NumPy ndarray.repeat() Method

NumPy ndarray.repeat()

The numpy.ndarray.repeat() method repeats elements of an array along a specified axis. This function is useful for expanding data in an array by duplicating its elements.

Syntax

ndarray.repeat(repeats, axis=None)

Parameters

Parameter	Type	Description
repeats	int or array_like	Number of times to repeat each element. Can be an integer (same repeat count for all elements) or an array specifying the repeat count per element.
axis	int, optional	Axis along which to repeat values. If None, the input array is flattened before repeating.

Return Value

Returns a new array with repeated values. The shape of the output array depends on the specified axis and repeats parameter.

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Examples

1. Repeating Elements in a Flattened Array

In this example, we repeat each element of a 1D array a fixed number of times.

import numpy as np  

# Creating a 1D array
arr = np.array([1, 2, 3])

# Repeating each element 3 times
result = arr.repeat(3)

print(result)  # Output: [1 1 1 2 2 2 3 3 3]

Output:

[1 1 1 2 2 2 3 3 3]

The function repeats each element in sequence, producing a flattened output.

2. Repeating Elements Along a Specific Axis

Repeating elements in a 2D array along a specified axis.

import numpy as np  

# Creating a 2D array
arr = np.array([[1, 2], [3, 4]])

# Repeating elements along axis 0 (rows)
result_axis0 = arr.repeat(2, axis=0)
print(result_axis0)

# Repeating elements along axis 1 (columns)
result_axis1 = arr.repeat(2, axis=1)
print(result_axis1)

Output:

[[1 2]
 [1 2]
 [3 4]
 [3 4]]
[[1 1 2 2]
 [3 3 4 4]]

Repeating along axis=0 duplicates rows, while axis=1 duplicates columns.

3. Specifying Different Repeats for Each Element

Using an array to define a different repeat count for each element.

import numpy as np  

# Creating a 1D array
arr = np.array([10, 20, 30])

# Different repeat counts for each element
repeats = [1, 2, 3]
result = arr.repeat(repeats)

print(result)

Output:

[10 20 20 30 30 30]

Each element is repeated according to the corresponding value in the repeats array.

4. Repeating Elements with a Different Count Along an Axis

Applying different repeat counts to elements along a specified axis.

import numpy as np  

# Creating a 2D array
arr = np.array([[1, 2], [3, 4]])

# Repeating elements in different counts along axis 1
repeats = [1, 2]  # Repeat first column once, second column twice
result = arr.repeat(repeats, axis=1)

print(result)

Output:

[[1 2 2]
 [3 4 4]]

Elements in the first column are repeated once, while those in the second column are repeated twice.