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.
This exercise is part of the course
A/B Testing in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the mean, standard deviation and count
checkout.____(____)[____].agg({____,____,____})