Using numpy.row_stack() function (4 examples)

Introduction

numpy.row_stack() is a powerful function in the NumPy library, designed for stacking arrays row-wise. This tutorial aims to guide you through the usage of numpy.row_stack(), showcasing its versatility with four progressively complex examples. Whether you are a newcomer to NumPy or looking to deepen your array manipulation skills, this tutorial is tailored for you.

What is numpy.row_stack() Used for?

Before diving into examples, let’s understand what numpy.row_stack() does. This function takes a sequence of arrays and stacks them one on top of the other, along the first axis (row-wise). It is equivalent to numpy.vstack() but specifically designed for row-wise stacking, thus making array concatenation intuitive.

Syntax:

numpy.row_stack(tup)

Parameters:

• tup: sequence of array_like. The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.

Returns:

• stacked: ndarray. The array formed by stacking the given arrays, will be at least 2-D.

Example 1: Basic Usage

To begin, let’s look at a simple use case where we stack two one-dimensional arrays.

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
result = np.row_stack((a, b))

print(result)

This code yields the following output:

[[1 2 3]
 [4 5 6]]

This example demonstrates the straightforward application of numpy.row_stack() for combining two arrays vertically.

Example 2: With 2D Arrays

Next, we explore stacking two-dimensional arrays. This showcases the function’s capability to handle arrays of different dimensions.

import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
result = np.row_stack((a, b))

print(result)

This code produces:

[[1 2]
 [3 4]
 [5 6]
 [7 8]]

Here, numpy.row_stack() effectively combines two 2D arrays, maintaining their structure while stacking them vertically.

Example 3: Mixing Dimensions

Moving to a more advanced scenario, we will mix arrays of different dimensions. This example highlights the function’s flexibility.

import numpy as np

a = np.array([1, 2, 3])
b = np.array([[4, 5, 6], [7, 8, 9]])
result = np.row_stack((a, b))

print(result)

Output:

[[1 2 3]
 [4 5 6]
 [7 8 9]]

In this example, a one-dimensional array is seamlessly stacked atop a two-dimensional array, demonstrating the function’s adaptability.

Example 4: Real-world Application – Combining Data

For our final example, let’s see how .html)Using numpy.row_stack() function (4 examples)can be applied in a real-world scenario. Say we have data from different sources that we want to combine vertically for analysis.

import numpy as np

# Data from Source 1
source1 = np.array([['Name', 'Age'], ['Alice', '24'], ['Bob', '30']])

# Data from Source 2
source2 = np.array([['Cindy', '28'], ['Dave', '35']])

# Combining the data
result = np.row_stack((source1, source2))

print(result)

The combined data:

[[Name Age]
 [Alice 24]
 [Bob 30]
 [Cindy 28]
 [Dave 35]]

This real-world example underscores the utility of numpy.row_stack() in data manipulation tasks, such as combining datasets from different sources.

Conclusion

Throughout this tutorial, we’ve explored the numpy.row_stack() function with various examples, from basic to more complex applications. The function offers a straightforward way to stack arrays vertically, making it an invaluable tool in data manipulation and analysis tasks. Whether you’re combining data from multiple sources or aligning datasets of different dimensions, numpy.row_stack() can simplify your workflow.