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5 Python Libraries to Know for Data Science

5 Python Libraries to know for Data Science

In this post, we will discussing the 5 Python Libraries to Know for Data Science in 2022. This post follows some features based comparisons and some real world scenarios based comparisons.

We all know that Python is a great language for data science and machine learning. It has a wide variety of packages that allow you to do all kinds of analysis. To help you get started, we’ve compiled a list of 5 Python libraries that will take your data science skills one step further.


Numpy is the fundamental package for scientific computing in Python. It provides fast and efficient operations on arrays and matrices as well as linear algebra, random number generation, integral calculus, and more without sacrificing usability.

Numpy is used to analyze data with columns and rows; it can load from different formats such as Excel, CSV, JSON, etc., handle missing data appropriately, do aggregations across rows or down columns, apply functions across rows or up columns, perform statistical modeling via various algorithms such as K-Means clustering.

Numpy has some functions that are only available in Numpy. For example, numpy.signal allows you to work with signal processing while numpy.linalg has methods for linear algebraic computations like matrix decompositions and solving systems of linear equations. You can also use Numpy to slice through arrays or matrices by providing start and end points to get just the parts of interest without having to loop over them individually.


Pandas is a library that provides fast and efficient operations on data tables. In this post, we’ll cover the basics of how to load data from a CSV file into Pandas DataFrames, as well as how to perform fundamental operations on those data tables.

First, we’ll import the pandas module:

import pandas as pd

Next, we will initialize a DataFrame object with some dummy data:

import numpy as np
df = pd.DataFrame([np.random.randn(10), np.random.randn(10)], columns=['A', 'B'])

The df variable now contains two rows of random numbers in A and B columns respectively. To find out more about what df contains, you can use the print() function.


Matplotlib is a Python module for creating 2D plots. It’s an object-oriented API that can be used in scripts or applications. You can use it to create plots, histograms, power spectra, bar charts, error charts, scatterplots, etc.

Matplotlib provides the following features among many others:

  • rich set of plotting commands (lines, markers, images)
  • interactive window for easy plotting
  • support for various output formats (PDFs, PostScript files etc.)
  • automated generation of common plots like dot plots and boxplots.


Scikit-learn is a Python module for machine learning. It is a tool to do supervised or unsupervised learning, or as a module in larger machine learning projects.


Statsmodels is a Python module that allows users to explore data and estimate statistical models. It was designed to deal with large data sets, but can also be used for smaller datasets.

Sklearn is a library for machine learning in Python. It features various algorithms such as support vector machines, random forest, gradient boosting, and more.

These were the 5 libraries to know for data science career in 2022. Please comment down below which of the features mentioned above influences you towards learning Python for Data Science.

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