>1035.000000</td>\n",
       "      <td>2192.500000</td>\n",
       "      <td>3425.000000</td>\n",
       "      <td>16.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3100.00000</td>\n",
       "      <td>575.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>4755.000000</td>\n",
       "      <td>2022.500000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>2315.000000</td>\n",
       "      <td>2175.000000</td>\n",
       "      <td>6610.000000</td>\n",
       "      <td>3235.000000</td>\n",
       "      <td>5750.000000</td>\n",
       "      <td>1375.000000</td>\n",
       "      <td>10800.000000</td>\n",
       "      <td>223.750000</td>\n",
       "      <td>518.250000</td>\n",
       "      <td>1842.500000</td>\n",
       "      <td>650.000000</td>\n",
       "      <td>4310.000000</td>\n",
       "      <td>385.000000</td>\n",
       "      <td>2212.500000</td>\n",
       "      <td>1775.000000</td>\n",
       "      <td>237.500000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>138.500000</td>\n",
       "      <td>482.750000</td>\n",
       "      <td>96097.500000</td>\n",
       "      <td>3248.750000</td>\n",
       "      <td>2262.500000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>4250.000000</td>\n",
       "      <td>1005.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>58.250000</td>\n",
       "      <td>20117.500000</td>\n",
       "      <td>6035.000000</td>\n",
       "      <td>1847.500000</td>\n",
       "      <td>6875.000000</td>\n",
       "      <td>7850.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10100.00000</td>\n",
       "      <td>2600.000000</td>\n",
       "      <td>880.000000</td>\n",
       "      <td>7520.000000</td>\n",
       "      <td>8920.000000</td>\n",
       "      <td>530.000000</td>\n",
       "      <td>8920.000000</td>\n",
       "      <td>5920.000000</td>\n",
       "      <td>25790.000000</td>\n",
       "      <td>14400.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>3940.000000</td>\n",
       "      <td>15800.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>2435.000000</td>\n",
       "      <td>5270.000000</td>\n",
       "      <td>2205.000000</td>\n",
       "      <td>13220.000000</td>\n",
       "      <td>1860.000000</td>\n",
       "      <td>6100.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>800.000000</td>\n",
       "      <td>2530.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>938400.000000</td>\n",
       "      <td>10290.000000</td>\n",
       "      <td>7070.000000</td>\n",
       "      <td>5040.000000</td>\n",
       "      <td>3960.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>8640.000000</td>\n",
       "      <td>3720.000000</td>\n",
       "      <td>3800.000000</td>\n",
       "      <td>505.000000</td>\n",
       "      <td>49950.000000</td>\n",
       "      <td>17990.000000</td>\n",
       "      <td>5520.000000</td>\n",
       "      <td>27010.000000</td>\n",
       "      <td>28461.000000</td>\n",
       "      <td>85.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "drugsID       A01237       A01303      B01163       B01166       B01235  \\\n",
       "count       46.00000    46.000000   46.000000    46.000000    46.000000   \n",
       "mean      2228.26087   422.478261  156.304348  2791.086957  1075.217391   \n",
       "std       2359.12065   581.139369  184.744372  2399.603882  1760.733104   \n",
       "min          0.00000     0.000000    0.000000     0.000000     0.000000   \n",
       "25%        710.00000    48.500000    0.000000   810.000000     0.000000   \n",
       "50%       1210.00000    98.500000  100.000000  2070.000000    60.000000   \n",
       "75%       3100.00000   575.000000  200.000000  4755.000000  2022.500000   \n",
       "max      10100.00000  2600.000000  880.000000  7520.000000  8920.000000   \n",
       "\n",
       "drugsID      B01304       B01373       C01829        C01842        D01057  \\\n",
       "count     46.000000    46.000000    46.000000     46.000000     46.000000   \n",
       "mean     114.304348  1625.673913  1389.565217   4688.695652   2496.521739   \n",
       "std      125.607125  1857.911851  1520.103734   4598.066071   3202.005772   \n",
       "min        0.000000     0.000000     0.000000      0.000000      0.000000   \n",
       "25%       11.250000   130.000000   125.000000   1460.000000    425.000000   \n",
