pos_rooftop_profile.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "quarterly-compatibility",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import awswrangler as wr\n",
    "from datetime import datetime, timedelta\n",
    "import json\n",
    "import gzip\n",
    "from io import BytesIO, TextIOWrapper\n",
    "import boto3\n",
    "import dask.dataframe as dd\n",
    "import plotly.express as px\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "higher-tutorial",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_rooftop_day=pd.read_csv(\"pos_rooftop_day.csv.gz\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "emotional-officer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['calendar_date', 'pfcntr', 'dma_name', 'dma_code', 'week_end_sat',\n",
       "       'ordr_type_name', 'gross_sales_amt', 'net_sales_amt', 'check_cnt'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_rooftop_day.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "alien-module",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calendar_date</th>\n",
       "      <th>pfcntr</th>\n",
       "      <th>dma_name</th>\n",
       "      <th>dma_code</th>\n",
       "      <th>week_end_sat</th>\n",
       "      <th>ordr_type_name</th>\n",
       "      <th>gross_sales_amt</th>\n",
       "      <th>net_sales_amt</th>\n",
       "      <th>check_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>39923929</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39923930</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>39923931</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39923933 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         calendar_date  pfcntr dma_name  dma_code week_end_sat ordr_type_name  \\\n",
       "0                  NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "1                  NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "2                  NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "3                  NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "4                  NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "...                ...     ...      ...       ...          ...            ...   \n",
       "39923928           NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "39923929           NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "39923930           NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "39923931           NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "39923932           NaN     NaN      NaN       NaN          NaN            NaN   \n",
       "\n",
       "          gross_sales_amt  net_sales_amt  check_cnt  \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  \n",
       "...                   ...            ...        ...  \n",
       "39923928              NaN            NaN        NaN  \n",
       "39923929              NaN            NaN        NaN  \n",
       "39923930              NaN            NaN        NaN  \n",
       "39923931              NaN            NaN        NaN  \n",
       "39923932              NaN            NaN        NaN  \n",
       "\n",
       "[39923933 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_rooftop_day[pos_rooftop_day.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "labeled-schema",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_missing=pos_rooftop_day[pos_rooftop_day['gross_sales_amt']==0.00].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "coated-bubble",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4830675 entries, 49 to 39923894\n",
      "Data columns (total 9 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   calendar_date    object \n",
      " 1   pfcntr           int64  \n",
      " 2   dma_name         object \n",
      " 3   dma_code         int64  \n",
      " 4   week_end_sat     object \n",
      " 5   ordr_type_name   object \n",
      " 6   gross_sales_amt  float64\n",
      " 7   net_sales_amt    float64\n",
      " 8   check_cnt        float64\n",
      "dtypes: float64(3), int64(2), object(4)\n",
      "memory usage: 368.6+ MB\n"
     ]
    }
   ],
   "source": [
    "pos_missing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "collected-chemistry",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_missing.drop(columns=['dma_name', 'dma_code'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "organic-patio",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>49</th>\n",
       "      <td>2018-07-02</td>\n",
       "      <td>300176</td>\n",
       "      <td>2018-07-07</td>\n",
       "      <td>otg_dt</td>\n",
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       "      <td>2018-07-07</td>\n",
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       "      <th>93</th>\n",
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       "      <td>2018-12-15</td>\n",
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       "      <th>186</th>\n",
       "      <td>2019-10-09</td>\n",
       "      <td>300176</td>\n",
       "      <td>2019-10-12</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>14643</th>\n",
       "      <td>2020-11-21</td>\n",
       "      <td>300296</td>\n",
       "      <td>2020-11-21</td>\n",
       "      <td>otg_walk_in</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>14644</th>\n",
       "      <td>2020-11-21</td>\n",
       "      <td>300296</td>\n",
       "      <td>2020-11-21</td>\n",
       "      <td>otg_dt</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>14647</th>\n",
       "      <td>2020-11-25</td>\n",
       "      <td>300296</td>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>otg_walk_in</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>2020-11-25</td>\n",
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       "      <td>2020-11-28</td>\n",
       "      <td>otg_dt</td>\n",
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       "      <td>2018-05-29</td>\n",
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       "      <td>2018-06-02</td>\n",
       "      <td>otg_dt</td>\n",
