# What is the numpy.compress() Method

Numpy.compress() method “returns selected slices of an array along the mentioned axis that satisfies an axis.”

## Syntax

``numpy.compress (condition, input_array, axis = None, out = None)``

## Parameters

1. condition: It depicts the condition based on which the user extracts elements. On applying a condition to the input_array, it returns an array filled with either True or False, and after those input_Array elements are extracted from the Indices having True value.
2. Input_array: It depicts the input array in which the user applies conditions on its elements
3. axis: It Indicates which slice the user wants to select. It is optional, and by default, it works on a flattened array[1-D].
4. out: It depicts the Output_array with elements of input_array, that satisfies the condition. It is an entirely optional parameter.

## Return Value

The compress() function returns the copy of the array elements satisfied according to the given conditions along the axis.

## Example 1: How does the np.compress() Method work?

``````import numpy as np

array = np.arange(10).reshape(5, 2)
print("Original array : \n", array)

a = np.compress((array > 0), array, axis=0)
print("\nSliced array : \n", a)
``````

Output

``````Original array :
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]

Sliced array :
[[0 1]
[2 3]]
``````

## Example 2: How to Use np.compress() Method

``````import numpy as np

array = np.arange(10).reshape(5, 2)
print("Original array : \n", array)

a = np.compress([True, False], array, axis=1)
print("\nSliced array : \n", a)
``````

Output

``````Original array :
[[0 1]
[2 3]
[4 5]
[6 7]
[8 9]]

Sliced array :
[



]
``````

In the above code, the Boolean list was passed as a condition, so along the 1 axis, all the elements were extracted from the 1st column.

That’s it.

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