Workearly_Final_Assignment_Panagiotis_Chatzinikolaou.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing liquor store sales data using Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Author: Panagiotis Chatzinikolaou\n",
    "- e-mail: chatzinp@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tasks: \n",
    "- For the timeframe 2016-2019:\n",
    "    1. Discern the most popular item in each zipcode\n",
    "    2. Compute the sales percentage per store (in dollars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objectives:\n",
    "- *Data Extraction and Cleaning*: Extract pertinent data and conduct preliminary cleaning to handle missing or inconsistent data.\n",
    "- *Data Analysis*: Employ Python with Pandas or SQL to analyze the dataset, identifying the predominant item per zipcode and calculating the proportion of sales for each store between 2016 and 2019.\n",
    "- *Data Visualization*: Represent the analyzed data aesthetically and informatively using tools like matplotlib, seaborn, plotly, or integrate with visualization platforms such as Tableau Public, Power BI, Looker Studio, or HEX for a more interactive experience.\n",
    "\n",
    "PS. You will only need 1 type of graph for the solution, but feel free to experiment with different graphs and formats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Constraints:\n",
    "- All data computations, transformations, and manipulations must be executed via Python scripts.\n",
    "- The visual representation should succinctly convey the findings, maintaining a balance between detail and clarity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deliverables:\n",
    "- **Analytical Report**: A comprehensive report detailing the methodologies used, the analysis performed, and the insights derived as well as the code.\n",
    "- **Visualization Dashboard**: An interactive dashboard or a set of plots/graphs using one of the prescribed tools, encapsulating the key findings and insights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Workflow:\n",
    "- *Data Preparation*: Extract, clean, and prepare the dataset for analysis.\n",
    "- *Exploratory Data Analysis (EDA)*: Explore the dataset to understand its structure, trends, and patterns.\n",
    "- *Data Manipulation*: Use Python and Pandas for data transformation and computation to derive insights.\n",
    "- *Visualization and Presentation*: Present the results in a visually appealing and informative manner using suitable visualization tools."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this assignment I used pandas for data manipulation, as well as matplotlib, seaborn and plotly for data visualization.\n",
    "All code was created in VS Code in a Jupyter notebook (ipynb)."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "# Install required packages for the project\n",
    "pip install pandas\n",
    "pip install matplotlib\n",
    "pip install seaborn\n",
    "pip install plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, I followed the next steps to prepare the dataset:\n",
    "- Extract the data from the csv file using pandas and read it to a Pandas DataFrame.\n",
    "- Check for any missing data and drop them.\n",
    "- Check the data types of the dataset and make any necessary conversions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the data from the CSV file\n",
    "df = pd.read_csv('https://storage.googleapis.com/courses_data/Assignment%20CSV/finance_liquor_sales.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Data:\n",
      " invoice_and_item_number     0\n",
      "date                        0\n",
      "store_number                0\n",
      "store_name                  0\n",
      "address                     0\n",
      "city                        0\n",
      "zip_code                    0\n",
      "store_location             18\n",
      "county_number               1\n",
      "county                      1\n",
      "category                    3\n",
      "category_name               9\n",
      "vendor_number               0\n",
      "vendor_name                 0\n",
      "item_number                 0\n",
      "item_description            0\n",
      "pack                        0\n",
      "bottle_volume_ml            0\n",
      "state_bottle_cost           0\n",
      "state_bottle_retail         0\n",
      "bottles_sold                0\n",
      "sale_dollars                0\n",
      "volume_sold_liters          0\n",
      "volume_sold_gallons         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing data\n",
    "print(\"Missing Data:\\n\", df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item Number for missing store location:  2       41844\n",
      "39      49182\n",
      "55      78115\n",
      "56      15644\n",
      "59      84617\n",
      "80      56193\n",
      "82      36908\n",