       "50%       77.500000  1110.000000   950.000000   4290.000000   1440.000000   \n",
       "75%      220.000000  2315.000000  2175.000000   6610.000000   3235.000000   \n",
       "max      530.000000  8920.000000  5920.000000  25790.000000  14400.000000   \n",
       "\n",
       "drugsID        D01085       D01304        F00464      G00817       G00821  \\\n",
       "count       46.000000    46.000000     46.000000   46.000000    46.000000   \n",
       "mean      4009.565217   816.086957   8343.478261  154.173913   424.130435   \n",
       "std       5161.491153   906.878598   3896.018462  137.072617   397.986354   \n",
       "min          0.000000     0.000000      0.000000    0.000000    50.000000   \n",
       "25%        600.000000   125.000000   6050.000000   65.750000   184.250000   \n",
       "50%       1420.000000   390.000000   9420.000000  105.000000   334.000000   \n",
       "75%       5750.000000  1375.000000  10800.000000  223.750000   518.250000   \n",
       "max      18000.000000  3940.000000  15800.000000  550.000000  2435.000000   \n",
       "\n",
       "drugsID       G00862       G00869        G00898       H00852       H00902  \\\n",
       "count      46.000000    46.000000     46.000000    46.000000    46.000000   \n",
       "mean     1096.782609   498.369565   3334.782609   276.739130  1641.304348   \n",
       "std      1393.879062   376.309941   3521.780950   318.048245  1717.823699   \n",
       "min         0.000000     0.000000      0.000000     0.000000     0.000000   \n",
       "25%         0.000000   248.500000    925.000000    92.500000   330.000000   \n",
       "50%       315.000000   506.000000   2200.000000   182.500000  1195.000000   \n",
       "75%      1842.500000   650.000000   4310.000000   385.000000  2212.500000   \n",
       "max      5270.000000  2205.000000  13220.000000  1860.000000  6100.000000   \n",
       "\n",
       "drugsID       H01121      L00822       M01076      M01090       M01225  \\\n",
       "count      46.000000   46.000000    46.000000   46.000000    46.000000   \n",
       "mean     1018.478261  160.043478   473.826087   90.478261   273.152174   \n",
       "std      1024.261181  178.880712   653.418338   93.824124   298.302082   \n",
       "min         0.000000    0.000000     0.000000    0.000000     0.000000   \n",
       "25%         0.000000   12.500000    16.250000   20.500000    45.250000   \n",
       "50%       750.000000  100.000000    65.500000   47.000000   149.500000   \n",
       "75%      1775.000000  237.500000   700.000000  138.500000   482.750000   \n",
       "max      3300.000000  800.000000  2530.000000  321.000000  1256.000000   \n",
       "\n",
       "drugsID         N00845        N00864       N00875       N00922       N00979  \\\n",
       "count        46.000000     46.000000    46.000000    46.000000    46.000000   \n",
       "mean      92063.717391   2294.130435  1493.478261  1396.739130   635.000000   \n",
       "std      187411.012397   2028.251383  1906.577174  1218.554799   810.620476   \n",
       "min           0.000000      0.000000     0.000000     0.000000     0.000000   \n",
       "25%         200.000000    848.750000    32.500000   400.000000     0.000000   \n",
       "50%        1925.000000   1800.000000   585.000000  1310.000000   500.000000   \n",
       "75%       96097.500000   3248.750000  2262.500000  1940.000000   950.000000   \n",
       "max      938400.000000  10290.000000  7070.000000  5040.000000  3960.000000   \n",
       "\n",
       "drugsID      O00583       P01191       P01299       Q00087      T01701  \\\n",
       "count     46.000000    46.000000    46.000000    46.000000   46.000000   \n",
       "mean     140.869565  2400.000000   695.217391   716.956522   48.260870   \n",
       "std       71.982284  2919.549433   826.561120  1010.741465   80.393873   \n",
       "min        0.000000     0.000000     0.000000     0.000000    0.000000   \n",
       "25%      100.000000   125.000000   110.000000     0.000000    6.250000   \n",
       "50%      145.000000   700.000000   420.000000   275.000000   28.000000   \n",
       "75%      200.000000  4250.000000  1005.000000  1000.000000   58.250000   \n",
       "max      300.000000  8640.000000  3720.000000  3800.000000  505.000000   \n",
       "\n",
       "drugsID        Z00197        Z00200       Z00209        Z00219        Z00226  \\\n",
       "count       46.000000     46.000000    46.000000     46.000000     46.000000   \n",
