ipad37 / 11_Python_Matplotlib_Module

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram, etc

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

11_Python_Matplotlib_Module

Introduction 👋

What Is Python Matplotlib? matplotlib.pyplot is a plotting library used for 2D graphics in python programming language. It can be used in python scripts, shell, web application servers and other graphical user interface toolkits.

What is Matplotlib used for? Matploitlib is a Python Library used for plotting, this python library provides and objected-oriented APIs for integrating plots into applications.

Is Matplotlib Included in Python? Matplotlib is not a part of the Standard Libraries which is installed by default when Python, there are several toolkits which are available that extend python matplotlib functionality. Some of them are separate downloads, others can be shipped with the matplotlib source code but have external dependencies.

Table of contents 📋

001_Python_Matplotlib_pyplot

002_Python_Matplotlib_Exercise_1

003_Python_Matplotlib_Exercise_2

gas_prices

fifa_data

company_sales_data

These are online read-only versions.

Frequently asked questions ❔

How can I thank you for writing and sharing this tutorial? 🌷

You can ⭐ star this tutorial. Starring is free for you, but it tells me and other people that it was helpful and you like this tutorial.

Go here if you aren't here already and click the "⭐ Star" button in the top right corner. You will be asked to create a GitHub account if you don't already have one.

How can I read this tutorial without an Internet connection? 🤔

  1. Go here if you aren't here already.

  2. Click the big green "Clone or download" button in the top right of the page, then click "Download ZIP".

    Download ZIP

  3. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run.

  4. Launch ipython notebook from the folder which contains the notebooks. Open each one of them

    Cell > All Output > Clear

This will clear all the outputs and now you can understand each statement and learn interactively.

If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again.

Authors ✍️

I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome🙏

See github's contributors page for details.

If you have trouble with this tutorial please tell me about it by Create an issue on GitHub. and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one.

If you like this tutorial, please give it a ⭐ star.

Licence 📜

You may use this tutorial freely at your own risk. See LICENSE.

About

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram, etc

License:Other


Languages

Language:Jupyter Notebook 100.0%