Data Visualization in Python

Take data and turn it into something colorful, graphical and meaningful :)

An introduction to basic data visualizations using Pandas, Matplotlib, Seaborn, and Plotnine all combined in a single Jupyter Notebook.

Intro

Data visualization allows data scientists to graphically represent data to extract and understand trends, outliers, patterns and further insights in the data.

Python has many many graphing libraries with different features and it can be daunting to know which library to use. This intro tutorial will focus on a few popular plotting libraries:

• Pandas - built on Matplotlib and easy to use with Pandas dataframes
• Matplotlib - massive library with lots of flexibility (stackoverflow will be your friend!)
• Seaborn - statistical visualization with default themes and beautiful styles
• Plotnine - based on gglot2 for all my R peeps!

This tutorial compares Matplotlib, Pandas, Seaborn and Plotnine for the following visualizations:

• Scatter Plots
• Line Charts
• Histograms
• Bar Charts
• Box Plots
• Pie Charts
• Heatmaps
• Faceting
• Pairplots

For interactive plots and possibly a feature tutorial

• Plotly
• D3
• Bokeh

Setup

• Clone the repo!
• Run the 1st block of code in Data_Viz_Tutorial to make sure all pacakges are installed!

You can pip or conda install the following packages (for Python 3.7):

• pandas version 1.0.5
• matplotlib version 3.2.2
• seaborn version 0.10.1
• numpy version 1.19.0 (should install with Pandas)
• plotnine version 0.7.0
• scikit-learn version 0.23.1
• jupyter version 1.0.0 If you are running an older version of Python run install using pip install -U <package name> to update your packages just make sure you are at least using Pandas >= 1.0.0.

For the tutorial I will be using a conda environment conda create env -n py37 python=3.7 but feel free to bypass a virtual environment. The requirements.txt contains the base packages to install (pip install requirements.txt).

Usage

Data_Viz_Tutorial notebook is intended to be used for live coding during the technical talk but if you get behind or run into errors, you can use Data_Viz_Tutorial_Solution to check your work.