AWS RDS Cost Analysis based on Instance Type

import pandas as pd from datetime import datetime, timedelta, timezone # Assuming df is your DataFrame and last_n_days is defined # Example: last_n_days = 30 if df is not None: #print("DataFrame Columns:", list(df.columns)) # Convert 'lineItem/UsageStartDate' to datetime and ensure it's in UTC df['lineItem/UsageStartDate'] = pd.to_datetime(df['lineItem/UsageStartDate'], utc=True) # Filter out negative costs df = df[df['lineItem/UnblendedCost'] >= 0] # Adjust the cutoff date to the start of the day in UTC cutoff_date = (datetime.utcnow() - timedelta(days=last_n_days)).replace(hour=0, minute=0, second=0, microsecond=0, tzinfo=timezone.utc) rds_df = df[(df['lineItem/UsageStartDate'] > cutoff_date) & (df['product/ProductName'] == 'Amazon Relational Database Service') & (df['product/instanceType'].notna())] '''# First full day after cutoff first_full_day_after_cutoff = cutoff_date + timedelta(days=1) rds_df = rds_df[rds_df['lineItem/UsageStartDate'] >= first_full_day_after_cutoff]''' if not rds_df.empty: # Group by 'lineItem/UsageStartDate' and 'product/instanceType' for RDS rds_result = rds_df.groupby([rds_df['lineItem/UsageStartDate'].dt.date, 'product/instanceType']).agg( usage_hours=('lineItem/UsageAmount', 'sum'), usage_cost=('lineItem/UnblendedCost', 'sum') ).reset_index() #print("Number of rows in result DataFrame:", len(rds_result)) # Plotting the RDS cost data context.plot.xlabel = 'Date' context.plot.ylabel = 'RDS Usage Cost($)' context.plot.title = f'RDS Cost Usage (Last {last_n_days} Days)' for instance_type in rds_result['product/instanceType'].unique(): instance_data = rds_result[rds_result['product/instanceType'] == instance_type] x = instance_data['lineItem/UsageStartDate'].tolist() y = instance_data['usage_cost'].tolist() #print(f"Instance Type: {instance_type}") #print(f"Sample Dates (x): {x[:5]}") #print(f"Sample Costs (y): {y[:5]}") context.plot.add_trace(name=f"RDS- {instance_type}", xpts=x, ypts=y, tracetype="line") else: print("No data available for RDS in the specified time frame.") else: print("DataFrame is empty. Exiting.") #context.proceed=False