      "94      77487\n",
      "103     43031\n",
      "119     82187\n",
      "128     43034\n",
      "147    101181\n",
      "151     76436\n",
      "171     28868\n",
      "174     77805\n",
      "175     67524\n",
      "183     86112\n",
      "199     33717\n",
      "Name: item_number, dtype: int64\n",
      "\n",
      "Item Number for missing county number:  66    905153\n",
      "Name: item_number, dtype: int64\n",
      "\n",
      "Item Number for missing category name:  21     100026\n",
      "37      48106\n",
      "38      48099\n",
      "39      49182\n",
      "61      48099\n",
      "69      84197\n",
      "102     84197\n",
      "153     48099\n",
      "181     48099\n",
      "Name: item_number, dtype: int64\n",
      "\n",
      "Item Number for missing category:  21     100026\n",
      "69      84197\n",
      "102     84197\n",
      "Name: item_number, dtype: int64\n",
      "\n",
      "Item Number for missing county:  66    905153\n",
      "Name: item_number, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Find the item_number of the missing store_location\n",
    "#df['item_number'].isna()\n",
    "store_location_na = df[df['store_location'].isna()][\"item_number\"]\n",
    "\n",
    "# Find the item number of the missing county number\n",
    "county_numb_na = df[df['county_number'].isna()][\"item_number\"]\n",
    "\n",
    "# Find the item number of the missing category name\n",
    "cat_name_na = df[df['category_name'].isna()][\"item_number\"]\n",
    "\n",
    "# Find the item number of the missing category\n",
    "category_na = df[df['category'].isna()][\"item_number\"]\n",
    "\n",
    "# Find the item number of the missing county\n",
    "county_na = df[df['county'].isna()][\"item_number\"]\n",
    "\n",
    "# Print the item number for each missing data\n",
    "print(\"Item Number for missing store location: \", store_location_na)\n",
    "print(\"\")\n",
    "print(\"Item Number for missing county number: \", county_numb_na)\n",
    "print(\"\")\n",
    "print(\"Item Number for missing category name: \", cat_name_na)\n",
    "print(\"\")\n",
    "print(\"Item Number for missing category: \", category_na)\n",
    "print(\"\")\n",
    "print(\"Item Number for missing county: \", county_na)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 173 entries, 0 to 198\n",
      "Data columns (total 24 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   invoice_and_item_number  173 non-null    object        \n",
      " 1   date                     173 non-null    datetime64[ns]\n",
      " 2   store_number             173 non-null    int64         \n",
      " 3   store_name               173 non-null    object        \n",
      " 4   address                  173 non-null    object        \n",
      " 5   city                     173 non-null    object        \n",
      " 6   zip_code                 173 non-null    float64       \n",
      " 7   store_location           173 non-null    object        \n",
      " 8   county_number            173 non-null    float64       \n",
      " 9   county                   173 non-null    object        \n",
      " 10  category                 173 non-null    float64       \n",
      " 11  category_name            173 non-null    object        \n",
      " 12  vendor_number            173 non-null    float64       \n",
      " 13  vendor_name              173 non-null    object        \n",
      " 14  item_number              173 non-null    int64         \n",
      " 15  item_description         173 non-null    object        \n",
      " 16  pack                     173 non-null    int64         \n",
      " 17  bottle_volume_ml         173 non-null    int64         \n",
      " 18  state_bottle_cost        173 non-null    float64       \n",
      " 19  state_bottle_retail      173 non-null    float64       \n",
      " 20  bottles_sold             173 non-null    int64         \n",
      " 21  sale_dollars             173 non-null    float64       \n",
      " 22  volume_sold_liters       173 non-null    float64       \n",
      " 23  volume_sold_gallons      173 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(9), int64(5), object(9)\n",
      "memory usage: 33.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data types of the columns\n",
    "df.dtypes\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing the data types we notice that the dataset consists of 24 columns and 173 rows. \n",
    "Also, the date variable is saved as an object. So we will convert it to a datetime type variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the date column to datetime\n",
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, I used some basic functions to further explore the liquor sales dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['invoice_and_item_number', 'date', 'store_number', 'store_name',\n",
