NumPy Library: What Exactly is this & How It’s used in Python?

Hello everyone, welcome back to CybercityHelp. Since you are now familiar with the Python language and know its basics, this is the right time to explore more and upgrade yourself. So here, we are starting a new journey on some advanced topics of Python.

In our today’s article, we will begin this journey toward advanced Python topics. We will be covering some amazing libraries that will help you decide which broader field you want to learn in the future such as machine learning, data analysis, or data science.

So today, we begin this journey, and the first library we are going to cover is known as NumPy. We will be discussing what exactly this library is about, how you can install it, and what its advantages are with examples. So let’s get started.

What is NumPy?

NumPy is nothing but a library. Here, a library means a collection of modules, and these modules are made up of various functions, structures, algorithms, etc. This collection is known as a library. And NumPy is one of the most popular libraries among data scientists.

Here “NumPy” stands for Numerical Python. As you can see from the name, it is a library made for numerical operations in Python. So it is mostly used for various mathematical calculations like arithmetic operations, linear algebra, or matrix calculations.

How to install NumPy library?

NumPy is not an inbuilt library in Python, so we need to manually install it on our computer using the command line interface or Terminal. We can install it using pip, for example:

pip install numpy

When you type the above command in the terminal or command line interface (CLI), NumPy gets installed in your computer. It downloads all required dependencies and files from the source package.

What NumPy is used for?

NumPy was mainly created for data manipulation processes which involve mathematical operations like addition, subtraction, multiplication, and division. It is also used in various mathematical areas such as:

• Linear Algebra
• Fourier Transformations
• Matrices

If you remember your 12th class, you must have solved many questions related to matrices and determinants. Those same types of problems can be solved using the NumPy library. That is why programs can calculate large numbers instantly.

How NumPy is used in Python?

Now let’s discuss how exactly NumPy is used in Python. There are multiple areas where NumPy is used. Let me tell you some of the most common ones. For example:

1. Array Related Calculations

The first example is arrays. It is mostly used in array-related calculations. It replaces Python lists with NumPy arrays by using np.array() to create arrays.

For example:

import numpy as np

arr = np.array([1, 2, 3, 4]) print(arr)

2. Vectorization Related Calculations

The second example is vectorization. NumPy is used for vectorization, which means applying mathematical operations to an entire array in one line. This removes the need for Python loops.

For example:

arr = np.array([1, 2, 3])

result = arr * 2  # result: array([2, 4, 6]) print(result)

3. Matrix Related Calculations

Another major area where NumPy is used is matrix calculations. Fields like machine learning heavily depend on matrix mathematics. NumPy handles matrix multiplication and linear algebra operations.

For example:

A = np.array([[1, 2],
          [3, 4]])

B = np.array([[5, 6], [7, 8]])

C = np.dot(A, B) print(C)

4. Statistical Related Calculations

NumPy is also widely used in statistics. It has many built-in functions that help solve complex statistical problems that are mostly used in data analysis.

For example:

data = np.array([10, 20, 30, 40])

print(np.mean(data)) print(np.median(data)) print(np.std(data))

5. Dataset Related Calculations

The last major area where NumPy is used as a backbone is data analysis. In data analysis, NumPy is used to read large CSV or text files quickly using np.loadtxt or np.genfromtxt.

For example:

data = np.loadtxt('data.csv', delimiter=',')

or when file has missing values

data = np.genfromtxt('data.csv', delimiter=',', filling_values=np.nan)

So these were the examples where NumPy is mostly used and how exactly it is used in Python.

Now we hope that you must have understood everything about NumPy like what it is, how to install it, and where and how it is used in Python. In case something is still unclear, you can ask your doubts in the comment section.

However, this was the first article about NumPy, so we didn’t go into detail. We just gave you an overall idea about NumPy. But from the next article onwards, we will directly start its application part and learn how to use this library to solve complex problems. So stay connected, and yeah, that’s all for today’s article. Thanks for reading this article till the end!

“So keep learning, keep growing!”