Working With Nullable Boolean Data

Introduction

In this lab, we will explore the Nullable Boolean data type, provided by the Pandas library in Python. We will learn how to use this feature in indexing and logical operations, and how it differs from traditional boolean operations due to the presence of 'NA' values.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

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Importing Necessary Libraries

First, we need to import the necessary libraries to perform the operations.

## Importing the pandas and numpy libraries
import pandas as pd
import numpy as np

Indexing with NA values

Pandas allows indexing with NA values in a boolean array, which are treated as False.

## Creating a pandas Series
s = pd.Series([1, 2, 3])

## Creating a boolean array with NA values
mask = pd.array([True, False, pd.NA], dtype="boolean")

## Indexing the series with the boolean array
s[mask] ## NA values are treated as False

If you want to keep the NA values, you can manually fill them with fillna(True).

## Filling NA values with True and indexing the series
s[mask.fillna(True)]

Kleene logical operations

Pandas implements Kleene Logic (three-value logic) for logical operations like & (and), | (or) and ^ (exclusive-or). This differs from how np.nan behaves in logical operations.

## Demonstrating the difference in 'or' operations between np.nan and NA
pd.Series([True, False, np.nan], dtype="object") | True ## np.nan behaves differently
pd.Series([True, False, pd.NA], dtype="boolean") | True ## NA follows Kleene logic

## Demonstrating the difference in 'and' operations between np.nan and NA
pd.Series([True, False, np.nan], dtype="object") & True ## np.nan behaves differently
pd.Series([True, False, pd.NA], dtype="boolean") & True ## NA follows Kleene logic

Summary

In this lab, we learned about the Nullable Boolean data type in Pandas and its implementation of Kleene logic for handling NA values in logical operations. This feature provides a more intuitive way to handle missing data in boolean operations, and differs from how np.nan behaves in these operations.