       "       'address', 'city', 'zip_code', 'store_location', 'county_number',\n",
       "       'county', 'category', 'category_name', 'vendor_number', 'vendor_name',\n",
       "       'item_number', 'item_description', 'pack', 'bottle_volume_ml',\n",
       "       'state_bottle_cost', 'state_bottle_retail', 'bottles_sold',\n",
       "       'sale_dollars', 'volume_sold_liters', 'volume_sold_gallons'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Column names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>invoice_and_item_number</th>\n",
       "      <th>date</th>\n",
       "      <th>store_number</th>\n",
       "      <th>store_name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>store_location</th>\n",
       "      <th>county_number</th>\n",
       "      <th>county</th>\n",
       "      <th>...</th>\n",
       "      <th>item_number</th>\n",
       "      <th>item_description</th>\n",
       "      <th>pack</th>\n",
       "      <th>bottle_volume_ml</th>\n",
       "      <th>state_bottle_cost</th>\n",
       "      <th>state_bottle_retail</th>\n",
       "      <th>bottles_sold</th>\n",
       "      <th>sale_dollars</th>\n",
       "      <th>volume_sold_liters</th>\n",
       "      <th>volume_sold_gallons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INV-31797900035</td>\n",
       "      <td>2020-11-10</td>\n",
       "      <td>4967</td>\n",
       "      <td>Jeff's Market / Blue Grass</td>\n",
       "      <td>102, W Mayne St</td>\n",
       "      <td>Blue Grass</td>\n",
       "      <td>52726.0</td>\n",
       "      <td>POINT (-90.766126 41.509119)</td>\n",
       "      <td>82.0</td>\n",
       "      <td>SCOTT</td>\n",
       "      <td>...</td>\n",
       "      <td>26858</td>\n",
       "      <td>Templeton 4YR Rye</td>\n",
       "      <td>12</td>\n",
       "      <td>375</td>\n",
       "      <td>9.99</td>\n",
       "      <td>14.99</td>\n",
       "      <td>1</td>\n",
       "      <td>14.99</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>INV-23548800092</td>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>2601</td>\n",
       "      <td>Hy-Vee Food Store / Fairfield</td>\n",
       "      <td>1300 West Burlington Ave</td>\n",
       "      <td>Fairfield</td>\n",
       "      <td>52556.0</td>\n",
       "      <td>POINT (-91.978702 41.006456)</td>\n",
       "      <td>51.0</td>\n",
       "      <td>JEFFERSON</td>\n",
       "      <td>...</td>\n",
       "      <td>86251</td>\n",
       "      <td>Juarez Triple Sec</td>\n",
       "      <td>12</td>\n",
       "      <td>1000</td>\n",
       "      <td>2.42</td>\n",
       "      <td>3.63</td>\n",
       "      <td>6</td>\n",
       "      <td>21.78</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INV-39482900037</td>\n",
       "      <td>2021-08-24</td>\n",
       "      <td>3719</td>\n",
       "      <td>Wal-Mart 0581 / Marshalltown</td>\n",
       "      <td>2802 S Center St</td>\n",
       "      <td>Marshalltown</td>\n",
       "      <td>50158.0</td>\n",
       "      <td>POINT (-92.91231 42.012918)</td>\n",
       "      <td>64.0</td>\n",
       "      <td>MARSHALL</td>\n",
       "      <td>...</td>\n",
       "      <td>65259</td>\n",
       "      <td>Jagermeister Liqueur Mini Meisters</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>4.93</td>\n",
       "      <td>7.40</td>\n",
       "      <td>12</td>\n",
       "      <td>88.80</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INV-39520400088</td>\n",
       "      <td>2021-08-25</td>\n",
       "      <td>5423</td>\n",
       "      <td>Stammer Liquor Corp</td>\n",
       "      <td>615 2nd Ave</td>\n",
       "      <td>Sheldon</td>\n",
       "      <td>51201.0</td>\n",
       "      <td>POINT (-95.854899 43.184242)</td>\n",
       "      <td>71.0</td>\n",
       "      <td>OBRIEN</td>\n",
       "      <td>...</td>\n",
       "      <td>77487</td>\n",
       "      <td>Tortilla Gold DSS</td>\n",
       "      <td>12</td>\n",
       "      <td>1000</td>\n",
       "      <td>4.85</td>\n",
       "      <td>7.28</td>\n",
       "      <td>4</td>\n",
       "      <td>29.12</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>INV-29941700050</td>\n",
       "      <td>2020-09-02</td>\n",
       "      <td>3842</td>\n",
       "      <td>Bancroft Liquor Store</td>\n",
       "      <td>107 N Portland St Po Bx 222</td>\n",
       "      <td>Bancroft</td>\n",
       "      <td>50517.0</td>\n",
       "      <td>POINT (-94.218 43.29355)</td>\n",
       "      <td>55.0</td>\n",
       "      <td>KOSSUTH</td>\n",
       "      <td>...</td>\n",
       "      <td>77055</td>\n",
       "      <td>Sir Winston Peach</td>\n",
       "      <td>6</td>\n",
       "      <td>750</td>\n",
       "      <td>13.01</td>\n",
       "      <td>19.52</td>\n",
       "      <td>18</td>\n",
       "      <td>351.36</td>\n",
