Python Data Science Handbook, 2nd Edition

Chapter 9. Comparisons, Masks, and Boolean Logic

This chapter covers the use of Boolean masks to examine and manipulate values within NumPy arrays. Masking comes up when you want to extract, modify, count, or otherwise manipulate values in an array based on some criterion: for example, you might wish to count all values greater than a certain value, or remove all outliers that are above some threshold. In NumPy, Boolean masking is often the most efficient way to accomplish these types of tasks.

Example: Counting Rainy Days

Imagine you have a series of data that represents the amount of precipitation each day for a year in a given city. For example, here we’ll load the daily rainfall statistics for the city of Seattle in 2015, using Pandas (see Part III):

In [1]: import numpy as np
        from vega_datasets import data

        # Use DataFrame operations to extract rainfall as a NumPy array
        rainfall_mm = np.array(
            data.seattle_weather().set_index('date')['precipitation']['2015'])
        len(rainfall_mm)
Out[1]: 365

The array contains 365 values, giving daily rainfall in millimeters from January 1 to December 31, 2015.

As a first quick visualization, let’s look at the histogram of rainy days in Figure 9-1, which was generated using Matplotlib (we will explore this tool more fully in Part IV):

In [2]: %matplotlib inline
        import matplotlib.pyplot as plt
        plt.style.use('seaborn-whitegrid')

In [3]: plt.hist(rainfall_mm, 40);

Figure 9-1. Histogram of 2015 rainfall in Seattle

This histogram gives us a general ...