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.
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
%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.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
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Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
Seaborn Python Tutorial will help you to implement all the different plots with Seaborn. Data Visualization is an essential component of a data scientist’s skill set and seaborn is one of the most popular packages in Python to implement data visualization. With the help of seaborn, you can create beautiful bar-plots, box-plots, scatter-plots, histograms and so much more.