Final Assignment Vlastos Dimitrios

Introduction.md

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+This report summarizes the analysis of the Iowa Liquor Sales dataset (2012-2020). The objective was to identify the most popular item per zipcode and the percentage of sales per store from 2016-2019.
+The analysis involved data extraction, transformation, and visualization using Python, Pandas, and Matplotlib.
+
+Data Extraction
+We queried the database to retrieve liquor sales data between 2016 and 2019 and exported the results to a CSV file for further analysis.
+
+Data Transformation
+Using Pandas, we performed the following transformations:
+1. Filtering the data for the years 2016-2019.
+2. Grouping and Aggregating to find the total bottles sold per item per zipcode.
+3. Calculating Percentages of sales per store.
+
+Data Visualization
+Two primary visualizations were created:
+1. Bar Plot: Most Popular Items per Zipcode
+2. Pie Chart: Sales per Store
+
+Conclusion
+The analysis revealed the most popular liquor items per zipcode and the sales distribution across stores.
+The visualizations effectively communicate the findings, providing insights into consumer preferences and sales performance in different areas.

scratch.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+# Load the dataset
+df = pd.read_csv(r"C:\Users\venic\Desktop\DIMITRIS\years16to19.csv")
+
+# Convert date column to datetime
+df['date'] = pd.to_datetime(df['date'])
+
+# Find the most popular item per zipcode
+popular_items = df.groupby(['zip_code', 'item_description'])['bottles_sold'].sum().reset_index()
+most_popular_items = popular_items.loc[popular_items.groupby('zip_code')['bottles_sold'].idxmax()]
+
+# Calculate the percentage of sales per store
+total_sales = df['sale_dollars'].sum()
+sales_per_store = df.groupby('store_number')['sale_dollars'].sum().reset_index()
+sales_per_store['percentage_of_sales'] = (sales_per_store['sale_dollars'] / total_sales) * 100
+
+# Print results
+print("Most popular items per zipcode:\n", most_popular_items)
+print("\nPercentage of sales per store:\n", sales_per_store)
+
+# Set the aesthetic style of the plots
+sns.set_style("whitegrid")
+
+# Bar plot of the most popular items per zipcode
+plt.figure(figsize=(12, 6))
+bar_plot = sns.barplot(data=most_popular_items, x='zip_code', y='bottles_sold', hue='zip_code', dodge=False, palette='viridis', legend=False)
+# Adding title and labels
+bar_plot.set_title('Most Popular Items per Zipcode (2016-2019)', fontsize=16)
+bar_plot.set_xlabel('Zipcode', fontsize=14)
+bar_plot.set_ylabel('Bottles Sold', fontsize=14)
+# Rotate x-axis labels for better readability
+plt.xticks(rotation=45)
+# Display the plot
+plt.show()
+
+
+# Calculate explode values
+explode_values = [0.1 if value > 10 else 0 for value in sales_per_store['percentage_of_sales']]
+# Pie chart of sales per store
+plt.figure(figsize=(10, 10))
+pie_chart = sales_per_store.set_index('store_number')['percentage_of_sales'].plot(
+    kind='pie',
+    autopct='%1.1f%%',
+    startangle=140,
+    colors=sns.color_palette('viridis', len(sales_per_store)),
+    explode=explode_values,
+    legend=True
+)
+# Adding title
+plt.title('Percentage of Sales per Store (2016-2019)', fontsize=16)
+# Equal aspect ratio ensures that pie is drawn as a circle
+plt.axis('equal')
+# Display the legend outside the pie chart
+plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
+# Remove y-axis label for cleaner look
+plt.ylabel('')
+# Display the plot
+plt.show()