Python Feature Engineering Cookbook

How to do it...

Let's begin by loading pandas and the dataset:

1. First, we'll import the pandas library:

import pandas as pd

2. Let's load the Credit Approval Data Set:

data = pd.read_csv('creditApprovalUCI.csv')

3. Let's calculate the percentage of missing values for each variable and sort them in ascending order:

data.isnull().mean().sort_values(ascending=True)

 The output of the preceding code is as follows:

A11    0.000000
A12    0.000000
A13    0.000000
A15    0.000000
A16    0.000000
A4     0.008696
A5     0.008696
A6     0.013043
A7     0.013043
A1     0.017391
A2     0.017391
A14    0.018841
A3     0.133333
A8     0.133333
A9     0.133333
A10    0.133333
dtype: float64

4. Now, we'll remove the observations with missing data in any of the variables:

data_cca = data.dropna()

To remove observations ...