ParcelTracking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline \n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "from datetime import datetime\n",
    "from operator import itemgetter\n",
    "from itertools import groupby\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import dataset\n",
    "pt = pd.read_csv('mall_pt/csv/providers.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2057399, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#exploring dataset\n",
    "pt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2057399 entries, 0 to 2057398\n",
      "Data columns (total 5 columns):\n",
      "tracking_id      int64\n",
      "date             object\n",
      "status           object\n",
      "mapped_status    object\n",
      "provider         object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 78.5+ MB\n"
     ]
    }
   ],
   "source": [
    "pt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.057399e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.625531e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.316196e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.301900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.302200e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.372770e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.619490e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.403023e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        tracking_id\n",
       "count  2.057399e+06\n",
       "mean   6.625531e+05\n",
       "std    3.316196e+05\n",
       "min    5.301900e+04\n",
       "25%    4.302200e+05\n",
       "50%    4.372770e+05\n",
       "75%    9.619490e+05\n",
       "max    1.403023e+06"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tracking_id          0\n",
       "date                60\n",
       "status              60\n",
       "mapped_status    34867\n",
       "provider             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find empty dates\n",
    "pt[pt[\"date\"].isnull() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pt[pt[\"date\"].isnull() == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove rows with empty date\n",
    "pt = pt.dropna(subset=['date'], how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pt[pt[\"date\"].isnull() == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert date to our datetime format\n",
    "def update_date(date):\n",
    "    new_date = datetime.strptime(date, '%Y-%m-%d %H:%M:%S')\n",
    "    return new_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pt = pt.copy(deep = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1363206</td>\n",
       "      <td>2017-12-13 16:29:27</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1363207</td>\n",
       "      <td>2017-12-13 13:38:48</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1363208</td>\n",
       "      <td>2017-12-13 12:08:33</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   tracking_id                 date                         status  \\\n",
       "0      1363206  2017-12-13 16:29:27  Balíček jsme úspěšně doručili   \n",
       "1      1363207  2017-12-13 13:38:48  Balíček jsme úspěšně doručili   \n",
       "2      1363208  2017-12-13 12:08:33  Balíček jsme úspěšně doručili   \n",
       "\n",
       "  mapped_status provider  \n",
       "0      Doručeno      dpd  \n",
       "1      Doručeno      dpd  \n",
       "2      Doručeno      dpd  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_pt.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_pt[\"date\"] = clean_pt[\"date\"].apply(update_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1363206</td>\n",
       "      <td>2017-12-13 16:29:27</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1363207</td>\n",
       "      <td>2017-12-13 13:38:48</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1363208</td>\n",
       "      <td>2017-12-13 12:08:33</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1363209</td>\n",
       "      <td>2017-12-13 14:06:00</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1363210</td>\n",
       "      <td>2017-12-13 15:14:08</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tracking_id                date                         status  \\\n",
       "0      1363206 2017-12-13 16:29:27  Balíček jsme úspěšně doručili   \n",
       "1      1363207 2017-12-13 13:38:48  Balíček jsme úspěšně doručili   \n",
       "2      1363208 2017-12-13 12:08:33  Balíček jsme úspěšně doručili   \n",
       "3      1363209 2017-12-13 14:06:00  Balíček jsme úspěšně doručili   \n",
       "4      1363210 2017-12-13 15:14:08  Balíček jsme úspěšně doručili   \n",
       "\n",
       "  mapped_status provider  \n",
       "0      Doručeno      dpd  \n",
       "1      Doručeno      dpd  \n",
       "2      Doručeno      dpd  \n",
       "3      Doručeno      dpd  \n",
       "4      Doručeno      dpd  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_pt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2057339 entries, 0 to 2057398\n",
      "Data columns (total 5 columns):\n",
      "tracking_id      int64\n",
      "date             datetime64[ns]\n",
      "status           object\n",
      "mapped_status    object\n",
      "provider         object\n",
      "dtypes: datetime64[ns](1), int64(1), object(3)\n",
