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# Boolean Masking and Filtering

`NumPy` lets you filter arrays using **boolean conditions**, a technique known as *masking*.

When you compare elements in an array, NumPy returns a new array containing only the values that satisfy the condition.

<br/>

## Boolean Arrays

A comparison such as `arr > 10` generates a new array of *boolean values* — `True` or `False`.

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

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

<br/>

## Filtering Values

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

```python title="Filtering Values"
print(arr[mask])   # [12 18]
```

Or write it more directly:

```python title="Filtering Values another way"
print(arr[arr > 10])  # [12 18]
```

Masking is especially useful for filtering rows, selecting ranges, or identifying outliers.

<br/>

## Summary

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

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.