I have analyzed the transaction data, confirming a revenue drop and a spike in failed transactions on September 15-16, coinciding with payment gateway outages. To determine if other factors contributed to this decline, I will now analyze customer behavior and product performance in parallel. This will help isolate the impact of the technical issues versus other potential causes. I will now execute two Python scripts simultaneously: one to analyze `customer_activity_aug_sep.csv` for behavioral shifts, and another to examine `product_performance_aug_sep.csv` for product-specific issues. I will also update the task list to reflect this progress.call:run_command{command: import pandas as pd # Load the data df = pd.read_csv('customer_activity_aug_sep.csv') # Convert month to datetime df['month'] = pd.to_datetime(df['month']) # Separate August and September data aug_df = df[df['month'].dt.month == 8] sep_df = df[df['month'].dt.month == 9] # Analyze customer segments aug_segments = aug_df['customer_segment'].value_counts() sep_segments = sep_df['customer_segment'].value_counts() print("Customer Segments - August:") print(aug_segments) print("\nCustomer Segments - September:") print(sep_segments) # Analyze account status aug_status = aug_df['account_status'].value_counts() sep_status = sep_df['account_status'].value_counts() print("\nAccount Status - August:") print(aug_status) print("\nAccount Status - September:") print(sep_status) }call:run_command{command: import pandas as pd # Load the data df = pd.read_csv('product_performance_aug_sep.csv') # Convert date to datetime df['date'] = pd.to_datetime(df['date']) # Separate August and September data aug_df = df[df['date'].dt.month == 8] sep_df = df[df['date'].dt.month == 9] # Analyze revenue by category aug_revenue_by_cat = aug_df.groupby('category')['revenue'].sum().sort_values(ascending=False) sep_revenue_by_cat = sep_df.groupby('category')['revenue'].sum().sort_values(ascending=False) print("Revenue by Category - August:") print(aug_revenue_by_cat) print("\nRevenue by Category - September:") print(sep_revenue_by_cat) # Analyze top 10 products by revenue aug_top_products = aug_df.groupby('product_name')['revenue'].sum().nlargest(10) sep_top_products = sep_df.groupby('product_name')['revenue'].sum().nlargest(10) print("\nTop 10 Products by Revenue - August:") print(aug_top_products) print("\nTop 10 Products by Revenue - September:") print(sep_top_products) }call:update_todos{updates:[{id:todo_2,status:completed},{id:todo_3,status:active},{id:todo_4,status:active}]}