      "memory usage: 94.2+ MB\n"
     ]
    }
   ],
   "source": [
    "clean_pt.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id = clean_pt[[\"tracking_id\"]].groupby(clean_pt[\"tracking_id\"]).count().sort_values(by='tracking_id', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove duplicates\n",
    "no_pt_dup = clean_pt.drop_duplicates(['tracking_id', 'date', 'status'], inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_pt_dup.sort_values(by='tracking_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id_no_dup = no_pt_dup[[\"tracking_id\"]].groupby(no_pt_dup[\"tracking_id\"]).count().sort_values(by='tracking_id', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id_1 = tracking_id_no_dup[tracking_id_no_dup[\"tracking_id\"].values == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([82, 81, 77, ...,  1,  1,  1])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tracking_id_no_dup[\"tracking_id\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id_1.to_csv('mall_pt/csv/1_state.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id_no_dup = tracking_id_no_dup[tracking_id_no_dup[\"tracking_id\"].values > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id_no_dup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id_no_dup.to_csv('mall_pt/csv/duplicate_ids.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tracking_id_same_status = no_pt_dup[[\"status\"]].groupby([no_pt_dup[\"status\"], no_pt_dup[\"tracking_id\"], no_pt_dup[\"provider\"]]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "same_statuses = tracking_id_same_status[tracking_id_same_status[\"status\"] > 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "same_statuses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "same_statuses.to_csv('mall_pt/csv/same_statuses.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_dup_per_id = no_pt_dup[no_pt_dup[\"tracking_id\"] == 1294084]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1951769</th>\n",
       "      <td>1294084</td>\n",
       "      <td>2017-12-12 17:51:30</td>\n",
       "      <td>Převážení/vážení</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>ppl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951770</th>\n",
       "      <td>1294084</td>\n",
       "      <td>2017-12-13 16:10:09</td>\n",
       "      <td>Příjem na centrálu</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>ppl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951771</th>\n",
       "      <td>1294084</td>\n",
       "      <td>2017-12-13 16:11:05</td>\n",
       "      <td>Příjem na centrálu</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>ppl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951772</th>\n",
       "      <td>1294084</td>\n",
       "      <td>2017-12-13 16:11:39</td>\n",
       "      <td>Příjem na centrálu</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>ppl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1951773</th>\n",
       "      <td>1294084</td>\n",
       "      <td>2017-12-13 16:12:35</td>\n",
       "      <td>Příjem na centrálu</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>ppl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tracking_id                date              status  \\\n",
       "1951769      1294084 2017-12-12 17:51:30    Převážení/vážení   \n",
       "1951770      1294084 2017-12-13 16:10:09  Příjem na centrálu   \n",
       "1951771      1294084 2017-12-13 16:11:05  Příjem na centrálu   \n",
       "1951772      1294084 2017-12-13 16:11:39  Příjem na centrálu   \n",
       "1951773      1294084 2017-12-13 16:12:35  Příjem na centrálu   \n",
       "\n",
       "              mapped_status provider  \n",
       "1951769  Převzato dopravcem      ppl  \n",
       "1951770  Převzato dopravcem      ppl  \n",
       "1951771  Převzato dopravcem      ppl  \n",
       "1951772  Převzato dopravcem      ppl  \n",
       "1951773  Převzato dopravcem      ppl  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_dup_per_id.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_dup_per_id.to_csv('mall_pt/csv/duplicates_example.csv', sep=',', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dup = no_pt_dup[no_pt_dup[\"tracking_id\"].isin(tracking_id_no_dup[\"tracking_id\"].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(268551, 5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_dup.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(659370, 5)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_pt_dup.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dup1 = all_dup.sort_values(by='tracking_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_dup1.to_csv('mall_pt/csv/duplicates_detail.csv', sep=',', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>tracking_id</th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1363206</td>\n",
       "      <td>2017-12-13 16:29:27</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1363207</td>\n",
       "      <td>2017-12-13 13:38:48</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1363208</td>\n",
       "      <td>2017-12-13 12:08:33</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1363209</td>\n",
       "      <td>2017-12-13 14:06:00</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1363210</td>\n",
