# Filtering

Learn how to use where() and mask() for filtering and replacing data based on boolean indexing.

## We'll cover the following

## Recap of boolean indexing

Before we dive into filtering numerical values with the `pandas`

methods of `where()`

and `mask()`

, it’ll be good to revisit the concept of boolean indexing.** Boolean indexing** is the technique of selecting data from a DataFrame based on an array of `True`

/`False`

values so that only the elements from the original data, where the corresponding element in the mask is `True`

, are selected.

This array of `True`

/`False`

values is known as a **boolean mask** and has the same shape as the original data. The `True`

or `False`

values in the boolean mask are determined by the specific criteria we define. For example, we have the following subset of the credit card dataset, and we set a condition for numerical values to be less than `40`

:

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