The numpy.zeros_like() method creates a new array of zeros with the same shape and type as a given input array or array-like object.

import numpy as np

arr = np.array([[1, 2, 3],
                [4, 5, 6]])

zeros_array = np.zeros_like(arr)

print(zeros_array)

# Output:
# [[0 0 0]
#  [0 0 0]]

We created a 2D array of 0s from the input array with the same shape (2×3) and matching int64 dtype (the default on most systems).

Syntax

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

Parameters

Argument	Description
arr (required, array_like)	It represents an array or array-like object. You can take it as a reference array from which the zeros_like() method will create a new array filled with 0s with the same shape and size as arr.
dtype (data-type, optional)	It overrides the dtype of the resulting array. By default, it is an int64, but you can override it as needed.
order ({‘C’, ‘F’, ‘A’, ‘K’}, optional)	It controls the memory layout of the output array. By default, it is “k”.
subok (bool, optional)	If True (default), they are subclasses of ndarray. If False, the output is always a base ndarray.
shape (int or sequence of ints, optional)	It overrides the shape of the resulting array.

Overriding dtype

Change the output array’s type to int64 by passing the dtype=int argument, even if the input array contains float values.

import numpy as np

arr = np.array([1.9, 2.1, 3.5])

zeros_int = np.zeros_like(arr, dtype=int)

print(zeros_int)

# Output: [0 0 0]

Specifying memory order

The ‘order’ control defines the layout; ‘K’ preserves the input’s order for optimal memory access.

import numpy as np

arr = np.array([[1, 2], [3, 4]], order='F')  # Fortran-contiguous

z_c = np.zeros_like(arr, order='C')  # Force C-order

print(z_c)
# Output: [[0 0] [0 0]], C-contiguous

z_k = np.zeros_like(arr)

print(z_k)
# Output: [[0 0] [0 0]], K-contiguous
# Default 'K' preserves 'F'

Empty array input

The np.zeros_like() method handles empty shapes correctly, producing an array of zeros.

import numpy as np

empty_array = np.array([])  # Shape (0,)

empty_zeros = np.zeros_like(empty_array)

print(empty_zeros)

# Output: []

That’s all!