Data Visualization in Python using Matplotlib

By StartxLabs
Date 25-05-21
Data Visualization in Python using Matplotlib
" Data Visualization in Python using Matplotlib"

 

A picture speaks more than a sentence of words can do the same. To effectively visualize data, Python has a library named matplotlib. It is a massive library that helps in creating quality graphics. This article covers some basics about matplotlib library, its importance in data visualization, functionality, and more.

 

What is Matplotlib?

 

The most popular Python library introduced in the year 2003 by John D. Hunter, Matplotlib, to provide an effective plotting functionality. Matplotlib abstracts various elements of a plot in the hierarchy. For producing plots on the screen, it uses some of the backend user interfaces such as Qt, TkInter, WxWidgets, or macOS. These user interfaces are known as "interactive". To produce files on disk, it uses hardcopy backends for file formats such as bitmap and vectors. These hardcopy backends are called "non-interactive".

 

Matplotlib has a distinguishing feature known as the pyplot state machine, without which matplotlib is impossible to use. This enables the coders to develop crisp and compact procedural code. Pyplot identifies the relevant method to be applied to the objects from the context. It also creates the objects whenever needed, and if they don't exist. By this, time can be saved, but reusability and maintainability are hardly achieved.

 

 

A sample 3D image created using Matplotlib

 

Read our article on - Web Scraping using Python - A Beginner's Guide

 

When and When Not to Use? 

 

Every library or package has its own pros and cons. Not all packages can be used all the time. Likewise, there are some areas that can easily make use of matplotlib. At the same time, there are some areas for which using matplotlib is quite difficult. Let's have look at those.

 

Matplotlib can be powerfully used in areas such as:

 

  • Exploratory Data Analysis: Pyplot interface of matplotlib greatly helps in analyzing exploratory data. Matplotlib with seaborn library also helps in data analysis by providing more visualizations.

  • Publication Scientific Plotting: This is the most important area that needs the help of matplotlib in data visualization. It is used to create vector images in various formats using the hardcopy. Matplotlib uses Anto Grain Geometry (Agg) to provide aesthetic rendering while generating bitmap images.

 

Matplotlib can be quite difficult to use for:

 

  • Graphical User Interfaces

  • Larger datasets

  • Interactive Visualization used for web

 

Data Visualization and its Purpose

 

Data visualization is defined as the graphical representation of data and information. We can't understand if the data is just a bunch of words or numbers. Instead, proper visualization methods such as graphs, charts, or maps can make things effectively understandable.

 

Here is a simple code for data visualization using matplotlib:

 


 import csv 

 import numpy as np 

 import pandas as pd 

 from collections import Counter 

 from matplotlib import pyplot as plt 

   

 plt.style.use("fivethirtyeight") 

   

 data = pd.read_csv('data.csv') 

 ids = data['Responder_id'] 

 lang_responses = data['LanguagesWorkedWith'] 

   

 language_counter = Counter() 

   

 for response in lang_responses: 

     language_counter.update(response.split(';')) 

   

 languages = [] 

 popularity = [] 

   

 for item in language_counter.most_common(15): 

     languages.append(item[0]) 

     popularity.append(item[1]) 

   

 languages.reverse() 

 popularity.reverse() 

   

 plt.barh(languages, popularity) 

   

 plt.title("Most Popular Languages") 

 # plt.ylabel("Programming Languages") 

 plt.xlabel("Number of People Who Use") 

   

 plt.tight_layout() 

   

 plt.show() 

 

Toolkits

 

There are some toolkits available to extend the functionalities of the Matplotlib. There are:

 

  • Qt interface

  • Mplot3d for plotting 3D images

  • Natgrid, an interface to provide gridding for data that are irregularly spaced.

  • matplotlib2tikz, for exporting to PGF plots to provide smooth integration into LaTex documents.

  • Seaborn, to provide API that offers choices for color defaults and plot style.

  • Basemap, a map plotting that has different map projections, political boundaries, and coastlines.

  • Cartopy, a mapping library that has object-oriented map projection definitions, lines, polygon, arbitrary point,s and capabilities of image transformation.

  • Excel tools, that help in data exchanging with MS excel.