       "      <td>13.50</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoice_and_item_number       date  store_number  \\\n",
       "0         INV-31797900035 2020-11-10          4967   \n",
       "1         INV-23548800092 2019-11-27          2601   \n",
       "3         INV-39482900037 2021-08-24          3719   \n",
       "4         INV-39520400088 2021-08-25          5423   \n",
       "5         INV-29941700050 2020-09-02          3842   \n",
       "\n",
       "                      store_name                      address          city  \\\n",
       "0     Jeff's Market / Blue Grass              102, W Mayne St    Blue Grass   \n",
       "1  Hy-Vee Food Store / Fairfield     1300 West Burlington Ave     Fairfield   \n",
       "3   Wal-Mart 0581 / Marshalltown             2802 S Center St  Marshalltown   \n",
       "4            Stammer Liquor Corp                  615 2nd Ave       Sheldon   \n",
       "5          Bancroft Liquor Store  107 N Portland St Po Bx 222      Bancroft   \n",
       "\n",
       "   zip_code                store_location  county_number     county  ...  \\\n",
       "0   52726.0  POINT (-90.766126 41.509119)           82.0      SCOTT  ...   \n",
       "1   52556.0  POINT (-91.978702 41.006456)           51.0  JEFFERSON  ...   \n",
       "3   50158.0   POINT (-92.91231 42.012918)           64.0   MARSHALL  ...   \n",
       "4   51201.0  POINT (-95.854899 43.184242)           71.0     OBRIEN  ...   \n",
       "5   50517.0      POINT (-94.218 43.29355)           55.0    KOSSUTH  ...   \n",
       "\n",
       "   item_number                    item_description  pack bottle_volume_ml  \\\n",
       "0        26858                   Templeton 4YR Rye    12              375   \n",
       "1        86251                   Juarez Triple Sec    12             1000   \n",
       "3        65259  Jagermeister Liqueur Mini Meisters    12               20   \n",
       "4        77487                   Tortilla Gold DSS    12             1000   \n",
       "5        77055                   Sir Winston Peach     6              750   \n",
       "\n",
       "   state_bottle_cost state_bottle_retail  bottles_sold  sale_dollars  \\\n",
       "0               9.99               14.99             1         14.99   \n",
       "1               2.42                3.63             6         21.78   \n",
       "3               4.93                7.40            12         88.80   \n",
       "4               4.85                7.28             4         29.12   \n",
       "5              13.01               19.52            18        351.36   \n",
       "\n",
       "   volume_sold_liters  volume_sold_gallons  \n",
       "0                0.37                 0.09  \n",
       "1                6.00                 1.58  \n",
       "3                0.24                 0.06  \n",
       "4                4.00                 1.05  \n",
       "5               13.50                 3.56  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the first few rows of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>date</th>\n",
       "      <th>store_number</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>county_number</th>\n",
       "      <th>category</th>\n",
       "      <th>vendor_number</th>\n",
       "      <th>item_number</th>\n",
       "      <th>pack</th>\n",
       "      <th>bottle_volume_ml</th>\n",
       "      <th>state_bottle_cost</th>\n",
       "      <th>state_bottle_retail</th>\n",
       "      <th>bottles_sold</th>\n",
       "      <th>sale_dollars</th>\n",
       "      <th>volume_sold_liters</th>\n",
       "      <th>volume_sold_gallons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>173</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>1.730000e+02</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017-08-23 18:35:22.543352576</td>\n",
       "      <td>3647.624277</td>\n",
       "      <td>51406.271676</td>\n",
       "      <td>56.161850</td>\n",
       "      <td>1.174537e+06</td>\n",
       "      <td>277.421965</td>\n",
       "      <td>100839.456647</td>\n",
       "      <td>10.982659</td>\n",
       "      <td>879.739884</td>\n",
       "      <td>10.363757</td>\n",
       "      <td>15.549827</td>\n",
       "      <td>59.716763</td>\n",
       "      <td>778.680116</td>\n",
       "      <td>57.004971</td>\n",
       "      <td>15.057283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2012-01-17 00:00:00</td>\n",
       "      <td>2178.000000</td>\n",
       "      <td>50009.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.011300e+06</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>1.340000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2014-12-18 00:00:00</td>\n",
       "      <td>2593.000000</td>\n",
