numpy.choose

numpy.diagonal

# numpy.compress¶

`numpy.``compress`(condition, a, axis=None, out=None)[source]

Return selected slices of an array along given axis.

When working along a given axis, a slice along that axis is returned in output for each index where condition evaluates to True. When working on a 1-D array, `compress` is equivalent to `extract`.

Parameters
condition1-D array of bools

Array that selects which entries to return. If len(condition) is less than the size of a along the given axis, then output is truncated to the length of the condition array.

aarray_like

Array from which to extract a part.

axisint, optional

Axis along which to take slices. If None (default), work on the flattened array.

outndarray, optional

Output array. Its type is preserved and it must be of the right shape to hold the output.

Returns
compressed_arrayndarray

A copy of a without the slices along axis for which condition is false.

`ndarray.compress`

Equivalent method in ndarray

`np.extract`

Equivalent method when working on 1-D arrays

`ufuncs-output-type`

Examples

```>>> a = np.array([[1, 2], [3, 4], [5, 6]])
>>> a
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.compress([0, 1], a, axis=0)
array([[3, 4]])
>>> np.compress([False, True, True], a, axis=0)
array([[3, 4],
[5, 6]])
>>> np.compress([False, True], a, axis=1)
array([,
,
])
```

Working on the flattened array does not return slices along an axis but selects elements.

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