       "mean     14253.695652   4652.282609  1314.826087   5866.108696   6027.934783   \n",
       "std      11117.449020   3747.073481   932.136025   7655.104433   7157.890525   \n",
       "min         30.000000    250.000000   260.000000      0.000000      0.000000   \n",
       "25%       6560.000000   2160.000000   752.500000    800.000000   1570.000000   \n",
       "50%      10430.000000   4155.000000  1035.000000   2192.500000   3425.000000   \n",
       "75%      20117.500000   6035.000000  1847.500000   6875.000000   7850.000000   \n",
       "max      49950.000000  17990.000000  5520.000000  27010.000000  28461.000000   \n",
       "\n",
       "drugsID     Z00241  \n",
       "count    46.000000  \n",
       "mean     21.543478  \n",
       "std      20.468628  \n",
       "min       0.000000  \n",
       "25%       7.000000  \n",
       "50%      16.500000  \n",
       "75%      30.000000  \n",
       "max      85.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill NaN == 0\n",
    "df = df.fillna(0)\n",
    "\n",
    "# set threshold for number of zeros\n",
    "threshold = 15\n",
    "\n",
    "# drop columns with more than the threshold number of zeros\n",
    "df_month1 = df.loc[:, (df == 0).sum() <= threshold]\n",
    "df_month1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Drug Consumption Over Time')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x3000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_month1.plot(kind='line', figsize=(10, 30), )\n",
    "\n",
    "# set axis labels and chart title\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Consumption')\n",
    "ax.set_title('Drug Consumption Over Time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_month1.columns.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>drugsID</th>\n",
       "      <th>A01237</th>\n",
       "      <th>A01303</th>\n",
       "      <th>B01163</th>\n",
       "      <th>B01166</th>\n",
       "      <th>B01235</th>\n",
       "      <th>B01304</th>\n",
       "      <th>B01373</th>\n",
       "      <th>C01829</th>\n",
       "      <th>C01842</th>\n",
       "      <th>D01057</th>\n",
       "      <th>D01085</th>\n",
       "      <th>D01304</th>\n",
       "      <th>F00464</th>\n",
       "      <th>G00817</th>\n",
       "      <th>G00821</th>\n",
       "      <th>G00862</th>\n",
       "      <th>G00869</th>\n",
       "      <th>G00898</th>\n",
       "      <th>H00852</th>\n",
       "      <th>H00902</th>\n",
       "      <th>H01121</th>\n",
       "      <th>L00822</th>\n",
       "      <th>M01076</th>\n",
       "      <th>M01090</th>\n",
       "      <th>M01225</th>\n",
       "      <th>N00845</th>\n",
       "      <th>N00864</th>\n",
       "      <th>N00875</th>\n",
       "      <th>N00922</th>\n",
       "      <th>N00979</th>\n",
       "      <th>O00583</th>\n",
       "      <th>P01191</th>\n",
       "      <th>P01299</th>\n",
       "      <th>Q00087</th>\n",
       "      <th>T01701</th>\n",
       "      <th>Z00197</th>\n",
       "      <th>Z00200</th>\n",
       "      <th>Z00209</th>\n",
       "      <th>Z00219</th>\n",
       "      <th>Z00226</th>\n",
       "      <th>Z00241</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11</th>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2210.0</td>\n",
       "      <td>950.0</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12</th>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7270.0</td>\n",
       "      <td>520.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01</th>\n",
       "      <td>200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3180.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>5550.0</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>3100.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2840.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7550.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "drugsID  A01237  A01303  B01163  B01166  B01235  B01304  B01373  C01829  \\\n",
       "time                                                                      \n",
       "2018-10   200.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-11   200.0     0.0     0.0     0.0    60.0     0.0     0.0   500.0   \n",
       "2018-12   600.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-01   200.0   500.0    50.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2019-02     0.0     0.0     0.0   560.0     0.0    10.0     0.0  1000.0   \n",