       "      <td>50401.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.051110e+06</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>28868.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>750.000000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>7.130000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>50.820000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017-06-28 00:00:00</td>\n",
       "      <td>3447.000000</td>\n",
       "      <td>51401.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>1.081330e+06</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>67522.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.390000</td>\n",
       "      <td>12.590000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>217.800000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>3.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020-09-16 00:00:00</td>\n",
       "      <td>4319.000000</td>\n",
       "      <td>52317.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.091300e+06</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>82847.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>813.900000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>15.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022-06-28 00:00:00</td>\n",
       "      <td>9001.000000</td>\n",
       "      <td>52807.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>1.901200e+06</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>986845.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1750.000000</td>\n",
       "      <td>34.990000</td>\n",
       "      <td>52.490000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>7240.860000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>237.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1237.073414</td>\n",
       "      <td>944.644489</td>\n",
       "      <td>26.700913</td>\n",
       "      <td>2.556170e+05</td>\n",
       "      <td>153.748439</td>\n",
       "      <td>200713.107601</td>\n",
       "      <td>6.697339</td>\n",
       "      <td>426.273832</td>\n",
       "      <td>7.068157</td>\n",
       "      <td>10.603641</td>\n",
       "      <td>115.172901</td>\n",
       "      <td>1281.964148</td>\n",
       "      <td>119.504567</td>\n",
       "      <td>31.569571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                date  store_number      zip_code  \\\n",
       "count                            173    173.000000    173.000000   \n",
       "mean   2017-08-23 18:35:22.543352576   3647.624277  51406.271676   \n",
       "min              2012-01-17 00:00:00   2178.000000  50009.000000   \n",
       "25%              2014-12-18 00:00:00   2593.000000  50401.000000   \n",
       "50%              2017-06-28 00:00:00   3447.000000  51401.000000   \n",
       "75%              2020-09-16 00:00:00   4319.000000  52317.000000   \n",
       "max              2022-06-28 00:00:00   9001.000000  52807.000000   \n",
       "std                              NaN   1237.073414    944.644489   \n",
       "\n",
       "       county_number      category  vendor_number    item_number        pack  \\\n",
       "count     173.000000  1.730000e+02     173.000000     173.000000  173.000000   \n",
       "mean       56.161850  1.174537e+06     277.421965  100839.456647   10.982659   \n",
       "min         3.000000  1.011300e+06      35.000000     168.000000    3.000000   \n",
       "25%        43.000000  1.051110e+06      85.000000   28868.000000    6.000000   \n",
       "50%        57.000000  1.081330e+06     300.000000   67522.000000   12.000000   \n",
       "75%        77.000000  1.091300e+06     421.000000   82847.000000   12.000000   \n",
       "max        97.000000  1.901200e+06     662.000000  986845.000000   48.000000   \n",
       "std        26.700913  2.556170e+05     153.748439  200713.107601    6.697339   \n",
       "\n",
       "       bottle_volume_ml  state_bottle_cost  state_bottle_retail  bottles_sold  \\\n",
       "count        173.000000         173.000000           173.000000    173.000000   \n",
       "mean         879.739884          10.363757            15.549827     59.716763   \n",
       "min           20.000000           0.890000             1.340000      1.000000   \n",
       "25%          750.000000           4.750000             7.130000      4.000000   \n",
       "50%         1000.000000           8.390000            12.590000     24.000000   \n",
       "75%         1000.000000          15.000000            22.500000     72.000000   \n",
       "max         1750.000000          34.990000            52.490000    900.000000   \n",
       "std          426.273832           7.068157            10.603641    115.172901   \n",
       "\n",
       "       sale_dollars  volume_sold_liters  volume_sold_gallons  \n",
       "count    173.000000          173.000000           173.000000  \n",
       "mean     778.680116           57.004971            15.057283  \n",
