How to Duplicate and Repeat a 1D Column Array Along the Y-Axis with NumPy

Introduction

One common operation is duplicating (or repeating) a column of values along the y-axis to create a 2D array with multiple columns. This article demonstrates how to achieve this using np.repeat.

Using np.repeat

np.repeat allows you to duplicate elements along a specific axis in a NumPy array. When dealing with a column array, you can repeat its values horizontally (along the y-axis or axis=1) to create a 2D array with repeated columns.

Example 1: Repeating Numeric Values

Here’s how to duplicate a column of integers multiple times:

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import numpy as np

# Define a column array
c = np.array([[1], [2], [3], [5], [7], [11]])

print(c)  # Original array
print(c.shape)  # Shape of the original array

# Repeat the column 4 times along the y-axis
result = np.repeat(c, 4, axis=1)
print(result)  # Output after repeating

Output

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[[ 1]
 [ 2]
 [ 3]
 [ 5]
 [ 7]
 [11]]

(6, 1)

[[ 1  1  1  1]
 [ 2  2  2  2]
 [ 3  3  3  3]
 [ 5  5  5  5]
 [ 7  7  7  7]
 [11 11 11 11]]

Example 2: Repeating Boolean Values

The same operation can be performed on a column of boolean values:

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import numpy as np

# Define a column array of booleans
c = np.array([[False], [True], [False], [False], [False], [True]])

print(c)  # Original array
print(c.shape)  # Shape of the original array

# Repeat the column 4 times along the y-axis
result = np.repeat(c, 4, axis=1)
print(result)  # Output after repeating

Output

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[[False]
 [ True]
 [False]
 [False]
 [False]
 [ True]]

(6, 1)

[[False False False False]
 [ True  True  True  True]
 [False False False False]
 [False False False False]
 [False False False False]
 [ True  True  True  True]]

Key Points to Remember

1. Input Format: The input array must have the shape (n, 1) for proper duplication along the y-axis. If the input is a 1D array, reshape it first using array.reshape(-1, 1).

2. axis=1: This specifies the horizontal direction (columns) for duplication.

3. Flexible Applications: np.repeat works with various data types, including integers, floats, booleans, and more.

Use Cases

Note: This approach has been used to vectorize a research code and improve its performance. More details can be found How to Plot CloudSat 2B-CLDCLASS-LIDAR Product Using Python ?.

References