       "      <td>2017-12-13 15:14:08</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tracking_id                date                         status  \\\n",
       "0      1363206 2017-12-13 16:29:27  Balíček jsme úspěšně doručili   \n",
       "1      1363207 2017-12-13 13:38:48  Balíček jsme úspěšně doručili   \n",
       "2      1363208 2017-12-13 12:08:33  Balíček jsme úspěšně doručili   \n",
       "3      1363209 2017-12-13 14:06:00  Balíček jsme úspěšně doručili   \n",
       "4      1363210 2017-12-13 15:14:08  Balíček jsme úspěšně doručili   \n",
       "\n",
       "  mapped_status provider  \n",
       "0      Doručeno      dpd  \n",
       "1      Doručeno      dpd  \n",
       "2      Doručeno      dpd  \n",
       "3      Doručeno      dpd  \n",
       "4      Doručeno      dpd  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_pt_dup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#- najvyuzivanejsi dopravca\n",
    "most_common_provider = no_pt_dup[\"provider\"].groupby([no_pt_dup[\"provider\"]]).count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "provider\n",
       "intime             422669\n",
       "ppl                109325\n",
       "ceskaposta          45982\n",
       "raben               44902\n",
       "gebruderweiss       17724\n",
       "ulozenka            14394\n",
       "toptrans             2020\n",
       "geis                 1751\n",
       "zavolejsikuryra       495\n",
       "dpd                   108\n",
       "Name: provider, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common_provider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10db72550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm  \n",
    "\n",
    "\n",
    "type_colors = cm.autumn(np.linspace(0.05,0.95,len(most_common_provider)))\n",
    "ax = most_common_provider.plot.bar(color=type_colors, title='Mall providers', figsize=(10,10))\n",
    "ax.set_xlabel = 'Providers'\n",
    "ax.set_ylabel = 'Number of deliveries'\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x(), p.get_height() * 1.005))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = ax.get_figure()\n",
    "fig.savefig('mall_pt/csv/providers.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "delivered = no_pt_dup[no_pt_dup[\"mapped_status\"] == \"Doručeno\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(112669, 5)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>tracking_id</th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1363206</td>\n",
       "      <td>2017-12-13 16:29:27</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1363207</td>\n",
       "      <td>2017-12-13 13:38:48</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1363208</td>\n",
       "      <td>2017-12-13 12:08:33</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1363209</td>\n",
       "      <td>2017-12-13 14:06:00</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1363210</td>\n",
       "      <td>2017-12-13 15:14:08</td>\n",
       "      <td>Balíček jsme úspěšně doručili</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>dpd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tracking_id                date                         status  \\\n",
       "0      1363206 2017-12-13 16:29:27  Balíček jsme úspěšně doručili   \n",
       "1      1363207 2017-12-13 13:38:48  Balíček jsme úspěšně doručili   \n",
       "2      1363208 2017-12-13 12:08:33  Balíček jsme úspěšně doručili   \n",
       "3      1363209 2017-12-13 14:06:00  Balíček jsme úspěšně doručili   \n",
       "4      1363210 2017-12-13 15:14:08  Balíček jsme úspěšně doručili   \n",
       "\n",
       "  mapped_status provider  \n",
       "0      Doručeno      dpd  \n",
       "1      Doručeno      dpd  \n",
       "2      Doručeno      dpd  \n",
       "3      Doručeno      dpd  \n",
       "4      Doručeno      dpd  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "most_common_provider_delivered = delivered[\"provider\"].groupby([delivered[\"provider\"]]).count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "provider\n",
       "intime             60951\n",
       "ppl                16977\n",
       "ceskaposta         14612\n",
       "raben               9697\n",
       "gebruderweiss       8191\n",
       "ulozenka            1756\n",
       "toptrans             257\n",
       "dpd                  108\n",
       "geis                  66\n",
       "zavolejsikuryra       54\n",
       "Name: provider, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_common_provider_delivered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106219668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_colors = cm.autumn(np.linspace(0.05,0.95,len(most_common_provider_delivered)))\n",
    "ax = most_common_provider_delivered.plot.bar(color=type_colors, title='Mall providers', figsize=(10,10))\n",
    "ax.set_xlabel = 'Providers'\n",
    "ax.set_ylabel = 'Number of deliveries'\n",
    "for p in ax.patches:\n",
    "    ax.annotate(str(p.get_height()), (p.get_x(), p.get_height() * 1.005))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig1 = ax.get_figure()\n",
    "fig1.savefig('mall_pt/csv/providers_delivered.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancelled = no_pt_dup[no_pt_dup[\"mapped_status\"] == \"Stornováno\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>tracking_id</th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4374</th>\n",