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       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      calendar_date  pfcntr week_end_sat ordr_type_name  gross_sales_amt  \\\n",
       "49       2018-07-02  300176   2018-07-07         otg_dt              0.0   \n",
       "53       2018-07-02  300176   2018-07-07         otg_dt              0.0   \n",
       "89       2018-12-13  300176   2018-12-15         otg_dt              0.0   \n",
       "93       2018-12-13  300176   2018-12-15         otg_dt              0.0   \n",
       "186      2019-10-09  300176   2019-10-12            NaN              0.0   \n",
       "...             ...     ...          ...            ...              ...   \n",
       "14643    2020-11-21  300296   2020-11-21    otg_walk_in              0.0   \n",
       "14644    2020-11-21  300296   2020-11-21         otg_dt              0.0   \n",
       "14647    2020-11-25  300296   2020-11-28    otg_walk_in              0.0   \n",
       "14648    2020-11-25  300296   2020-11-28         otg_dt              0.0   \n",
       "14670    2018-05-29  300297   2018-06-02         otg_dt              0.0   \n",
       "\n",
       "       net_sales_amt  check_cnt  \n",
       "49               0.0        0.0  \n",
       "53               0.0        0.0  \n",
       "89               0.0        0.0  \n",
       "93               0.0        0.0  \n",
       "186              0.0        0.0  \n",
       "...              ...        ...  \n",
       "14643            0.0        0.0  \n",
       "14644            0.0        0.0  \n",
       "14647            0.0        0.0  \n",
       "14648            0.0        0.0  \n",
       "14670            0.0        0.0  \n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_missing.head(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "mexican-knife",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_missing=pd.DataFrame(pos_missing.groupby(['week_end_sat', 'pfcntr']).sum().to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "compliant-bikini",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2021-01-02</td>\n",
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       "    <tr>\n",
       "      <th>715207</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>360679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715208</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>361797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715209</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>361816</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715210</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>362441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>715211 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       week_end_sat  pfcntr  gross_sales_amt  net_sales_amt  check_cnt\n",
       "0        2018-01-06  300178              0.0            0.0        0.0\n",
       "1        2018-01-06  300202              0.0            0.0        0.0\n",
       "2        2018-01-06  300205              0.0            0.0        0.0\n",
       "3        2018-01-06  300208              0.0            0.0        0.0\n",
       "4        2018-01-06  300215              0.0            0.0        0.0\n",
       "...             ...     ...              ...            ...        ...\n",
       "715206   2021-01-02  359678              0.0            0.0        0.0\n",
       "715207   2021-01-02  360679              0.0            0.0        0.0\n",
       "715208   2021-01-02  361797              0.0            0.0        0.0\n",
       "715209   2021-01-02  361816              0.0            0.0        0.0\n",
       "715210   2021-01-02  362441              0.0            0.0        1.0\n",
       "\n",
       "[715211 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "alternate-spyware",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 715211 entries, 0 to 715210\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   week_end_sat     715211 non-null  object \n",
      " 1   pfcntr           715211 non-null  int32  \n",
      " 2   gross_sales_amt  715211 non-null  float64\n",
      " 3   net_sales_amt    715211 non-null  float64\n",
      " 4   check_cnt        715211 non-null  float64\n",
      "dtypes: float64(3), int32(1), object(1)\n",
      "memory usage: 24.6+ MB\n"
     ]
    }
   ],
   "source": [
    "pos_missing.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "received-rehabilitation",
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_missing.to_csv(\"pos_rooftop_missing.csv\", index=False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aquatic-detection",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "sound-compression",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2021-01-02</td>\n",
       "      <td>362441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>715211 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       week_end_sat  pfcntr  gross_sales_amt  net_sales_amt  check_cnt\n",
       "0        2018-01-06  300178              0.0            0.0        0.0\n",
       "1        2018-01-06  300202              0.0            0.0        0.0\n",
       "2        2018-01-06  300205              0.0            0.0        0.0\n",
       "3        2018-01-06  300208              0.0            0.0        0.0\n",
       "4        2018-01-06  300215              0.0            0.0        0.0\n",
       "...             ...     ...              ...            ...        ...\n",
       "715206   2021-01-02  359678              0.0            0.0        0.0\n",
       "715207   2021-01-02  360679              0.0            0.0        0.0\n",
       "715208   2021-01-02  361797              0.0            0.0        0.0\n",
       "715209   2021-01-02  361816              0.0            0.0        0.0\n",
       "715210   2021-01-02  362441              0.0            0.0        1.0\n",
       "\n",
       "[715211 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "american-threshold",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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