       "min        6.750000            0.150000             0.030000  \n",
       "25%       50.820000            4.000000             1.060000  \n",
       "50%      217.800000           13.500000             3.560000  \n",
       "75%      813.900000           60.000000            15.850000  \n",
       "max     7240.860000          900.000000           237.750000  \n",
       "std     1281.964148          119.504567            31.569571  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate basic statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note: We notice that the earlieast year of the dataset is 2012 and the latest is 2022. For this assignment, we need only the dates between 2016-2019. So we will filter the dataset for the given timeframe.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the data to only include the data between 2016 and 2019\n",
    "# Using base Python\n",
    "#df_clean = df[(df['date'] >= '2016-01-01') & (df['date'] <= '2019-12-31')]\n",
    "\n",
    "# Using pandas query\n",
    "df_clean = df.query('date >= \"2016-01-01\" and date <= \"2019-12-31\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Earliest date is: \n",
      " 2016-01-13 00:00:00\n",
      "\n",
      "Latest date is: \n",
      " 2019-12-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Print the earliest and latest date in the filtered data\n",
    "print(\"Earliest date is: \\n\", df_clean[\"date\"].min())\n",
    "print(\"\")\n",
    "print(\"Latest date is: \\n\", df_clean[\"date\"].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The aim of this task is to identify the most popular item per zip code. Thus, we first need to find which item was sold the most in each zipcode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   zip_code  item_number  bottles_sold\n",
      "0   50010.0       946574           288\n",
      "1   50022.0        86507             4\n",
      "2   50111.0        77805           108\n",
      "3   50131.0        38089            48\n",
      "4   50158.0        48099            24\n",
      "5   50158.0        67528             3\n",
      "6   50158.0        67557             6\n",
      "7   50263.0         3135            84\n",
      "8   50265.0        67526            72\n",
      "9   50266.0          250            90\n"
     ]
    }
   ],
   "source": [
    "# Group by zip_code and item_description, then sum the bottles_sold\n",
    "most_popular = df_clean.groupby(['zip_code', 'item_number'])['bottles_sold'].sum().reset_index()\n",
    "\n",
    "# Display the top ten most popular items\n",
    "print(most_popular.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    zip_code  item_number  bottles_sold\n",
      "62   52314.0        75087          1560\n",
      "30   50702.0        77487           768\n",
      "0    50010.0       946574           288\n",
      "39   51106.0        67527           240\n",
      "13   50314.0        86251           240\n",
      "65   52402.0        86390           216\n",
      "33   50703.0          168           180\n",
      "41   51106.0        86251           144\n",
      "19   50320.0       973627           120\n",
      "2    50111.0        77805           108\n"
     ]
    }
   ],
   "source": [
    "# sort the data by bottles_sold in descending order\n",
    "most_popular_sorted = most_popular.sort_values(by='bottles_sold', ascending=False)\n",
    "\n",
    "# Display the top ten most popular items\n",
    "print(most_popular_sorted.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we found the most popular item per zip code, it is time to plot the new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we are going to use matplotlib to create a scatterplot of bottles sold and zipcode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the figure\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Loop through each row and plot each data point using the index as the x-axis\n",
    "for index, row in most_popular.iterrows():\n",
    "    plt.scatter(index, row['bottles_sold'], label=f\"Item {row['item_number']}\")\n",
    "    \n",
    "    # Annotate item_number if bottles_sold > 250, no decimals\n",
    "    if row['bottles_sold'] > 220:\n",
    "        plt.annotate(\n",
    "            f'{int(row[\"item_number\"])}',  # Convert item_number to integer to remove decimals\n",
    "            (index, row['bottles_sold']),\n",
    "            textcoords=\"offset points\", \n",
    "            xytext=(0, 5), \n",
    "            ha=\"center\"\n",
    "        )\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Zipcode')\n",
    "plt.ylabel('Bottles Sold')\n",
    "plt.title('Bottles Sold in each Zipcode')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for fun, we are going to replicate the plot using seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the plot\n",
    "\n",