       "      <td>424312</td>\n",
       "      <td>2017-11-30 10:24:52</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>intime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4375</th>\n",
       "      <td>424295</td>\n",
       "      <td>2017-11-30 10:26:18</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>intime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4376</th>\n",
       "      <td>424315</td>\n",
       "      <td>2017-11-30 10:26:41</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>intime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4377</th>\n",
       "      <td>424311</td>\n",
       "      <td>2017-11-30 10:27:15</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>intime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4378</th>\n",
       "      <td>424298</td>\n",
       "      <td>2017-11-30 10:27:36</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>intime</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tracking_id                date      status mapped_status provider\n",
       "4374       424312 2017-11-30 10:24:52  Stornováno    Stornováno   intime\n",
       "4375       424295 2017-11-30 10:26:18  Stornováno    Stornováno   intime\n",
       "4376       424315 2017-11-30 10:26:41  Stornováno    Stornováno   intime\n",
       "4377       424311 2017-11-30 10:27:15  Stornováno    Stornováno   intime\n",
       "4378       424298 2017-11-30 10:27:36  Stornováno    Stornováno   intime"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancelled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancelled_count = cancelled[\"provider\"].groupby([cancelled[\"provider\"]]).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancelled_count = cancelled_count.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancelled_count = cancelled_count.rename(columns = {'provider' : 'n_c'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>n_c</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>provider</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ceskaposta</th>\n",
       "      <td>2471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gebruderweiss</th>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geis</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>intime</th>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ppl</th>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ulozenka</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zavolejsikuryra</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  n_c\n",
       "provider             \n",
       "ceskaposta       2471\n",
       "gebruderweiss     522\n",
       "geis               32\n",
       "intime            594\n",
       "ppl               392\n",
       "ulozenka          125\n",
       "zavolejsikuryra    21"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancelled_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1084d03c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bx = cancelled_count.boxplot(column='n_c', by='provider', figsize = (10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig2 = bx.get_figure()\n",
    "fig2.savefig('mall_pt/csv/providers_cancelled.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "392266   1899-12-30 11:13:46\n",
       "426400   2017-09-15 12:28:05\n",
       "426452   2017-09-15 12:28:05\n",
       "428480   2017-09-15 13:42:58\n",
       "428532   2017-09-15 13:42:58\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delivered['date'].sort_values(ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "delivered_last_month = delivered.loc[delivered.date > '2017-12-01 08:00:00']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "delivered_last_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d9bd5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cx = delivered_last_month.groupby([pd.Grouper(key='date', freq='W'), 'provider']) \\\n",
    "  .size() \\\n",
    "  .unstack('provider', fill_value=0) \\\n",
    "  .pipe(lambda x: x.div(x.sum(1), axis=0)) \\\n",
    "  .plot(kind='area', figsize=(12,6), title='Percentage of delivered - December') \\\n",
    "  .legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig3 = cx.get_figure()\n",
    "fig3.savefig('mall_pt/csv/december.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "providers_grouped = no_pt_dup[[\"tracking_id\", \"provider\", \"status\", \"mapped_status\", \"date\"]].sort_values(by=['date']).groupby(\n",
    "    [no_pt_dup[\"tracking_id\"], no_pt_dup[\"date\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>tracking_id</th>\n",
       "      <th>provider</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>392266</th>\n",
       "      <td>1112829</td>\n",
       "      <td>intime</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>Doručeno</td>\n",
       "      <td>1899-12-30 11:13:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053357</th>\n",
       "      <td>1365496</td>\n",
       "      <td>ceskaposta</td>\n",
       "      <td>pořízeno na pochůzku</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>1970-01-01 11:33:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425204</th>\n",
       "      <td>427198</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 08:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425308</th>\n",
