Quick guide to top 5 most popular Python data visualization tools

We today we will cover the most popular Python data visualization tools. just a quick post for github, installation, quickstart guides, and cheat sheets if any.

1- Matplotlib

One of the most popular and commonly used python libraries

Github: https://github.com/matplotlib/matplotlib

To install:

python -m pip install -U pip
python -m pip install -U matplotlib

For anaconda:

Matplotlib is available both via the anaconda main channel

conda install matplotlib

as well as via the conda-forge community channel

conda install -c conda-forge matplotlib

Quickstart guides: https://matplotlib.org/stable/tutorials/index

For cheatsheets: https://matplotlib.org/cheatsheets/

2-Plotly.py 

Another popular library for interactive charts thats build on top of plotly.js

Github: https://github.com/plotly/plotly.py

To Install:

pip install plotly==5.6.0

or with conda

conda install -c plotly plotly=5.6.0

 Also supports JupyterLab & Jupyter Notebook, see Github above

Quickstart guides: https://plotly.com/python/

For cheatsheet: https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf

3-Bokeh

Another interactive visualization library with powerful dashboard and data applications capabilities

Github: https://github.com/bokeh/bokeh

To install:

pip install bokeh

For Conda:

conda install bokeh

Quickstart guides: https://docs.bokeh.org/en/latest/docs/first_steps.html#first-steps-guides

Cheatsheet: https://www.datacamp.com/blog/python-data-visualization-bokeh-cheat-sheet

4-Pandas

The library for data analysis but it also comes with some very good plotting capabilities

Github: https://github.com/pandas-dev/pandas

To install:

pip install pandas

For conda

conda install pandas

Quickstart: https://pandas.pydata.org/docs/user_guide/visualization.html

Cheatsheet: https://www.enthought.com/wp-content/uploads/2019/09/Enthought-Pandas-Cheat-Sheet-1-Plotting-with-Series-and-DataFrames-v1.0.2.pdf

5-Seaborn

Seaborn is based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

Github: https://github.com/mwaskom/seaborn

To Install:

pip install seaborn

For Conda

conda install seaborn

Quickstart: https://seaborn.pydata.org/tutorial.html

Cheatsheet: https://www.datacamp.com/blog/python-seaborn-cheat-sheet

Bonus:

Holoviews: https://github.com/holoviz/holoviews

Altair: https://github.com/altair-viz/altair

If you are looking for more libraries for visualizing data in Python you can check the following link https://pyviz.org/tools.html for other tools for dashboarding, geospatial, SciVis.