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Python – Data visualization tutorial

Last Updated : 16 May, 2023
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Data Visualization is a technique of presenting data graphically or in a pictorial format which helps to understand large quantities of data very easily. This allows decision-makers to make better decisions and also allows identifying new trends, patterns in a more efficient way. 

Python - Data visualization tutorial

In this tutorial, we will look at different modules provided by Python for data visualization and will learn about these modules from basic to advance with the help of a huge dataset containing information from installation to different types of charts to exercises, applications, and projects.


Matplotlib is an easy-to-use Python library for data visualization which is built on top of NumPy arrays. It is a low-level module and provides a lot of flexibility but at the cost of writing more code. 


Multiple Plots

Line Graph

Bar Chart


Scatter Plot

Pie Chart

3D Plots

Working with Images

Customizing Plots

More on Matplotlib

Recent Articles on Matplotlib !!!


Seaborn is a high-level library built on the top of Matplotlib which means that it can also use Matplotlib functions and classes. This library provides default styles and color palettes to make a plot more attractive.


Multiple Plots

Relation Plots

Categorical Plots

Distribution Plots

Regression Plots

Customizing Plots

Recent Articles on Seaborn !!!​​​​​​​


After going through these two libraries, you all might be wondering why Plotly. Why we have to learn Plotly over the above visualization tools. Here’s why – 

  • Plotly has hover tool capabilities that allow us to detect any outliers or anomalies in a large number of data points.
  • It is visually attractive that can be accepted by a wide range of audiences.
  • It allows us for the endless customization of our graphs that makes our plot more meaningful and understandable for others.


Different Types of Charts

Interacting with the Plots

Recent Articles on Plotly !!!​​​​​​​

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