       "      <td>427199</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 08:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425412</th>\n",
       "      <td>427200</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 08:05:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424996</th>\n",
       "      <td>427196</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 08:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425100</th>\n",
       "      <td>427197</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 08:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424892</th>\n",
       "      <td>427195</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 08:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425620</th>\n",
       "      <td>427202</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 11:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425516</th>\n",
       "      <td>427201</td>\n",
       "      <td>raben</td>\n",
       "      <td>Naloženo z depa (na line haul)</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-09-15 11:52:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         tracking_id    provider                          status  \\\n",
       "392266       1112829      intime                        Doručeno   \n",
       "2053357      1365496  ceskaposta            pořízeno na pochůzku   \n",
       "425204        427198       raben  Naloženo z depa (na line haul)   \n",
       "425308        427199       raben  Naloženo z depa (na line haul)   \n",
       "425412        427200       raben  Naloženo z depa (na line haul)   \n",
       "424996        427196       raben  Naloženo z depa (na line haul)   \n",
       "425100        427197       raben  Naloženo z depa (na line haul)   \n",
       "424892        427195       raben  Naloženo z depa (na line haul)   \n",
       "425620        427202       raben  Naloženo z depa (na line haul)   \n",
       "425516        427201       raben  Naloženo z depa (na line haul)   \n",
       "\n",
       "               mapped_status                date  \n",
       "392266              Doručeno 1899-12-30 11:13:46  \n",
       "2053357  Na cestě k příjemci 1970-01-01 11:33:37  \n",
       "425204   Na cestě k příjemci 2017-09-15 08:05:00  \n",
       "425308   Na cestě k příjemci 2017-09-15 08:05:00  \n",
       "425412   Na cestě k příjemci 2017-09-15 08:05:00  \n",
       "424996   Na cestě k příjemci 2017-09-15 08:24:00  \n",
       "425100   Na cestě k příjemci 2017-09-15 08:24:00  \n",
       "424892   Na cestě k příjemci 2017-09-15 08:24:00  \n",
       "425620   Na cestě k příjemci 2017-09-15 11:52:00  \n",
       "425516   Na cestě k příjemci 2017-09-15 11:52:00  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "providers_grouped.tail()[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = providers_grouped.head().sort_values(by=['provider', 'tracking_id', 'date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>tracking_id</th>\n",
       "      <th>provider</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>416436</th>\n",
       "      <td>53019</td>\n",
       "      <td>intime</td>\n",
       "      <td>Výdej na kurýra</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>2017-12-20 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421200</th>\n",
       "      <td>53019</td>\n",
       "      <td>intime</td>\n",
       "      <td>Výdej na kurýra</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>2017-12-20 18:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421759</th>\n",
       "      <td>53019</td>\n",
       "      <td>intime</td>\n",
       "      <td>Výdej na kurýra</td>\n",
       "      <td>Převzato dopravcem</td>\n",
       "      <td>2017-12-21 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422906</th>\n",
       "      <td>53019</td>\n",
       "      <td>intime</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>Na cestě k příjemci</td>\n",
       "      <td>2017-12-21 08:53:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423324</th>\n",
       "      <td>53019</td>\n",
       "      <td>intime</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>2017-12-21 10:05:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        tracking_id provider               status        mapped_status  \\\n",
       "416436        53019   intime      Výdej na kurýra   Převzato dopravcem   \n",
       "421200        53019   intime      Výdej na kurýra   Převzato dopravcem   \n",
       "421759        53019   intime      Výdej na kurýra   Převzato dopravcem   \n",
       "422906        53019   intime  Na cestě k příjemci  Na cestě k příjemci   \n",
       "423324        53019   intime           Stornováno           Stornováno   \n",
       "\n",
       "                      date  \n",
       "416436 2017-12-20 02:00:00  \n",
       "421200 2017-12-20 18:08:00  \n",
       "421759 2017-12-21 02:00:00  \n",
       "422906 2017-12-21 08:53:10  \n",
       "423324 2017-12-21 10:05:20  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df['tracking_id'] == 53019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp_dict = {}\n",
    "intime_dict = {}\n",
    "ppl_dict = {}\n",
    "dpd_dict = {}\n",
    "gw_dict = {}\n",
    "geis_dict = {}\n",
    "raben_dict = {}\n",
    "ulozenka_dict = {}\n",
    "kuryr_dict = {}\n",
