sales_data_process.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sales Data Processing "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1: Data Ingestion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date   Product  Quantity  Price\n",
      "0   2023-01-01  Widget A      10.0   15.5\n",
      "1   2023-01-01  Widget B       5.0   20.0\n",
      "2   2023-01-02  Widget A       8.0   15.5\n",
      "3   2023-01-02  Widget C       3.0   25.0\n",
      "4   2023-01-03  Widget A      15.0    NaN\n",
      "5   2023-01-03  Widget B       NaN   20.0\n",
      "6   2023-01-04  Widget C       7.0   25.0\n",
      "7   2023-01-05  Widget A      10.0   15.5\n",
      "8   2023-01-05  Widget B      10.0   20.0\n",
      "9   2023-01-06  Widget D       5.0   30.0\n",
      "10  2023-01-06  Widget E       3.0   40.0\n",
      "11  2023-01-07  Widget A      12.0   15.5\n",
      "12  2023-01-07  Widget C       6.0   25.0\n",
      "13  2023-01-08  Widget D       8.0   30.0\n",
      "14  2023-01-08  Widget B      10.0   20.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#load data from csv\n",
    "data = pd.read_csv(\"sales_data.csv\")\n",
    "\n",
    "#Display the data\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tep 2: Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date        0\n",
      "Product     0\n",
      "Quantity    1\n",
      "Price       1\n",
      "dtype: int64\n",
      "Date        0\n",
      "Product     0\n",
      "Quantity    0\n",
      "Price       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#check for missing values\n",
    "\n",
    "print(data.isnull().sum())\n",
    "\n",
    "#Fill missing  'Quantity' with 0, 'Price' with mean, and drop rows with missing 'Date' or 'Product'\n",
    "data['Quantity'].fillna(0, inplace=True)\n",
    "data['Price'].fillna(data['Price'].mean(), inplace=True)\n",
    "data.dropna(subset=['Date','Product'], inplace=True)\n",
    "\n",
    "#Remove duplicates\n",
    "data.drop_duplicates(inplace=True)\n",
    "\n",
    "#Verify the cleaning\n",
    "print(data.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 3: Data Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Date   Product  Quantity      Price  Total_Sales\n",
      "0   2023-01-01  Widget A      10.0  15.500000   155.000000\n",
      "1   2023-01-01  Widget B       5.0  20.000000   100.000000\n",
      "2   2023-01-02  Widget A       8.0  15.500000   124.000000\n",
      "3   2023-01-02  Widget C       3.0  25.000000    75.000000\n",
      "4   2023-01-03  Widget A      15.0  22.642857   339.642857\n",
      "5   2023-01-03  Widget B       0.0  20.000000     0.000000\n",
      "6   2023-01-04  Widget C       7.0  25.000000   175.000000\n",
      "7   2023-01-05  Widget A      10.0  15.500000   155.000000\n",
      "8   2023-01-05  Widget B      10.0  20.000000   200.000000\n",
      "9   2023-01-06  Widget D       5.0  30.000000   150.000000\n",
      "10  2023-01-06  Widget E       3.0  40.000000   120.000000\n",
      "11  2023-01-07  Widget A      12.0  15.500000   186.000000\n",
      "12  2023-01-07  Widget C       6.0  25.000000   150.000000\n",
      "13  2023-01-08  Widget D       8.0  30.000000   240.000000\n",
      "14  2023-01-08  Widget B      10.0  20.000000   200.000000\n"
     ]
    }
   ],
   "source": [
    "#Create a new coulmn for Total sales\n",
    "\n",
    "data['Total_Sales'] = data['Quantity'] * data['Price']\n",
    "\n",
    "#Display the transformed data\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Product  Total_Sales\n",
      "0  Widget A   959.642857\n",
      "1  Widget B   500.000000\n",
      "2  Widget C   400.000000\n",
      "3  Widget D   390.000000\n",
      "4  Widget E   120.000000\n"
     ]
    }
   ],
   "source": [
    "#Group by product and sum up total sales\n",
    "product_sales = data.groupby('Product')['Total_Sales'].sum().reset_index()\n",
    "\n",
    "#Display the sales per product\n",
    "print(product_sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the total sales per product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "\n",
    "#Plot total sales per product\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(product_sales['Product'], product_sales['Total_Sales'])\n",
    "plt.xlabel('Product')\n",
    "plt.ylabel('Total Sales')\n",
    "plt.title('Total Sales per Product')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 5: Save the Cleaned Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data saved to cleaned_sales_data.csv\n"
     ]
    }
   ],
   "source": [
    "#Save the cleaned and processed data to a new csv file\n",
    "\n",
    "data.to_csv(\"cleand_sales_data.csv\", index=False)\n",
    "\n",
    "print(\"Data saved to cleaned_sales_data.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}