Pandas: Data Visualization Tricks

Visualization and solving missing values problem to do better data analysis

Pandas library is one of the most used libraries. It is always the first choice while analyzing data. It has all the ingredients to be used for data preprocessing activities. We have learned its power and utilities earlier too. I would like to take a tour of the visualization part of pandas. We will also see how different problems can be solved in the data frame so that data visualization does not look affected by those values like missing values, null values, etc. It is good to know that pandas has the inbuilt ability to use features of matplotlib library.

Matplotlib in pandas

Matplotlib library is used for visualization and has the ability to create different charts with ease. Here, I don’t want to touch base matplotlib and assume that you already know the capability of matplotlib library. For using matplotlib with pandas, you have to explicitly import this library and then you can directly apply different charts to your data frame.

Import matplotlib as below which has become default to import and alias it with plt.

import matplotlib.pyplot as plt