    "# Create scatter plot using seaborn with all points in blue and no legend\n",
    "sns.scatterplot(\n",
    "    x=most_popular.index, \n",
    "    y='bottles_sold', \n",
    "    data=most_popular, \n",
    "    color='blue',   # Color all points blue\n",
    "    s=50            # Increase size of points to 50\n",
    ")\n",
    "\n",
    "# Annotate points where bottles_sold > 220, no decimals\n",
    "for i, row in most_popular[most_popular['bottles_sold'] > 250].iterrows():\n",
    "    plt.annotate(\n",
    "        f'{int(row[\"item_number\"])}',  # Convert item_number to integer to remove decimals\n",
    "        (i, row['bottles_sold']),\n",
    "        textcoords=\"offset points\", \n",
    "        xytext=(0, 5), \n",
    "        ha=\"center\"\n",
    "    )\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Zipcode')\n",
    "plt.ylabel('Bottles Sold')\n",
    "plt.title('Bottles Sold by Zipcode')\n",
    "\n",
    "# Tighten layout and show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we create an interactive plot using plotly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create scatter plot using Plotly with gradient coloring and size based on bottles_sold\n",
    "fig = px.scatter(\n",
    "    most_popular, \n",
    "    x=most_popular.index, \n",
    "    y='bottles_sold',\n",
    "    color=most_popular.index,  # Color by index to create the gradient\n",
    "    size='bottles_sold',  # Size of points based on bottles_sold\n",
    "    color_continuous_scale='plasma',  # Gradient from purple to yellow/orange\n",
    "    labels={'color': 'Index'},  # Legend label\n",
    "    title=\"Bottles Sold by Index\",\n",
    "    size_max=30,  # Maximum size of points\n",
    "    hover_data={'item_number': True, 'bottles_sold': True}  # Show item_number in hover info\n",
    ")\n",
    "\n",
    "# Update layout to move color legend outside the plot\n",
    "fig.update_layout(\n",
    "    coloraxis_colorbar=dict(\n",
    "        title=\"Index\",\n",
    "        orientation='v',\n",
    "        x=1.05,  # Position the color legend outside to the right\n",
    "        y=0.5,\n",
    "        len=0.75\n",
    "    ),\n",
    "    xaxis_title=\"Index\",\n",
    "    yaxis_title=\"Bottles Sold\"\n",
    ")\n",
    "\n",
    "# Show the plot\n",
    "fig.show(renderer=\"browser\")  # This will open the plot in your default web browser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Results: Most Popular Bottles per Zipcode\n",
    "\n",
    "In this analysis, we identified the most popular liquor items sold in each zipcode based on the number of bottles sold. Here are the key findings:\n",
    "\n",
    "- **Top Zipcode by Bottles Sold**: The zipcode 52314 had the highest number of bottles sold for item number 75087, with a total of 1560 bottles.\n",
    "- **Top 5 Most Popular Items**:\n",
    "    1. **Zipcode 52314**: Item 75087 with 1560 bottles sold.\n",
    "    2. **Zipcode 50702**: Item 77487 with 768 bottles sold.\n",
    "    3. **Zipcode 50010**: Item 946574 with 288 bottles sold.\n",
    "    4. **Zipcode 51106**: Item 67527 with 240 bottles sold.\n",
    "    5. **Zipcode 50314**: Item 86251 with 240 bottles sold.\n",
    "\n",
    "- **Distribution**: The distribution of the most popular items varies significantly across different zipcodes, with some items being extremely popular in certain areas.\n",
    "\n",
    "- **Visualization**: We used various visualization tools (matplotlib, seaborn, and plotly) to represent the data, providing both static and interactive views of the most popular items per zipcode.\n",
    "\n",
    "These insights help in understanding the preferences of different regions and can be useful for inventory management and targeted marketing strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Purpose: Compute the sales percentage per store (in dollars)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "store_name\n",
      "Wilkie Liquors                          18.203504\n",
      "Sam's Club 6432 / Sioux City            10.402915\n",
      "Sam's Club 6514 / Waterloo               8.144583\n",
      "Hy-Vee #3 / BDI / Des Moines             6.460138\n",
      "Sam's Club 6568 / Ames                   6.130994\n",
      "Cedar Ridge Vineyards                    5.815478\n",
      "Hy-Vee Wine and Spirits / WDM            5.282569\n",
      "Central City 2                           4.042403\n",
      "Fareway Stores #138 / Pleasant Hill      3.595023\n",
      "Hy-Vee Food Store #3 / Waterloo          3.533931\n",
      "Hy-Vee Food Store #2 / Waterloo          3.120621\n",
      "Hy-Vee Food Store / Fort Dodge           2.449691\n",
      "Hy-Vee / Waukee                          2.379012\n",
      "Fareway Stores #067 / Evansdale          2.113779\n",
      "Fareway Stores #153  /  W Des Moines     2.113591\n",
