Handling Missing Values

So far, you have looked at a variety of issues when it comes to datasets. Now it is time to discuss another issue that occurs quite frequently: missing values. As you may have guessed, this type of issue means that certain values are missing for certain variables.

The pandas package provides a method that we can use to identify missing values in a DataFrame: .isna(). Let's see it in action on the Online Retail dataset. First, you need to import pandas and load the data into a DataFrame:

import pandas as pd
file_url = 'https://github.com/PacktWorkshops/The-Data-Science-Workshop/blob/master/Chapter10/dataset/Online%20Retail.xlsx?raw=true'
df = pd.read_excel(file_url)

The .isna() method returns a pandas series with a binary value for each cell of a DataFrame and states whether it is missing a value (True) or not (False):

df.isna()

You should get the following output:

Figure 11.34: Output of the .isna() method...