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  3. A/B Testing in Python

Exercise

Proportions EDA

Exploratory data analysis (EDA) allows us to get an initial understanding of the data. Summary statistics explain the magnitude, direction, and spread of our average metrics, and visual distributions and trends provide deeper insights into understanding what to look for and uncovering interesting patterns that can be masked under the average summary statistics.

Consider yourself a Data Analyst about to start digging into A/B testing data at the initial EDA stage before running any statistical tests. See what you can find in the data that looks interesting to you. AdSmart and checkout DataFrames, as well as pandas as pd, NumPy and np, Matplotlib.pyplot as plt, and Seaborn as sns, have already been loaded for you.

The Adsmart Kaggle dataset source is linked here.

Instructions 1/3

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  • Calculate the mean, standard deviation and count of the purchased column by checkout_page in one line of code.