       "\n",
       "drugsID   C01842  D01057  D01085  D01304  F00464  G00817  G00821  G00862  \\\n",
       "time                                                                       \n",
       "2018-10      0.0     0.0  1400.0     0.0     0.0    30.0    50.0     0.0   \n",
       "2018-11      0.0     0.0   600.0     0.0   800.0    65.0    50.0     0.0   \n",
       "2018-12      0.0     0.0  1600.0     0.0   800.0    10.0   150.0  2080.0   \n",
       "2019-01  10000.0   500.0   600.0     0.0  1600.0     0.0    50.0  3180.0   \n",
       "2019-02   5100.0  3100.0  2000.0     0.0  2840.0    60.0    70.0  1500.0   \n",
       "\n",
       "drugsID  G00869  G00898  H00852  H00902  H01121  L00822  M01076  M01090  \\\n",
       "time                                                                      \n",
       "2018-10     0.0   460.0     0.0     0.0     0.0     0.0    30.0     0.0   \n",
       "2018-11   165.0   460.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
       "2018-12    80.0   400.0     0.0     0.0     0.0     0.0    30.0     0.0   \n",
       "2019-01    50.0     0.0   180.0   500.0     0.0     0.0    90.0     0.0   \n",
       "2019-02   200.0    60.0   150.0   500.0     0.0     0.0   200.0     5.0   \n",
       "\n",
       "drugsID  M01225  N00845  N00864  N00875  N00922  N00979  O00583  P01191  \\\n",
       "time                                                                      \n",
       "2018-10     0.0     0.0     0.0   400.0     0.0     0.0   150.0     0.0   \n",
       "2018-11     0.0     0.0     0.0     0.0     0.0     0.0   100.0     0.0   \n",
       "2018-12     0.0     0.0   600.0    20.0     0.0     0.0   100.0     0.0   \n",
       "2019-01    50.0    50.0  3800.0   770.0     0.0   500.0   100.0  1400.0   \n",
       "2019-02     5.0     0.0  2500.0    30.0     0.0     0.0   150.0     0.0   \n",
       "\n",
       "drugsID  P01299  Q00087  T01701   Z00197  Z00200  Z00209  Z00219  Z00226  \\\n",
       "time                                                                       \n",
       "2018-10     0.0     0.0     0.0     30.0   350.0   300.0     0.0     0.0   \n",
       "2018-11     0.0   200.0     0.0   2210.0   950.0  2100.0   100.0     0.0   \n",
       "2018-12     0.0   500.0     0.0   7270.0   520.0  1000.0   200.0     0.0   \n",
       "2019-01     0.0  1350.0     0.0  12150.0  1010.0   500.0  5550.0  6250.0   \n",
       "2019-02     0.0  1350.0     0.0   7550.0   250.0  1020.0  2500.0   700.0   \n",
       "\n",
       "drugsID  Z00241  \n",
       "time             \n",
       "2018-10     0.0  \n",
       "2018-11     0.0  \n",
       "2018-12     0.0  \n",
       "2019-01     0.0  \n",
       "2019-02    10.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month1.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chọn 10 loại thuốc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drugsID\n",
      "A01237     102500.0\n",
      "A01303      19434.0\n",
      "B01163       7190.0\n",
      "B01166     128390.0\n",
      "B01235      49460.0\n",
      "B01304       5258.0\n",
      "B01373      74781.0\n",
      "C01829      63920.0\n",
      "C01842     215680.0\n",
      "D01057     114840.0\n",
      "D01085     184440.0\n",
      "D01304      37540.0\n",
      "F00464     383800.0\n",
      "G00817       7092.0\n",
      "G00821      19510.0\n",
      "G00862      50452.0\n",
      "G00869      22925.0\n",
      "G00898     153400.0\n",
      "H00852      12730.0\n",
      "H00902      75500.0\n",
      "H01121      46850.0\n",
      "L00822       7362.0\n",
      "M01076      21796.0\n",
      "M01090       4162.0\n",
      "M01225      12565.0\n",
      "N00845    4234931.0\n",
      "N00864     105530.0\n",
      "N00875      68700.0\n",
      "N00922      64250.0\n",
      "N00979      29210.0\n",
      "O00583       6480.0\n",
      "P01191     110400.0\n",
      "P01299      31980.0\n",
      "Q00087      32980.0\n",
      "T01701       2220.0\n",
      "Z00197     655670.0\n",
      "Z00200     214005.0\n",
      "Z00209      60482.0\n",
      "Z00219     269841.0\n",
      "Z00226     277285.0\n",
      "Z00241        991.0\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['N00845',\n",
       " 'Z00197',\n",
       " 'F00464',\n",
       " 'Z00226',\n",
       " 'Z00219',\n",
