Are you exploring a dataset using Python? If you are in your initial discovery phase, you might want to produce some graphs so that you can easily see what’s going on with the data

Visualize Missing Data with Seaborn

Udemy’s course called Python for Data Science and Machine Learning Bootcamp has a video called 90. Logistic Regression with Python Part 1 where the instructor shows us how to do this. His code uses train instead of df. He’s using the Titanic dataset at Kaggle.

import seaborn as sns
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis')

Titanic Dataset

I have a post here called Data Imputation of Age. In that post there is an example of graphically displaying the number of missing values. You will see a purple rectangle with yellow markings.