Category Data visualization


Matplotlib is the “grandfather” library of data visualization with Python. It was created by John Hunter. He created it to try to replicate MatLab’s (another programming language) plotting capabilities in Python. So if you happen to be familiar with matlab, matplotlib will feel natural to you.

It is an excellent 2D and 3D graphics library for generating scientific figures.

Some of the major Pros of Matplotlib are:

  • Generally easy to get started for simple plots
  • Support for custom labels and texts
  • Great control of every element in a figure
  • High-quality output in many formats
  • Very customizable in general

Matplotlib allows you to create reproducible figures programmatically. Let’s learn how to use it! I encourage you just to explore the official Matplotlib web page:

Installation of Matplotlib:

To install the latest release of matplotlib, you can use pip:

pip install matplotlib

You can also use conda to install the latest version of matplotlib:

conda install matplotlib

Now from next lecture we will learn how to plot different kind of charts and plot with the help of matplotlib.


As per Seaborn’s official website, they state,

“If matplotlib “tries to make easy things easy and hard things possible”, seaborn tries to make a well-defined set of hard things easy too”

So we can say seaborn is an amazing python data visualization library built on top of the matplotlib.

Why one should you Seaborn instead of matplotlib?

  • Seaborn comes with a large number of high-level interfaces and customized themes where matplotlib lacks as it’s not easy to figure out the settings that makes plots attractive.
  • Matplotlib functions don’t work well with dataframes, whereas seaborn does.


To install the latest release of seaborn, you can use pip.

pip install seaborn

You can also use conda to install the latest version of seaborn

conda install seaborn

Seaborn- Matrix Plot

Matrix plots allow you to plot data as color-encoded matrices and can also be used to indicate clusters within the data (later in the machine learning section we will learn how to formally cluster data).

So in this article we will deal with basically two plots as per follow:

  1. Heatmaps:- A heat map (or heatmap) is a graphical representation of data where values are depicted by color. Heat maps make it easy to visualize complex data and understand it at a glance. To use a heatmap the data should be in a matrix form i.e the index name and the column name must match in some way ­so that the data that we fill inside the cells are relevant.
  2. Cluster maps:- Cluster maps uses hierarchical clustering. It performs the clustering based on the similarity of the rows and columns.

Let’s begin by exploring seaborn’s heatmap and clutermap