Counting the number of each top item sold each day

gistfile1.txt

# Identifying the items that make up 80% of total sales
top_items = item_sales_counts.cumsum().sort_values(ascending=False)
top_items = top_items[top_items <= eighty_percent_threshold].index

# Filtering data for only the top items
filtered_data = bakery_data[bakery_data['Items'].isin(top_items)]

# Counting the number of each top item sold each day
daily_top_item_sales = filtered_data.groupby(['Date', 'Items']).size().unstack(fill_value=0)

# Merging the daily top item sales with the daily total sales
combined_top_items_daily = daily_top_item_sales.join(daily_item_count.rename('Total_Daily_Sales'))

# Calculating the correlation matrix for this data
correlation_matrix_top_items_daily = combined_top_items_daily.corr()

# Plotting the heatmap of the correlation matrix
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix_top_items_daily, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Top Item Sales per Day and Total Daily Sales')
plt.show()