NumPy: Create an array with the same value (np.zeros, np.ones, np.full)

This article explains how to create a NumPy array (ndarray) filled with the same value.

There are methods such as np.zeros(), np.ones(), and np.full(), which allow you to specify any shape and data type (dtype). Additionally, np.zeros_like(), np.ones_like(), and np.full_like() can be used to create arrays with the same shape and data type as an existing array.

To create an empty array, refer to the following article.

• NumPy: Create an empty array (np.empty, np.empty_like)

You can also convert a list to an ndarray with np.array() or create sequentially numbered ndarray with np.arange() or np.linspace().

• Convert numpy.ndarray and list to each other
• numpy.arange(), linspace(): Generate ndarray with evenly spaced values

The NumPy version used in this article is as follows. Note that functionality may vary between versions.

import numpy as np

print(np.__version__)
# 1.26.1

Create an array filled with 0: np.zeros()

To create an array filled with 0, use np.zeros().

• numpy.zeros — NumPy v1.26 Manual

Specify the shape as the first argument. A scalar results in a one-dimensional array, and a tuple results in a multi-dimensional array.

print(np.zeros(3))
# [0. 0. 0.]

print(np.zeros((2, 3)))
# [[0. 0. 0.]
#  [0. 0. 0.]]

By default, the data type (dtype) is float64. It can also be specified using the second argument, dtype.

print(np.zeros(3).dtype)
# float64

print(np.zeros(3, dtype=np.int64))
# [0 0 0]

print(np.zeros(3, dtype=np.int64).dtype)
# int64

For more information about data types (dtype) in NumPy, refer to the following article.

• NumPy: Cast ndarray to a specific dtype with astype()

Create an array filled with 1: np.ones()

To create an array filled with 1, use np.ones().

• numpy.ones — NumPy v1.26 Manual

The usage is the same as np.zeros().

Specify the shape as the first argument.

print(np.ones(3))
# [1. 1. 1.]

print(np.ones((2, 3)))
# [[1. 1. 1.]
#  [1. 1. 1.]]

By default, the data type (dtype) is float64. It can also be specified using the second argument, dtype.

print(np.ones(3).dtype)
# float64

print(np.ones(3, dtype=np.int64))
# [1 1 1]

print(np.ones(3, dtype=np.int64).dtype)
# int64

Create an array filled with any values: np.full()

To create an array filled with any specified value, use np.full().

• numpy.full — NumPy v1.26 Manual

Specify the shape as the first argument and a fill value as the second argument, fill_value.

print(np.full(3, 100))
# [100 100 100]

print(np.full((2, 3), 0.123))
# [[0.123 0.123 0.123]
#  [0.123 0.123 0.123]]

The data type (dtype) is set according to the fill value. For example, 100 results in int64, and 100.0 results in float64.

print(np.full(3, 100).dtype)
# int64

print(np.full(3, 100.0).dtype)
# float64

print(np.full(3, 0.123).dtype)
# float64

It is also possible to specify the data type as the third argument, dtype. The array is filled with the type-converted value.

print(np.full(3, 100, dtype=np.float64))
# [100. 100. 100.]

print(np.full(3, 0.123, dtype=np.int64))
# [0 0 0]

Create an array filled with 0 using another array: np.zeros_like()

Consider following two arrays with different shapes and data types (dtype).

a_int64 = np.arange(3)
print(a_int64)
# [0 1 2]

print(a_int64.dtype)
# int64

a_float64 = np.arange(6).reshape(2, 3) / 10
print(a_float64)
# [[0.  0.1 0.2]
#  [0.3 0.4 0.5]]

print(a_float64.dtype)
# float64

To create an array filled with 0 that has the same shape and data type as another array, use np.zeros_like().

• numpy.zeros_like — NumPy v1.26 Manual

Specify the original array as the first argument. An array with the same shape and data type as the original array is created.

print(np.zeros_like(a_int64))
# [0 0 0]

print(np.zeros_like(a_int64).dtype)
# int64

print(np.zeros_like(a_float64))
# [[0. 0. 0.]
#  [0. 0. 0.]]

print(np.zeros_like(a_float64).dtype)
# float64

If you specify the second argument, dtype, the array will be of the specified type, not the type of the original array.

print(np.zeros_like(a_int64, dtype=np.float64))
# [0. 0. 0.]

print(np.zeros_like(a_int64, dtype=np.float64).dtype)
# float64

Create an array filled with 1 using another array: np.ones_like()

To create an array filled with 1 that has the same shape and data type as another array, use np.ones_like().

• numpy.ones_like — NumPy v1.26 Manual

The usage is the same as np.zeros_like(). Specify the original array as the first argument.

print(np.ones_like(a_int64))
# [1 1 1]

print(np.ones_like(a_int64).dtype)
# int64

print(np.ones_like(a_float64))
# [[1. 1. 1.]
#  [1. 1. 1.]]

print(np.ones_like(a_float64).dtype)
# float64

If you specify the second argument, dtype, the array will be of the specified type, not the type of the original array.

print(np.ones_like(a_int64, dtype=np.float64))
# [1. 1. 1.]

print(np.ones_like(a_int64, dtype=np.float64).dtype)
# float64

Create an array filled with any values using another array: np.full_like()

To create an array filled with any values that has the same shape and data type as another array, use np.full_like().

• numpy.full_like — NumPy v1.26 Manual

Specify the original array as the first argument and a fill value as the second argument, fill_value.

print(np.full_like(a_int64, 100))
# [100 100 100]

print(np.full_like(a_int64, 100).dtype)
# int64

print(np.full_like(a_float64, 100))
# [[100. 100. 100.]
#  [100. 100. 100.]]

print(np.full_like(a_float64, 100).dtype)
# float64

Be aware that the data type of the original array takes precedence over the data type of fill_value. For example, even if fill_value is float, it will be cast to int if the original array is int.

print(np.full_like(a_int64, 0.123))
# [0 0 0]

print(np.full_like(a_int64, 0.123).dtype)
# int64

If the third argument, dtype, is specified, that data type takes precedence.

print(np.full_like(a_int64, 0.123, dtype=np.float64))
# [0.123 0.123 0.123]

print(np.full_like(a_int64, 0.123, dtype=np.float64).dtype)
# float64

Fill an existing array with any values

zeros_like(), ones_like(), and full_like() create new arrays based on an existing array. The original array remains unchanged.

a = np.arange(6).reshape(2, 3)
print(a)
# [[0 1 2]
#  [3 4 5]]

b = np.zeros_like(a)
print(b)
# [[0 0 0]
#  [0 0 0]]

print(a)
# [[0 1 2]
#  [3 4 5]]

To fill an existing array with any values, select all elements with a slice and assign the new value.

• NumPy: Slicing ndarray

a[:, :] = 0
print(a)
# [[0 0 0]
#  [0 0 0]]

You can omit the trailing : and use just [:] to select all elements regardless of the number of dimensions.

a[:] = 1
print(a)
# [[1 1 1]
#  [1 1 1]]

Note that the value assigned to an array is automatically cast to match the array's data type (dtype). For example, assigning a float value to an int array results in the value being cast to int. To assign a different data type, change the array's type using astype() before assigning.

• NumPy: Cast ndarray to a specific dtype with astype()

print(a.dtype)
# int64

a[:] = 0.123
print(a)
# [[0 0 0]
#  [0 0 0]]

a = a.astype(np.float64)
a[:] = 0.123
print(a)
# [[0.123 0.123 0.123]
#  [0.123 0.123 0.123]]