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{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"id": "40d0c64c-1de1-47ed-aa66-7b28d9e8fd1f",
"metadata": {},
"outputs": [],
"source": [
"import pickle \n",
"import pandas as pd \n",
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5043d237-0287-4705-bfd5-73b880b36def",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_pickle('data/test_data.pkl')\n",
"df = df.loc[(df[\"Branch\"] == \"15\") & (df[\"Group\"].isin([\"6\",\"7\",\"4\",\"1\"]))]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "dce8096f-23d4-4075-8654-6693632c45bc",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" vertical-align: middle;\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sales</th>\n",
" <th>DayInYear</th>\n",
" <th>time_idx</th>\n",
" <th>Wahl</th>\n",
" <th>Baustelle</th>\n",
" <th>MontagLangesWE</th>\n",
" <th>FreitagLangesWE</th>\n",
" <th>nosale</th>\n",
" <th>holiday</th>\n",
" <th>AufSommerzeit</th>\n",
" <th>...</th>\n",
" <th>Branch</th>\n",
" <th>Weekday</th>\n",
" <th>Date</th>\n",
" <th>MTXWTH_Day_precip</th>\n",
" <th>MTXWTH_Temp_max</th>\n",
" <th>MTXWTH_Temp_min</th>\n",
" <th>Start</th>\n",
" <th>End</th>\n",
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"</div>"
],
"text/plain": [
" sales DayInYear time_idx Wahl Baustelle MontagLangesWE \\\n",
"270300 1600.9030 177 2369 0.0 0.0 0.0 \n",
"270301 1811.1958 178 2370 0.0 0.0 0.0 \n",
"270302 1784.2916 179 2371 0.0 0.0 0.0 \n",
"270303 1757.3488 180 2372 0.0 0.0 0.0 \n",
"270304 1741.0982 181 2373 0.0 0.0 0.0 \n",
"... ... ... ... ... ... ... \n",
"287065 1643.1700 173 2730 0.0 0.0 0.0 \n",
"287066 1597.3518 174 2731 0.0 0.0 0.0 \n",
"287067 1683.6228 175 2732 0.0 0.0 0.0 \n",
"287068 1785.2180 176 2733 0.0 0.0 0.0 \n",
"287069 1589.9020 177 2734 0.0 0.0 0.0 \n",
"\n",
" FreitagLangesWE nosale holiday AufSommerzeit ... Branch Weekday \\\n",
"270300 0.0 0 none 0.0 ... 15 6 \n",
"270301 0.0 0 none 0.0 ... 15 0 \n",
"270302 0.0 0 none 0.0 ... 15 1 \n",
"270303 0.0 0 none 0.0 ... 15 2 \n",
"270304 0.0 0 none 0.0 ... 15 3 \n",
"... ... ... ... ... ... ... ... \n",
"287065 0.0 0 none 0.0 ... 15 3 \n",
"287066 0.0 0 none 0.0 ... 15 4 \n",
"287067 0.0 0 none 0.0 ... 15 5 \n",
"287068 0.0 0 none 0.0 ... 15 6 \n",
"287069 0.0 0 none 0.0 ... 15 0 \n",
"\n",
" Date MTXWTH_Day_precip MTXWTH_Temp_max MTXWTH_Temp_min Start \\\n",
"270300 2022-06-26 0.0 28.52 17.47 7.0 \n",
"270301 2022-06-27 0.0 25.75 16.70 6.0 \n",
"270302 2022-06-28 0.0 23.57 14.17 6.0 \n",
"270303 2022-06-29 0.0 26.81 13.09 6.0 \n",
"270304 2022-06-30 0.0 27.26 15.00 6.0 \n",
"... ... ... ... ... ... \n",
"287065 2023-06-22 0.0 26.93 13.06 6.0 \n",
"287066 2023-06-23 1.0 23.99 15.98 6.0 \n",
"287067 2023-06-24 0.0 25.99 12.04 6.0 \n",
"287068 2023-06-25 0.0 28.99 15.02 7.0 \n",
"287069 2023-06-26 0.0 27.96 17.01 6.0 \n",
"\n",
" End ShiftLength weight \n",
"270300 10.983333 240.0 1 \n",
"270301 13.983333 480.0 1 \n",
"270302 13.983333 480.0 1 \n",
"270303 13.983333 480.0 1 \n",
"270304 13.983333 480.0 1 \n",
"... ... ... ... \n",
"287065 16.983333 660.0 1 \n",
"287066 16.983333 660.0 1 \n",
"287067 15.983333 600.0 1 \n",
"287068 15.983333 540.0 1 \n",
"287069 16.983333 660.0 1 \n",
"\n",
"[1464 rows x 22 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "5ea34c2b-fc24-4cfa-ad46-f369bea42364",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('2023-06-26 00:00:00')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(df[\"Date\"])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "a024d9e3-e018-43fe-9bb0-20b9d9e91b53",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime.date(2023, 5, 27)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datetime.date(2023, 6, 26) - datetime.timedelta(days = 30)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "52e0a2c8-5b53-42f4-91c9-3ca7d6a8d356",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['sales', 'DayInYear', 'time_idx', 'Wahl', 'Baustelle', 'MontagLangesWE',\n",
" 'FreitagLangesWE', 'nosale', 'holiday', 'AufSommerzeit',\n",
" 'AufWinterzeit', 'Group', 'Branch', 'Weekday', 'Date',\n",
" 'MTXWTH_Day_precip', 'MTXWTH_Temp_max', 'MTXWTH_Temp_min', 'Start',\n",
" 'End', 'ShiftLength', 'weight'],\n",
" dtype='object')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "75415b44-41ec-4893-8452-939766ebaabc",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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