Missing investors
Dealing with missing data is one of the most common tasks in data science. There are a variety of types of missingness, as well as a variety of types of solutions to missing data.
You just received a new version of the banking DataFrame containing data on the amount held and invested for new and existing customers. However, there are rows with missing inv_amount values.
You know for a fact that most customers below 25 do not have investment accounts yet, and suspect it could be driving the missingness. The pandas, missingno and matplotlib.pyplot packages have been imported as pd, msno and plt respectively. The banking DataFrame is in your environment.
This exercise is part of the course
Cleaning Data in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Print number of missing values in banking
print(____)
# Visualize missingness matrix
____
____