Python NumPy hstack Function

The numpy.hstack() function in Python’s NumPy library is used to horizontally(column-wise) stack arrays. It concatenates the input arrays along the second axis (axis=1), i.e., it joins them horizontally. This process is similar to concatenate arrays along the second axis, except for 1-D arrays where it concatenates along the first axis. It stacks a sequence of two or more arrays into a single NumPy array.

In this article, I will explain numpy.hstack() function syntax, parameters, and how to create an array that is formed by stacking the given arrays horizontally.

1. Quick Examples of hstack() Function

If you are in a hurry, below are some quick examples of the NumPy hstack() function in Python. For more functions of NumPy refer to NumPy Tutorial.

# Quick examples of hstack() function

# Example 1: Use NumPy.hstack() functions
arr = np.array([ 2, 3, 4])
arr1 = np.array([5, 6, 7])
arr2 = np.hstack((arr,arr1))

# Example 2: Use numpy.hstack() function 
# To 2-d numpy arrays
arr =  np.array([[ 2, 3, 5], [ -1, -3, -5]])
arr1 = np.array([[ 6, 8, 10], [ -7, -8, -9]])
arr2 = np.hstack((arr,arr1))

# Example 3: Stacking arrays horizontally
arr =  np.array([[ 2, 3], [ 4, 6]])
arr1 = np.array([[ 6, 8, 10], [ 8, 10, 12]])
arr2 = np.hstack((arr,arr1))

2. Syntax of NumPy hstack()

Following is the Syntax of hstack().

# Syntax of NumPy hstack()
numpy.hstack(tup)

2.1 Parameters of hstack()

Following are the Parameters hstack()

• tup – It is a sequence of input arrays that you want to stack horizontally. This can be a tuple, list, or any iterable containing arrays.

2.2 Return Value of hstack()

It returns an array formed by stacking over the given arrays horizontally.

3. Use numpy.hstack() Function

Use numpy.hstack() function to concatenate arrays horizontally (column-wise). When you use hstack() on multiple arrays of 1-D, it combines all arrays and returns the result in a single array.

Let’s create two 1-dimensional arrays of length three and then horizontally stack them with the hstack() function. The numpy.hstack() function is used to horizontally stack arrays.

In the below example, arr and arr1 are two 1-dimensional arrays. The numpy.hstack() function is used to horizontally stack them, resulting in a new 1-dimensional array arr2 with elements from both arr and arr1.

# Import numpy
import numpy as np

# Create 2-D array
arr = np.array([ 2, 3, 4])
print("First array:\n",arr)
arr1 = np.array([5, 6, 7])
print("Second array:\n",arr1)

# Use NumPy.hstack() functions
arr2 = np.hstack((arr,arr1))
print("Horizontally stacked array:\n",arr2) 

Yields below output.

4. Use hstack() Function to 2-D Numpy Arrays

When working with 2-dimensional NumPy arrays, the numpy.hstack() function can be used to horizontally stack arrays along the second axis (columns).

In the below example, arr and arr1 are two 2-dimensional arrays. The numpy.hstack() function is used to horizontally stack them, resulting in a new 2-dimensional array arr2 with columns from both arr and arr1. The resulting array has the shape (2, 6) because it concatenates along the columns.

# Creating an 2D input array
arr =  np.array([[ 2, 3, 5], [ -1, -3, -5]])
arr1 = np.array([[ 6, 8, 10], [ -7, -8, -9]])

# Use numpy.hstack() function 
# To 2-D numpy arrays
arr2 = np.hstack((arr,arr1))
print("Horizontally stacked array:\n",arr2) 

# Output:
# Horizontally stacked array:
#  [[ 2  3  5  6  8 10]
#  [-1 -3 -5 -7 -8 -9]]

Below is another example where two arrays have the same shape along all except different second axes.

# Creating an 2D input array
arr =  np.array([[ 2, 3], [ 4, 6]])
arr1 = np.array([[ 6, 8, 10], [ 8, 10, 12]])

# Stacking arrays horizontally
arr2 = np.hstack((arr,arr1))
print("Horizontally stacked array:\n",arr2) 

# Output:
# Horizontally stacked array:
# [[ 2  3  6  8 10]
# [ 4  6  8 10 12]]

Frequently Asked Questions

Conclusion

In this article, I have explained numpy.hstack() function and using this function you can stack the NumPy arrays horizontally.

Happy Learning!!

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References

• https://numpy.org/doc/stable/reference/generated/numpy.vstack.html#numpy.vstack