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Distribution Analysis of AWS Normalized Usage

This task showcases the distribution of normalized usage amounts across AWS services. The normalized usage amount is a standardized metric that provides a consistent unit of measure across different AWS services, allowing users to better understand and compare their consumption patterns. The histogram, offers insights into the commonality of various usage levels and highlights trends or anomalies in service consumption.

Normalized Usage in the context of AWS Cost and Usage Reports refers to a consistent metric that AWS provides across various services. With the myriad of AWS services available, each has its unit of measurement — for instance, EC2 might be measured in hours of instance usage, S3 in GB-months, and Lambda in GB-seconds. These disparate units make it challenging to compare the usage of one service against another directly. Hence, AWS offers a "normalized" metric, which standardizes these varied measurements into a consistent unit. This allows users to aggregate and compare the consumption of different AWS services more easily.

X-axis (Normalized Usage Amount): Represents different levels of normalized usage amounts, as specified in the lineItem/NormalizedUsageAmount column. Each bin or segment of the histogram corresponds to a range of normalized usage values.

Y-axis (Frequency): Indicates the number of times (or the frequency) a particular range of normalized usage values appears in the data. The height of the bars in the histogram represents this frequency.

The histogram provides a visual representation of how often different levels of normalized usage are observed. Peaks (or areas where the bars are taller) indicate commonly occurring usage amounts, while valleys (or areas with shorter or no bars) point to less frequent usage levels. By understanding this distribution, organizations can identify common usage patterns, outliers, or anomalies in their AWS consumption.

import boto3 import pandas as pd from botocore.exceptions import ClientError # Assuming df is your DataFrame loaded with data if df is not None: print("Analyzing and visualizing distribution of Normalized Usage Amount...") # Ensure the target column is of float type df['lineItem/NormalizedUsageAmount'] = df['lineItem/NormalizedUsageAmount'].astype(float) # Compute frequency of each unique value frequency = df['lineItem/NormalizedUsageAmount'].value_counts().reset_index() frequency.columns = ['lineItem/NormalizedUsageAmount', 'Frequency'] # Sorting values for better visualization frequency = frequency.sort_values(by='lineItem/NormalizedUsageAmount') # Extract x and y values for plot x = frequency['lineItem/NormalizedUsageAmount'].tolist() y = frequency['Frequency'].tolist() #print(x) #print(y) # Set the properties for your plot context.plot.xlabel = 'Normalized Usage Amount' context.plot.ylabel = 'Frequency' context.plot.title = 'Distribution of Normalized Usage Amount' # Add trace (adjust as needed) context.plot.add_trace(name="Distribution of Normalized Usage Amount", xpts=x, ypts=y, tracetype="bar")

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Distribution Analysis of AWS Normalized Usage

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