What is the numpy.zeros_like() function in Python?

Overview

The numpy.zeros_like() function in Python is used to return an array of zeros (0) with the same shape and data type as the array passed to it.

Syntax

numpy.zeros_like(a, dtype=None, order='K', subok=True, shape=None)

Parameters

The numpy.zeros_like() function takes the following parameter values:

  • a: This represents the array_like object.
  • dtype: This represents the desired data type of the array. This is optional.
  • order: This overrides the memory layout of the result. It can take any of C, F, A, or K orders. This is optional.
  • subok: This takes a boolean value. If the value passed is True, then the newly created array will make use of the subclass type of the array_like object. Otherwise, it will make use of the base-class array. The default value is True. This is optional.
  • shape: This represents integers or sequence of integers that helps override the shape of the output array.

Return value

The numpy.zeros_like() function returns an array of zeros (0) with the same shape and type as the array_like object passed to it.

Example

Try different values for each argument passed to the function to get a better understanding
import numpy as np
# creating an array_like 
thisarray = np.arange(5, dtype = int)
# implementing the numpy.zerps_like() function 
myarray = np.zeros_like(thisarray, dtype=int, order='C', subok=True, shape=(2,3))
# printing the two arrays
print(thisarray)
print(myarray)

Explanation

  • Line 1: We import the numpy module.

  • Line 4: We create an array prototype with 5 elements using the numpy.arange() method. The output is assigned to a variable thisarray.

  • Line 7: We implement the numpy.zero_like() function on thisarray to create an array with the int data type, C order, True value for subok and a shape of 2 arrays with 3 elements each. The result is assigned to another variable myarray.

  • Line 11–12: We print both the prototype thisarray and the modified array myarray.

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