    "toptrans_dict = {}\n",
    "uni = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_arr = []\n",
    "c = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, group in providers_grouped:\n",
    "    temp_arr.append([group.mapped_status.values[0], group.provider.values[0], group.tracking_id.values[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = collections.defaultdict(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in temp_arr:\n",
    "    d[v].append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = sorted(d.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53019 ['Převzato dopravcem', 'Převzato dopravcem', 'Převzato dopravcem', 'Na cestě k příjemci', 'Stornováno']\n",
      "56307 ['Převzato dopravcem', 'Na cestě k příjemci', 'Stornováno']\n"
     ]
    }
   ],
   "source": [
    "for i,j in new[0:2]:\n",
    "    print (i,j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,j in new:\n",
    "    cp_dict[i] = j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "450562"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cp_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "default = ['Převzato dopravcem', 'Na cestě k příjemci', 'Doručeno', 'Stornováno']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "message = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicts1 = [cp_dict, intime_dict, ppl_dict, dpd_dict, gw_dict, geis_dict, raben_dict, ulozenka_dict,\n",
    "         kuryr_dict, toptrans_dict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_nan(adict):\n",
    "    for j, i in adict.items():\n",
    "        for h in i:\n",
    "            if type(h) == float:\n",
    "                np.delete(i, h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check1(dict):\n",
    "    c = 0\n",
    "    for j, i in dict.items():\n",
    "        status_count = 0\n",
    "        for k in i:\n",
    "            \n",
    "            temp_len = len(i)\n",
    "            \n",
    "            if k == 'Doručeno' or k == 'Stornováno':\n",
    "\n",
    "                if status_count == temp_len-1:\n",
    "                    message.append(['Final state', j, k])\n",
    "            status_count +=1\n",
    "            \n",
    "        if 'Převzato dopravcem' not in i and 'Na cestě k příjemci' in i:\n",
    "\n",
    "            message.append(['Missing Převzato dopravcem state', j, k])\n",
    "        if default not in i and status_count == temp_len-1:\n",
    "\n",
    "            message.append(['Missing expected statuses', j, i])\n",
    "\n",
    "        if 'Doručeno' in i or 'Stornováno' in i:\n",
    "\n",
    "            if i[-1] not in ['Doručeno', 'Stornováno']:\n",
    "                message.append([\"New status after final\", j, i])\n",
    "            \n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "check1(cp_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {},
   "outputs": [],
   "source": [
    "check1(kuryr_dict, 'zavolejsikuryra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = pd.DataFrame(message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>New status after final</td>\n",
       "      <td>274189</td>\n",
       "      <td>[Na cestě k příjemci, Stornováno, Na cestě k p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>New status after final</td>\n",
       "      <td>280861</td>\n",
       "      <td>[Na cestě k příjemci, Stornováno, Na cestě k p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>New status after final</td>\n",
       "      <td>283417</td>\n",
       "      <td>[Na cestě k příjemci, Stornováno, Na cestě k p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2669</th>\n",
       "      <td>New status after final</td>\n",
       "      <td>391816</td>\n",
       "      <td>[Doručeno, Převzato dopravcem]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>New status after final</td>\n",
       "      <td>415620</td>\n",
       "      <td>[Převzato dopravcem, nan, Na cestě k příjemci,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0       1  \\\n",
       "1353  New status after final  274189   \n",
       "1374  New status after final  280861   \n",
       "1428  New status after final  283417   \n",
       "2669  New status after final  391816   \n",
       "2797  New status after final  415620   \n",
       "\n",
       "                                                      2  \n",
       "1353  [Na cestě k příjemci, Stornováno, Na cestě k p...  \n",
       "1374  [Na cestě k příjemci, Stornováno, Na cestě k p...  \n",
       "1428  [Na cestě k příjemci, Stornováno, Na cestě k p...  \n",
       "2669                     [Doručeno, Převzato dopravcem]  \n",
       "2797  [Převzato dopravcem, nan, Na cestě k příjemci,...  "
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[t[0] == 'New status after final'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = t.rename(columns={0 : 'message', 1 : 'tracking_id', 2 : 'detail'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "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>message</th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>detail</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Final state</td>\n",
       "      <td>53019</td>\n",
       "      <td>Stornováno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Final state</td>\n",
       "      <td>56307</td>\n",