       " 'C01842',\n",
       " 'Z00200',\n",
       " 'D01085',\n",
       " 'G00898',\n",
       " 'B01166']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_sums = df_month1.sum(axis=0)\n",
    "print(column_sums) \n",
    "top_values = column_sums.nlargest(10).index.tolist()\n",
    "top_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>time</th>\n",
       "      <th>drugsID</th>\n",
       "      <th>order_numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>432</td>\n",
       "      <td>432</td>\n",
       "      <td>432.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>46</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2020-09</td>\n",
       "      <td>Z00197</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15549.634259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66398.312389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8907.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>938400.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           time drugsID  order_numbers\n",
       "count       432     432     432.000000\n",
       "unique       46      10            NaN\n",
       "top     2020-09  Z00197            NaN\n",
       "freq         10      46            NaN\n",
       "mean        NaN     NaN   15549.634259\n",
       "std         NaN     NaN   66398.312389\n",
       "min         NaN     NaN      20.000000\n",
       "25%         NaN     NaN    1495.000000\n",
       "50%         NaN     NaN    4210.000000\n",
       "75%         NaN     NaN    8907.500000\n",
       "max         NaN     NaN  938400.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = df_month['drugsID'].isin(top_values)\n",
    "\n",
    "# Step 4: Select the rows that contain the top 7 values\n",
    "df_top10 = df_month.loc[mask]\n",
    "df_top10.drugsID.unique()\n",
    "df_top10.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>time</th>\n",
       "      <th>drugsID</th>\n",
       "      <th>order_numbers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>D01085</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>G00898</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>Z00197</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2018-10</td>\n",
       "      <td>Z00200</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2018-11</td>\n",
       "      <td>D01085</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time drugsID  order_numbers\n",
       "3   2018-10  D01085         1400.0\n",
       "6   2018-10  G00898          460.0\n",
       "10  2018-10  Z00197           30.0\n",
       "11  2018-10  Z00200          350.0\n",
       "18  2018-11  D01085          600.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top10.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def find_outliers(data):\n",
    "#     # calculate quartiles\n",
    "#     Q1 = data.quantile(0.25)\n",
    "#     Q3 = data.quantile(0.75)\n",
    "#     # calculate interquartile range\n",
    "#     IQR = Q3 - Q1\n",
    "#     # calculate upper and lower bounds for outliers\n",
    "#     upper_bound = Q3 + 1.5*IQR\n",
    "#     lower_bound = Q1 - 1.5*IQR\n",
    "#     # find outliers\n",
    "#     outliers = data[(data < lower_bound) | (data > upper_bound)]\n",
    "#     return outliers\n",
    "\n",
    "# outliers = find_outliers(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'find_outliers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m outlier_counts \u001b[39m=\u001b[39m []\n\u001b[0;32m      3\u001b[0m \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m df\u001b[39m.\u001b[39mcolumns:\n\u001b[1;32m----> 4\u001b[0m     outliers \u001b[39m=\u001b[39m find_outliers(df[col])\n\u001b[0;32m      5\u001b[0m     outlier_counts\u001b[39m.\u001b[39mappend(\u001b[39mlen\u001b[39m(outliers))\n\u001b[0;32m      6\u001b[0m \u001b[39mprint\u001b[39m(outlier_counts)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'find_outliers' is not defined"
     ]
    }
   ],
   "source": [
    "# Count number of outliers for each column\n",
    "outlier_counts = []\n",
    "for col in df.columns:\n",
    "    outliers = find_outliers(df[col])\n",
    "    outlier_counts.append(len(outliers))\n",
    "print(outlier_counts)\n",
    "max_outliers= max(outlier_counts)\n",
    "print(max_outliers)\n",
    "\n",
    "# Find columns with more than 17 outliers\n",
    "cols_more_than_10_outliers = []\n",
