The reason Python is so popular among the Data Scientist is because of all the built-in libraries within Python.

In Data Science, effective data visualizations are key to communicate your findings. After having done a series of data cleaning and data analysis, one has to communicate the findings from that data analysis and is usually done through visual aids: graphs and charts.

_“Visualizing information can give us a very quick solution to problems. We can get clarity or the answer to a simple problem very quickly.” — _David McCandless

One of the many libraries is Matplotlib, a plotting tool to create data visualizations. Other data visualization libraries such as Seaborn and Pandas DataFrames plot method are built upon Matplotlib. In this article, I will be strictly illustrating static plots — which I believe Matplotlib is best for. It’s quick, easy and intuitive.

If you are new to data science or even well versed in Python, I would highly recommend reading the documentation on Matplotlib, especially if you are reading this to learn data visualizations.

First you would import Matplotlib with pyplot (a Matplotlib module).

#imports
import pandas as pd 
import matplotlib.pyplot as plt

#data-visualization #data-science #matplotlib