Beautifying the Messy Plots in Python & Solving Common Issues in Seaborn

Beautifying the Messy Plots in Python & Solving Common Issues in Seaborn

Beautifying the Messy Plots in Python & Solving Common Issues in Seaborn

Creating presentable plots in Python can be a bit daunting. It’s especially so if you are used to making your visualizations using other BI software or even R, where most plots come already prettified for you. Another problem is that there are many ways things can go wrong and ways to resolve the issue will depend on the choices you made for the plot. Here, I will demonstrate a few ways to easily create plots in Python for the various scenarios, and show you how to resolve some of the issues that may arise in each case.

In this post, I will focus on efficiency and share some of the tidbits that will make creating visually appealing plots fast.

Basic Set-up

Pyplot in Matplotlib is a must-have to plot in Python. Other libraries are likely all using Matplotlib as its backend. Seaborn is one example. Seaborn adds some nice functionalities, but these functionalities do create confusion sometimes. Let’s import all our packages first.

import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline 

Use %matplotlib inline to display plots if you are using an iPython platform that allows you to display your plots in the front-end, such as Jupyter Notebook.

data-visualization seaborn python

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