    "for i, count in enumerate(outlier_counts):\n",
    "    if count > 7:\n",
    "        cols_more_than_17_outliers.append(df.columns[i])\n",
    "\n",
    "print(cols_more_than_17_outliers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3: 21, 4: 20, 2: 41, 8: 12, 9: 18, 5: 20, 1: 35, 6: 22, 10: 15, 7: 11, 0: 15, 11: 4}\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "count_dict = dict(Counter(outlier_counts))\n",
    "print(count_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drugsID\n",
      "A01194     3\n",
      "A01217     4\n",
      "A01237     4\n",
      "A01252     2\n",
      "A01259     8\n",
      "A01264     9\n",
      "A01268     5\n",
      "A01272     5\n",
      "A01303     4\n",
      "A01338     1\n",
      "A01341     9\n",
      "A01348     6\n",
      "A01379    10\n",
      "A01401     7\n",
      "A01408     2\n",
      "A01411     5\n",
      "A01414     4\n",
      "A01500     9\n",
      "B01113     3\n",
      "B01141     3\n",
      "B01163     2\n",
      "B01166     0\n",
      "B01178     6\n",
      "B01235     1\n",
      "B01244    11\n",
      "B01247     8\n",
      "B01304     0\n",
      "B01308     5\n",
      "B01332     4\n",
      "B01338     4\n",
      "B01347     0\n",
      "B01373     2\n",
      "B01414     3\n",
      "B01463     9\n",
      "B01472     6\n",
      "B01514    10\n",
      "B01570     6\n",
      "B01585     5\n",
      "B01586     2\n",
      "C01829     1\n",
      "C01842     2\n",
      "C01942    10\n",
      "C02136    10\n",
      "C02283     2\n",
      "D01057     4\n",
      "D01063     5\n",
      "D01078     4\n",
      "D01085     5\n",
      "D01107     6\n",
      "D01116     1\n",
      "D01149     4\n",
      "D01160     5\n",
      "D01169     4\n",
      "D01183    10\n",
      "D01198     9\n",
      "D01215     6\n",
      "D01260     1\n",
      "D01304     1\n",
      "D01311    10\n",
      "D01335     0\n",
      "D01349     2\n",
      "D01355     5\n",
      "D01357     9\n",
      "D01366    10\n",
      "D01375     2\n",
      "D01425     5\n",
      "D01449     8\n",
      "D01502     3\n",
      "D01510     6\n",
      "D01519    10\n",
      "D01522     7\n",
      "D01533     6\n",
      "D01599     6\n",
      "D01643     1\n",
      "E00471    10\n",
      "F00440     2\n",
      "F00443     2\n",
      "F00464     0\n",
      "F00472     1\n",
      "F00482     2\n",
      "F00490     1\n",
      "F00507     2\n",
      "F00512     9\n",
      "F00550     2\n",
      "F00598     9\n",
      "F00609     8\n",
      "G00817     3\n",
      "G00821     2\n",
      "G00862     1\n",
      "G00869     2\n",
      "G00898     5\n",
      "G00910     1\n",
      "G00911     6\n",
      "G00939     9\n",
      "G00943     6\n",
      "G00951     1\n",
      "G00955    10\n",
      "G00971     6\n",
      "G00973    10\n",
      "G00999    10\n",
      "G01006     6\n",
      "G01021     8\n",
      "H00852     1\n",
      "H00902     3\n",
      "H00905     7\n",
      "H00920     8\n",
      "H00979     2\n",
      "H01031     1\n",
      "H01036     3\n",
      "H01039     6\n",
      "H01068     3\n",
      "H01121     0\n",
      "H01210     8\n",
      "H01224     2\n",
      "H01251     4\n",
      "H01913     9\n",
      "H01921     8\n",
      "H01924     7\n",
      "H01930     2\n",
      "H02087     3\n",
      "H02098     1\n",
      "H02101     1\n",
      "K00511     0\n",
      "K00628     2\n",
      "L00726     2\n",
      "L00750     0\n",
      "L00763     2\n",
      "L00784     9\n",
      "L00793     9\n",
      "L00801     1\n",
      "L00822     2\n",
      "L00837     3\n",
      "L00847     5\n",
      "L00854     2\n",
      "L00877     7\n",
      "L00905     6\n",
      "L00993     2\n",
      "L01005     8\n",
      "L01012    11\n",
      "L01021     8\n",
      "L01038     3\n",
      "L01052     1\n",
      "L01053     1\n",
      "L01057     1\n",
      "L01058     1\n",
      "M01045     7\n",
      "M01070     6\n",
      "M01076     3\n",
      "M01090     1\n",
      "M01096     2\n",
      "M01215     4\n",
      "M01225     1\n",
      "M01327     1\n",
      "M01331     9\n",
      "M01476     2\n",
      "M01596     1\n",
      "M01605     1\n",
      "M01656     6\n",
      "N00520     7\n",
      "N00826     2\n",
      "N00845     4\n",
      "N00864     1\n",
      "N00875     3\n",
      "N00922     2\n",
