# Solved: Python NumPy column_stack Function Example with 2d array

Python NumPy is a popular library that provides multiple advanced mathematical functionalities and makes it extremely efficient to work with multidimensional arrays. One such useful function is the NumPy column_stack function. In this article, we will take an in-depth look at the column_stack function and its application in working with 2d arrays. We will also explore different libraries and functions related to working with arrays in Python.

## Introduction to NumPy column_stack Function

NumPy’s column_stack function is a convenient tool for stacking multiple 1D and 2D arrays into a single 2D array, arranging the input arrays into columns. It can be helpful in various scenarios, such as when combining multiple datasets or working with matrix operations. The syntax for the column_stack function is as follows:

```numpy.column_stack((array1, array2, ..., arrayN))
```

Now let’s dive into solving a problem using the column_stack function and further explain the code step by step.

## Problem Statement and Solution

Suppose we have two separate datasets containing the scores of students in two subjects, and we want to create a consolidated dataset with both subjects’ scores as columns. We can use the NumPy column_stack function to achieve this.

Here’s a code example of combining two 1D arrays into a single 2D array using column_stack:

```import numpy as np

subject_1_scores = np.array([95, 85, 78, 90])
subject_2_scores = np.array([88, 78, 81, 92])

combined_scores = np.column_stack((subject_1_scores, subject_2_scores))

print(combined_scores)
```

## Step-by-Step Explanation of the Code

Let’s break down the code and explain each step:

1. First, we import the NumPy library with the alias “np.”

2. We create two NumPy arrays, subject_1_scores and subject_2_scores, containing the scores of students in the two subjects.

3. We then use the column_stack function to stack the two arrays column-wise and store the resulting 2D array in a variable called combined_scores.

4. Finally, we print the combined_scores 2D array to the console.

The output of this code will be:

```[[95 88]
[85 78]
[78 81]
[90 92]]
```

Here, we can observe that the input arrays have been successfully stacked column-wise into a single 2D array.

## Other Functions and Libraries to Work with Arrays

Working with multidimensional arrays in Python can be effectively achieved using various libraries and functions. Apart from the NumPy column_stack function, some other notable features are:

• NumPy.hstack: This function also stacks arrays horizontally (column-wise), though it requires the input arrays to have the same number of dimensions, unlike column_stack.
• NumPy.vstack: This function stacks arrays vertically (row-wise).
• NumPy.concatenate: This versatile function can be used to concatenate arrays along a specified axis.
• Pandas: Another popular Python library for data analysis, Pandas provides advanced functionalities for working with structured data, such as combining DataFrames (pandas.DataFrame) using the merge, join, or concat functions.

By mastering these libraries and functions, you can efficiently handle and process various multidimensional array operations in Python. Happy coding!

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