What is NumPy Vstack in Python?

This recipe will serve as a one-stop guide to NumPy vstack in Python.

In the realm of array manipulation in Python, there are times when you need to add a row to an array, and this task can be effortlessly accomplished with vstack. In this blog post, we'll explore a simple example to grasp the usage of vstack in Python.

Learn how to build Regression (Linear,Ridge,Lasso) Models in NumPy Python 

What is NumPy vstack in Python?

The np vstack function in Python is an invaluable tool for stacking arrays vertically and is an indispensable part of the NumPy library. It is indispensable for data integration, allowing the seamless combination of datasets from different sources with similar features but varying rows. In time series analysis, vstack proves its worth by stacking multiple time series data for aggregate analysis, simplifying complex operations. Data expansion is another key use case, where new records are added to existing datasets, a common requirement in data collection and augmentation tasks. Furthermore, in the realm of natural language processing, vstack is instrumental for appending additional text data to an existing dataset, expanding the corpus available for text analysis and machine learning tasks. These versatile applications make vstack an indispensable tool for data scientists, researchers, and developers.

By understanding and effectively employing vstack to stack numpy arrays vertically, you can enhance your data manipulation and analysis capabilities, particularly when dealing with structured datasets. 

How to use Hstack in NumPy Python?

In this guide, we'll learn how to use NumPy vstack in Python to stack arrays vertically row-wise.

Step 1: Import the NumPy Library

To begin with, we need to import the required library. NumPy, the crucial library for scientific computing and array manipulation in Python, is instrumental for this purpose.

import numpy as np

The statement import numpy as np is a standard practice in Python's data science and scientific computing community, enabling us to utilize NumPy module with the alias np for convenience.

Step 2: Set up the Input Arrays for Horizontal Stacking

In this step, we create two arrays to serve as our examples. The first single array, denoted as a, is formed using np.ones((3, 3)), resulting in a 3x3 array filled with ones. The second single array, b, is generated by converting a one-dimensional array containing 2, 2, 2 into a 1x3 array.

a = np.ones((3, 3))

b = np.array((2, 2, 2))

While the arrays here are straightforward, in practical applications, your arrays might represent more complex data, such as financial records, sensor readings, or any other structured information.

Step 3: Using Vstack

With our two numpy arrays a and b at our disposal, we can employ vstack to concatenate them vertically and add row b to array a. The process is straightforward, as demonstrated in the following code snippet:

result = np.vstack((a, b))

The np.vstack() function takes a tuple of arrays that are to be stacked vertically. In our case, we are combining a and b. The output is a new array where the rows from b have been appended below the rows of a. So, the number of columns for the new array are different from the the given arrays at the input.

Step 4: Let's Look at the Array formed

After executing the vstack operation, let's examine the resultant dataset by using the following code:

print(result)

Upon running this code snippet, we will obtain the following output:

Output:

[[1. 1. 1.]

 [1. 1. 1.]

 [1. 1. 1.]

 [2. 2. 2.]]

The output illustrates the merged array, comprising the contents of both a and b. The rows from b have been added underneath the rows of a, effectively extending our dataset vertically. . Notice the stacked array does not have the same shape as the input array dimensions.

Dive Deeper in NumPy with ProjectPro!

The np.vstack() in NumPy is a vital tool for vertically stacking a sequence of arrays, making it an essential component of data analysis, data science, machine learning, and scientific computing in Python. By following the simple steps outlined in this short guide, you can readily harness the power of vstack to append and expand your data, rendering it suitable for a wide array of applications. If you want to understand deeply how the function can be implemented in real-world projects then look no further than ProjectPro. It is a platform that offers a subscription to a repository of industry-level projects in data science and big data that have been prepared by experts. So, check out ProjectPro today and hop to your journey of exponential growth in your career.

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