Removing observations with missing data

Complete Case Analysis (CCA), also called list-wise deletion of cases, consists of discarding observations with missing data. CCA can be applied to both categorical and numerical variables. With CCA, we preserve the distribution of the variables after the imputation, provided the data is missing at random and only in a small proportion of observations. However, if data is missing for many variables, CCA may lead to the removal of a large portion of the dataset.

How to do it...

Let’s begin by making some imports and loading the dataset:

1. Let’s import the pandas and matplotlib libraries:

   import matplotlib.pyplot as plt
   import pandas as pd

2. Load the dataset that we prepared in the Technical requirements section:

   data = pd.read_csv("credit_approval_uci.csv")

3. Find the proportion of missing values per variable, sort them in ascending order, and then make a bar plot, rotating the ticks on the xaxis and adding...