Understanding NumPy Unstack: A Step-by-Step Guide with Examples

numpy unstack


In this guide, we'll explore the unstack function step by step with practical examples. One such function that comes in handy for reshaping and organizing data is numpy.unstack().



What is NumPy Unstack?

numpy.unstack() is a function that reverses the operation of stacking arrays along a particular axis. It is particularly useful when you have data organized in a stacked format and want to rearrange it for better analysis or visualization.

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Step 1: Importing NumPy

Before diving into the unstack function, make sure to import the NumPy library.

pip install numpy

Now, let's import NumPy in your Python script or Jupyter Notebook:

import numpy as np

Step 2: Creating a Stacked Array

For demonstration purposes, let's create a stacked array using numpy.stack():

stacked_array = np.stack(([1, 2, 3], [4, 5, 6]))
print("Stacked Array:")

This will give us a 2x3 stacked array.

Step 3: Using NumPy Unstack

Now, let's unstack the array using numpy.unstack():

unstacked_array = np.unstack(stacked_array, axis=0)
print("Unstacked Array:")

Here, we unstacked along axis 0, resulting in two arrays. You can choose a different axis based on your data structure.



Step 4: Practical Example

Let's consider a practical example. Suppose you have sales data organized in a stacked format:

sales_data = np.array([[100, 150, 200], [250, 300, 350], [400, 450, 500]])

To analyze sales by product, unstack the data:

unstacked_sales = np.unstack(sales_data, axis=0)
print("Unstacked Sales Data:")

This will provide separate arrays for each product's sales.


NumPy's numpy.unstack() is a versatile tool for reshaping and organizing data. By following this step-by-step guide and experimenting with examples, you'll be better equipped to leverage this function for your data manipulation needs. Whether you're working with numerical data or real-world datasets, understanding numpy.unstack() opens up new possibilities for efficient data analysis.

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