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**

**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.**Input_array:**It depicts the input array in which the user applies conditions on its elements**axis:**It Indicates which slice the user wants to select. It is optional, and by default, it works on a flattened array[1-D].**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)[1], 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 :
[[0]
[2]
[4]
[6]
[8]]
```

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.

Ankit Lathiya is a Master of Computer Application by education and Android and Laravel Developer by profession and one of the authors of this blog. He is also expert in JavaScript and Python development.