       "      <td>Stornováno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Final state</td>\n",
       "      <td>113225</td>\n",
       "      <td>Doručeno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Missing Převzato dopravcem state</td>\n",
       "      <td>113225</td>\n",
       "      <td>Doručeno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Final state</td>\n",
       "      <td>113226</td>\n",
       "      <td>Doručeno</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            message  tracking_id      detail\n",
       "0                       Final state        53019  Stornováno\n",
       "1                       Final state        56307  Stornováno\n",
       "2                       Final state       113225    Doručeno\n",
       "3  Missing Převzato dopravcem state       113225    Doručeno\n",
       "4                       Final state       113226    Doručeno"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(232799, 3)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_state = t[[\"message\"]].groupby([t[\"message\"]]).count().sort_values(by='message', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "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>message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>message</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Missing Převzato dopravcem state</th>\n",
       "      <td>118535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Final state</th>\n",
       "      <td>104228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New status after final</th>\n",
       "      <td>10036</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  message\n",
       "message                                  \n",
       "Missing Převzato dopravcem state   118535\n",
       "Final state                        104228\n",
       "New status after final              10036"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "t.sort_values(by='message').to_csv('mall_pt/csv/all.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 985,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_state1 = t[[\"provider\", \"message\"]].groupby([t[\"message\"], t[\"provider\"]]).count().sort_values(by='message', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 986,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_state1.to_csv('mall_pt/csv/allg.csv', encoding='utf-8', index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 987,
   "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></th>\n",
       "      <th>provider</th>\n",
       "      <th>message</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>message</th>\n",
       "      <th>provider</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Missing Převzato dopravcem state</th>\n",
       "      <th>ceskaposta</th>\n",
       "      <td>27246</td>\n",
       "      <td>27246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New status after final</th>\n",
       "      <th>ceskaposta</th>\n",
       "      <td>18762</td>\n",
       "      <td>18762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missing expected statuses</th>\n",
       "      <th>ceskaposta</th>\n",
       "      <td>16218</td>\n",
       "      <td>16218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Final state</th>\n",
       "      <th>ceskaposta</th>\n",
       "      <td>15844</td>\n",
       "      <td>15844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             provider  message\n",
       "message                          provider                     \n",
       "Missing Převzato dopravcem state ceskaposta     27246    27246\n",
       "New status after final           ceskaposta     18762    18762\n",
       "Missing expected statuses        ceskaposta     16218    16218\n",
       "Final state                      ceskaposta     15844    15844"
      ]
     },
     "execution_count": 987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_state1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tracking_id</th>\n",
       "      <th>provider</th>\n",
       "      <th>status</th>\n",
       "      <th>mapped_status</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2056991</th>\n",
       "      <td>1402787</td>\n",
       "      <td>ceskaposta</td>\n",
       "      <td>Vrácena - odběrní lhůta</td>\n",
       "      <td>Stornováno</td>\n",
       "      <td>2017-12-27 10:29:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2057226</th>\n",
       "      <td>1402787</td>\n",
       "      <td>ceskaposta</td>\n",
       "      <td>Chybně směrována vinou pošty (zpět na SPU)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017-12-28 09:19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         tracking_id    provider                                      status  \\\n",
       "2056991      1402787  ceskaposta                     Vrácena - odběrní lhůta   \n",
       "2057226      1402787  ceskaposta  Chybně směrována vinou pošty (zpět na SPU)   \n",
       "\n",
       "        mapped_status                date  \n",
       "2056991    Stornováno 2017-12-27 10:29:00  \n",
       "2057226           NaN 2017-12-28 09:19:00  "
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[new_df[\"tracking_id\"] == 1402787].sort_values(by='date')"
   ]
  }
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