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# NumPy | compress method

schedule Aug 12, 2023
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NumPy's `compress(~)` method extracts a subset of the input values based on the given condition.

NOTE

NumPy's `extract(~)` method is equivalent to the `compress(~)` when the input array is 1D.

# Parameters

1. `condition` | `array-like` of `boolean`

An array of booleans that indicate whether or not to set the value. If the size of condition is smaller than the input array `a`, then output will be truncated so as to match the size of the condition.

2. `a` | `array-like`

The input array.

3. `axis` | `int` | `optional`

The axis along which to extract the values. For 2D arrays, the allowed values are as follows:

Axis

Meaning

0

Extraction is done row-wise.

1

Extraction is done column-wise.

None

The input array is treated as a flattened array.

By default, `axis=None`.

4. `out` | `Numpy array` | `optional`

A NumPy array to place the extracted subset.

# Return value

A new NumPy array holding a subset of input values.

# Examples

## Compressing a 1D array

``` np.compress([True, False, True, False], [4,5,6,7]) array([4, 6]) ```

Notice that this is equivalent to the following:

``` a = np.array([4,5,6,7])mask = [True, False, True, False]a[mask] array([4, 6]) ```

## Truncation

Truncation happens when the size of the condition array is smaller than that of the input array:

``` a = np.array([4,5,6,7])np.compress([True, False], a) array([4]) ```

Here, only the first two values in the array are considered - yet only first value is returned since the second value is flagged as False.

## Compressing a 2D array

Consider the following 2D array:

``` a = np.array([[4,5],[6,7]])a array([[4, 5], [6, 7]]) ```

### Extracting from a flattened array

``` np.compress([True, False, False, True], a) array([4, 7]) ```

### Extracting row-wise

``` np.compress([True, False], a, axis=0) array([[4, 5]]) ```

### Extracting column-wise

``` np.compress([True, False], a, axis=1) array([[4], [6]]) ```
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