      "N00928     2\n",
      "N00941     2\n",
      "N00979     2\n",
      "N00994     5\n",
      "N01007     5\n",
      "N01010     0\n",
      "N01013    11\n",
      "N01041     3\n",
      "N01084     5\n",
      "N01108     4\n",
      "N01165     7\n",
      "N01195     3\n",
      "N01200     7\n",
      "N01257     9\n",
      "N01260     5\n",
      "N01299     1\n",
      "O00583     0\n",
      "P01083     5\n",
      "P01087     2\n",
      "P01146     0\n",
      "P01191     0\n",
      "P01260     5\n",
      "P01275     6\n",
      "P01278     1\n",
      "P01291     1\n",
      "P01299     2\n",
      "P01336     4\n",
      "P01349     2\n",
      "P01364     0\n",
      "P01394     9\n",
      "P01411     8\n",
      "P01452     3\n",
      "P01475     6\n",
      "P01487     9\n",
      "P01532     4\n",
      "P01605     2\n",
      "Q00087     4\n",
      "Q00103     2\n",
      "Q00108     1\n",
      "R00329     3\n",
      "R00342     6\n",
      "R00365     0\n",
      "R00376    10\n",
      "S01123     1\n",
      "S01242     4\n",
      "S01434     2\n",
      "S01447    10\n",
      "T01684     8\n",
      "T01701     3\n",
      "T01711     9\n",
      "T01855     3\n",
      "T01864     0\n",
      "T02071     2\n",
      "T02722     4\n",
      "U00134    10\n",
      "V01000     6\n",
      "V01101     5\n",
      "V01106     2\n",
      "V01114     1\n",
      "Z00197     1\n",
      "Z00200     3\n",
      "Z00209     1\n",
      "Z00219     7\n",
      "Z00226     5\n",
      "Z00230     7\n",
      "Z00236     4\n",
      "Z00241     2\n",
      "Z00244    11\n",
      "Z00253     9\n",
      "Z00290     6\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "def count_outliers(data):\n",
    "    outliers, upper, lower = find_outliers(data)\n",
    "    return len(outliers)\n",
    "\n",
    "outlier_counts = df.apply(count_outliers)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "object of type 'numpy.float64' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[60], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m outlier_counts \u001b[39m=\u001b[39m [\u001b[39mlen\u001b[39m(find_outliers(df[col])[\u001b[39m0\u001b[39m]) \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m df\u001b[39m.\u001b[39mcolumns]\n\u001b[0;32m      2\u001b[0m cols_more_than_17_outliers \u001b[39m=\u001b[39m [col \u001b[39mfor\u001b[39;00m i, col \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(df\u001b[39m.\u001b[39mcolumns) \u001b[39mif\u001b[39;00m outlier_counts[i] \u001b[39m>\u001b[39m \u001b[39m17\u001b[39m]\n\u001b[0;32m      3\u001b[0m \u001b[39mprint\u001b[39m(cols_more_than_17_outliers)\n",
      "Cell \u001b[1;32mIn[60], line 1\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[0m outlier_counts \u001b[39m=\u001b[39m [\u001b[39mlen\u001b[39;49m(find_outliers(df[col])[\u001b[39m0\u001b[39;49m]) \u001b[39mfor\u001b[39;00m col \u001b[39min\u001b[39;00m df\u001b[39m.\u001b[39mcolumns]\n\u001b[0;32m      2\u001b[0m cols_more_than_17_outliers \u001b[39m=\u001b[39m [col \u001b[39mfor\u001b[39;00m i, col \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(df\u001b[39m.\u001b[39mcolumns) \u001b[39mif\u001b[39;00m outlier_counts[i] \u001b[39m>\u001b[39m \u001b[39m17\u001b[39m]\n\u001b[0;32m      3\u001b[0m \u001b[39mprint\u001b[39m(cols_more_than_17_outliers)\n",
      "\u001b[1;31mTypeError\u001b[0m: object of type 'numpy.float64' has no len()"
     ]
    }
   ],
   "source": [
    "outlier_counts = [len(find_outliers(df[col])[0]) for col in df.columns]\n",
    "cols_more_than_17_outliers = [col for i, col in enumerate(df.columns) if outlier_counts[i] > 17]\n",
    "print(cols_more_than_17_outliers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [year, month, value]\n",
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     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_above_upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [year, month, value]\n",
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_below_lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  value\n",
       "0   NaN    NaN    NaN\n",
       "1   NaN    NaN    NaN\n",
       "2   NaN    NaN    NaN\n",
       "3   NaN    NaN    NaN\n",
       "4   NaN    NaN    NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outliers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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