      "Name: sale_dollars, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Compute the sales percentage per store_name in dollars\n",
    "sales_percentage = df_clean.groupby('store_name')['sale_dollars'].sum() / df_clean['sale_dollars'].sum() * 100\n",
    "\n",
    "# Sort the values in descending order\n",
    "sales_percentage = sales_percentage.sort_values(ascending=False)\n",
    "\n",
    "# Display the top ten stores by sales percentage\n",
    "print(sales_percentage.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the sales_percentage data using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a horizontal bar plot using matplotlib\n",
    "plt.figure(figsize=(12, 6))\n",
    "bars=plt.barh(sales_percentage.head(15).index, sales_percentage.head(15), color='skyblue')\n",
    "plt.xlabel('Sales (%)')\n",
    "plt.ylabel('Store name')\n",
    "plt.title('%Sales by store')\n",
    "plt.gca().invert_yaxis()  # Invert the y-axis to show the highest percentage at the top\n",
    "\n",
    "for bar in bars:\n",
    "    plt.text(\n",
    "        bar.get_width() + 0.1,  # Positioning the text next to the bar\n",
    "        bar.get_y() + bar.get_height() / 2,  # Centering vertically\n",
    "        f'{bar.get_width():.2f}',  # Format to one decimal place\n",
    "        va='center',  # Vertical alignment\n",
    "        ha='left'  # Horizontal alignment\n",
    "    )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we replicate the plot using seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert to DataFrame for easier handling\n",
    "sales_percentage_df = pd.Series(sales_percentage).reset_index()\n",
    "sales_percentage_df.columns = ['Store Name', 'Sales Percentage']\n",
    "\n",
    "# Create a horizontal bar plot using Seaborn\n",
    "plt.figure(figsize=(13, 7))\n",
    "sns.barplot(x='Sales Percentage', y='Store Name', data=sales_percentage_df.head(15), hue='Store Name', palette='pastel', legend=False)\n",
    "\n",
    "plt.xlabel('Sales (%)')\n",
    "plt.ylabel('Store Name')\n",
    "plt.title('% Sales by Store')\n",
    "plt.gca().invert_yaxis()  # Invert the y-axis to show the highest percentage at the top\n",
    "\n",
    "# Display the values next to the bars\n",
    "for index, row in sales_percentage_df.head(15).iterrows():\n",
    "    plt.text(\n",
    "        row['Sales Percentage'] + 0.1,  # Positioning the text next to the bar\n",
    "        index,  # y position of the bar\n",
    "        f'{row[\"Sales Percentage\"]:.2f}%',  # Format to one decimal place\n",
    "        va='center',  # Vertical alignment\n",
    "        fontweight='bold'  # Make the text bold\n",
    "    )\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the data using plotly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a horizontal bar plot using Plotly\n",
    "fig = px.bar(sales_percentage_df.head(15), x='Sales Percentage', y='Store Name', text='Sales Percentage', color='Sales Percentage', title='% Sales by Store')\n",
    "fig.update_traces(texttemplate='%{text:.2f}%', textposition='outside')  # Format the text to two decimal places\n",
    "fig.update_layout(xaxis_title='Sales (%)', yaxis_title='Store Name', yaxis_categoryorder='total ascending')  # Set the x and y-axis labels\n",
    "\n",
    "# Set xlimit from 0 to 20\n",
    "fig.update_xaxes(range=[0, 20])\n",
    "\n",
    "# Change colour to gradient viridis by Sales Percentage\n",
    "fig.update_traces(marker=dict(color=sales_percentage_df['Sales Percentage'], colorscale='viridis', showscale=True))\n",
    "\n",
    "# Display the plot\n",
    "fig.show(renderer = \"browser\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Results: Sales Percentage per Store\n",
    "\n",
    "In this analysis, we computed the sales percentage per store based on the total sales in dollars. Here are the key findings:\n",
    "\n",
    "- **Top Store by Sales Percentage**: Wilkie Liquors had the highest sales percentage, accounting for 18.20% of the total sales.\n",
    "- **Top 5 Stores by Sales Percentage**:\n",
    "    1. **Wilkie Liquors**: 18.20%\n",
    "    2. **Sam's Club 6432 / Sioux City**: 10.40%\n",
    "    3. **Sam's Club 6514 / Waterloo**: 8.14%\n",
    "    4. **Hy-Vee #3 / BDI / Des Moines**: 6.46%\n",
    "    5. **Sam's Club 6568 / Ames**: 6.13%\n",
    "\n",
    "- **Distribution**: The sales percentages are heavily skewed, with a few stores contributing a significant portion of the total sales.\n",
    "\n",
    "- **Visualization**: We used various visualization tools (matplotlib, seaborn, and plotly) to represent the data, providing both static and interactive views of the sales percentages per store.\n",
    "\n",
    "These insights help in understanding the performance of different stores and can be useful for strategic decision-making and resource allocation."
   ]
  }
 ],
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