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.
