Merge Historical sales 20-22.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa6a984e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import date, datetime\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e6dd1365",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sales2020 = pd.read_csv('Historical_sales2020.csv')\n",
    "Sales2021 = pd.read_csv('Historical_sales2021.csv')\n",
    "Sales2022 = pd.read_csv('Historical_sales2022.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb0bfbb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sales2021['unit_price'] = Sales2021['unit_price'].str.replace(',','.').astype(float)\n",
    "Sales2020['unit_price'] = Sales2020['unit_price'].str.replace(',','.').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61e94f9d",
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   "outputs": [
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       "        transaction_id transaction_date transaction_time  store_id  staff_id  \\\n",
       "0                  363        1/15/2020         11:17:45         8         6   \n",
       "1                  363        1/15/2020         11:17:45         8         6   \n",
       "2                 1812        1/15/2020         17:14:59         8        43   \n",
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       "...                ...              ...              ...       ...       ...   \n",
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       "\n",
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     "execution_count": 4,
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   "source": [
    "Sales20_22 = pd.concat([Sales2020, Sales2021, Sales2022], ignore_index=True)\n",
    "Sales20_22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b6220b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sales20_22['transaction_date'] = pd.to_datetime(Sales20_22['transaction_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "643f1c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "Sales20_22.to_csv('Historical_sales.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82eb388a",
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       "        transaction_id transaction_date transaction_time  store_id  staff_id  \\\n",
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       "1                  363       2020-01-15         11:17:45         8         6   \n",
       "2                 1812       2020-01-15         17:14:59         8        43   \n",
       "3                 1812       2020-01-15         17:14:59         8        43   \n",
       "4                 1597       2020-01-15         19:17:28         8        45   \n",
       "...                ...              ...              ...       ...       ...   \n",
       "553310             626       2022-04-29         19:57:00         8        15   \n",
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       "        customer_id  product_id  quantity_sold  unit_price promo_item_yn  \n",
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       "4              8059          87              1        2.10             Y  \n",
       "...             ...         ...            ...         ...           ...  \n",
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       "553311         8440          77              1        3.00             N  \n",
       "553312         1000          52              2        2.50             N  \n",
       "553313         1000          51              2        3.00             N  \n",
       "553314         8600          30              1        3.00             N  \n",
       "\n",
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     "execution_count": 10,
     "metadata": {},
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   ],
   "source": [
    "pd.read_csv('Historical_sales.csv')"
   ]
  }
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