Numpy hstack() Function

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The Numpy hstack() Function which is used to horizontally stack arrays. It combines a sequence of arrays along their horizontal axis i.e. axis 1.

All input arrays must have the same number of rows or compatible shapes for broadcasting and the function returns a new array with columns concatenated.

This function is useful for merging arrays side-by-side particularly in data processing and manipulation tasks.

And also useful for combining multiple arrays into a single array where each array is added as a column.

Syntax

The syntax for the Numpy hstack() function is as follows −

numpy.hstack(tup, *, dtype=None, casting='same_kind')

Parameters

Following are the parameters of the Numpy hstack() Function −

• tup: A tuple of arrays to be stacked. The arrays must have the same shape along all but the second axis.
• dtype: This parameter specifies the data type of the resulting array. If provided all input arrays are cast to this data type before stacking. If None the data type is determined from the input arrays.
• casting: This parameter Controls the type casting rule if dtype is specified. It determines how the data types are handled when converting to dtype

Return Value

This function returns a single ndarray formed by stacking the given arrays horizontally.

Example 1

Following is the example of Numpy hstack() function which shows how two 1D arrays are concatenated into a single 1D array.

import numpy as np

# Creating two 1D arrays
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])

# Stacking arrays horizontally
result = np.hstack((array1, array2))
print(result)

Output

[1 2 3 4 5 6]

Example 2

This example shows how a 1D array is reshaped and stacked with a 2D array, demonstrating the flexibility of numpy.hstack() with mixed dimensions −

import numpy as np

# Creating a 2D array and a 1D array
array1 = np.array([[1, 2, 3], [4, 5, 6]])
array2 = np.array([7, 8])

# Reshape array2 to be a column vector with the same number of rows as array1
# Note: array2 needs to have the same number of rows as array1 for successful horizontal stacking
array2 = np.array([7, 8]).reshape(-1, 1)  # Reshape to (2, 1) to match the number of rows in array1

# Stack arrays horizontally
result = np.hstack((array1, array2))

print("Resulting Array:")
print(result)

Output

Resulting Array:
[[1 2 3 7]
 [4 5 6 8]]

Example 3

Here in this example we are combining two arrays into 1 single array −

import numpy as np

# Define the first array
a = np.array([[1, 2], [3, 4]])

print('First array:')
print(a)
print('\n')

# Define the second array
b = np.array([[5, 6], [7, 8]])

print('Second array:')
print(b)
print('\n')

# Horizontally stack the two arrays
c = np.hstack((a, b))

print('Horizontal stacking:')
print(c)
print('\n')

Output

First array:
[[1 2]
 [3 4]]


Second array:
[[5 6]
 [7 8]]


Horizontal stacking:
[[1 2 5 6]
 [3 4 7 8]]