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Insight into Consolidated Billing Differences and Discounts

This task delves deep into the intricacies of AWS billing by comparing the blended and unblended costs associated with AWS services. The blended cost encapsulates consolidated pricing for accounts, factoring in volume discounts, while the unblended cost represents the raw, unaltered expenses. By analyzing the difference between these two metrics, the task offers insights into the cost-saving benefits that stem from consolidated billing and volume discounts. The visual representation, depicted as a histogram, provides a clear frequency distribution of how often certain cost differences occur, enabling users to gauge the financial advantages they gain through AWS's pricing model.

X-axis (Cost Difference): Represents the difference between "Blended Cost" and "Unblended Cost" for AWS services. This difference indicates the impact of consolidated billing, volume discounts, or any other discount mechanisms applied by AWS.

Blended Cost: AWS provides blended rates, which are average costs across all accounts in an organization (especially if you're using AWS Organizations). This accounts for volume discounts and is meant to provide a simplified view of costs.
Unblended Cost: This is the raw cost, representing what you would pay for the AWS service without any volume discounts or other considerations.
The difference (Blended Cost - Unblended Cost) can either be positive or negative. A positive value indicates that the blended cost is higher than the unblended cost, while a negative value indicates that the user is receiving some discount benefits.

Y-axis (Frequency): Represents the number of times a particular cost difference (as calculated and represented on the x-axis) occurs in the dataset.

For instance, if the histogram shows a tall bar at a cost difference of -$5, it indicates that many instances in the data had a discount of $5 due to consolidated billing or other discount mechanisms.
The height of the bars provides insight into how common specific cost differences are. A higher bar means that particular cost difference is more prevalent in the data.

In summary, the figure provides a visual representation of how consolidated billing and volume discounts impact the user's AWS expenses. The x-axis showcases the magnitude of these differences, while the y-axis indicates how frequently such differences occur.

import boto3 import pandas as pd from botocore.exceptions import ClientError import pandas as pd if df is not None: print("Analyzing and visualizing distribution of consolidated billing impact...") #filter_condition = (df['lineItem/BlendedCost'] >= 0) & (df['lineItem/UnblendedCost'] >= 0) #filtered_df = df[filter_condition] filtered_df = df # Convert 'lineItem/BlendedCost' and 'lineItem/UnblendedCost' to float filtered_df['lineItem/BlendedCost'] = filtered_df['lineItem/BlendedCost'].astype(float) filtered_df['lineItem/UnblendedCost'] = filtered_df['lineItem/UnblendedCost'].astype(float) # Calculate the difference between 'lineItem/BlendedCost' and 'lineItem/UnblendedCost' consolidated_diff = filtered_df['lineItem/BlendedCost'] - filtered_df['lineItem/UnblendedCost'] # Compute frequency of each unique value of consolidated_diff frequency = consolidated_diff.value_counts().reset_index() frequency.columns = ['Cost Difference', 'Frequency'] # Sorting values for better visualization frequency = frequency.sort_values(by='Cost Difference') # Extract x and y values for plot x = frequency['Cost Difference'].tolist() y = frequency['Frequency'].tolist() #print(x) #print(y) # Set the properties for your plot context.plot.xlabel = 'Cost Difference ($)' context.plot.ylabel = 'Frequency' context.plot.title = 'Distribution of Consolidated Billing Impact' # Add the trace to your plot with x and y context.plot.add_trace(name="Distribution of Consolidated Billing Impact", xpts=x, ypts=y, tracetype="bar") print("Analysis complete.") else: print("Failed to fetch data from dataframe. Exiting.")

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Insight into Consolidated Billing Differences and Discounts

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