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# Boolean Masking and Filtering

NumPy lets you filter arrays using **boolean conditions** — also called *masking*.

You can compare values in an array, and NumPy will return a new array with only the values that match the condition.

<br/>

## Boolean Arrays

A comparison like `arr > 10` returns a new array of `True` or `False` values.

```python
arr = np.array([5, 12, 18, 7])
mask = arr > 10

print(mask)        # [False  True  True False]
```

<br/>

## Filtering Values

You can use the boolean array as a mask to filter the original array.

```python 
print(arr[mask])   # [12 18]
```

Or write it in one line:

```python
print(arr[arr > 10])  # [12 18]
```

This is very useful for filtering rows, selecting values in a range, or finding outliers.

<br/>

## Summary

* Use comparisons (`>`, `<`, `==`, etc.) to create boolean masks
* Apply the mask to select only the values you want
* Works on both 1D and 2D arrays

<br/>

## What’s Next

You’ll now practice filtering arrays using conditions in a Jupyter Notebook.

Boolean masking is a powerful feature in NumPy that enables you to filter arrays by applying conditions. This process involves creating a boolean array by comparing values, which is then used to extract or filter out the desired elements from the original array. It simplifies data selection and manipulation in both 1D and 2D arrays.

### Boolean masking allows you to filter NumPy arrays based on conditions.