Daily AWS Cost Trends

import pandas as pd from datetime import datetime, timedelta # Define last_n_days parameter #last_n_days = 30 # You can change this value as needed if df is not None: #print("Analyzing and visualizing daily AWS costs...") # Filter out negative values filter_condition = (df['lineItem/UnblendedCost'] >= 0) #| (df['lineItem/UnblendedCost'] <= -0.001) df = df[filter_condition] # Convert 'lineItem/UsageStartDate' to datetime df['lineItem/UsageStartDate'] = pd.to_datetime(df['lineItem/UsageStartDate']) # Extract the day of the month and create a new column 'day' df['day'] = df['lineItem/UsageStartDate'].dt.date # Get the maximum date in the dataset max_date = df['day'].max() # Calculate the start date based on the max date and last_n_days start_date = max_date - timedelta(days=last_n_days) # Filter the DataFrame to only include dates greater than or equal to the start date filtered_df = df[df['day'] >= start_date] # Group by day and sum 'lineItem/UnblendedCost' for each group daily_costs = filtered_df.groupby('day')['lineItem/UnblendedCost'].sum() # Extract x and y values x = daily_costs.index.tolist() # This gets the dates y = daily_costs.values.tolist() # This gets the corresponding costs #print(df.head()) # Print the first few rows of the original DataFrame #print(daily_costs) # Print the daily costs after grouping and summing #print(y) # Print the y values extracted from daily_costs # Set the properties for your plot context.plot.xlabel = 'Date' context.plot.ylabel = 'Cost ($)' context.plot.title = f'Daily AWS Costs (Last {last_n_days} Days)' context.plot.add_trace(name=f'Daily AWS Costs (Last {last_n_days} Days)', xpts=x, ypts=y, tracetype="lines") else: print("Failed to fetch data. Exiting.")