September 15 2021

Matplotlib -A Comprehensive Course on Data Visualization


Matplotlib -A Comprehensive Course on Data Visualization
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 14 lectures (1h 31m) | Size: 356.8 MB

Matplotlib has support for visualizing information with a wide array of colors and colormaps.


This Course is designed for those learners who wish to acquire knowledge on the advance details of data visualization

Creating visualizations with Matplotlib

More complicated classes and functions in Matplotlib

Advanced topics for experienced Matplotlib users and developers

It cover the basics of how these colormaps look, etc

Basic understanding of Python language

Matplotlib is one of the most popular Python packages used for data visualization. It is a cross-platform library for making 2D plots from data in arrays. It provides an object-oriented API that helps in embedding plots in applications using Python GUI toolkits such as PyQt, WxPythonotTkinter. It can be used in Python and IPython shells, Jupyter notebook and web application servers also.

Matplotlib is a low level graph plotting library in python that serves as a visualization utility.

Matplotlib was created by John D. Hunter.

Matplotlib is open source and we can use it freely.

Matplotlib is mostly written in python, a few snts are written in C, Objective-C and jаvascript for Platform compatibility.

Audience

This Course is designed for those learners who wish to acquire knowledge on the details of data visualization.

Prerequisites

Matplotlib is written in Python and makes use of NumPy, the numerical mathematics extension of Python. We assume that the readers of this tutorial have basic knowledge of Python.

The source code for Matplotlib is located at this github repository

This Matplotlib lecture takes you through the basics Python data visualization: the anatomy of a plot, pyplot and pylab, and much more

Humans are very visual creatures: we understand things better when we see things visualized. However, the step to presenting analyses, results or insights can be a bottleneck: you might not even know where to start or you might have already a right format in mind, but then questions like "Is this the right way to visualize the insights that I want to bring to my audience" will have definitely come across your mind.

When you're working with the Python plotting library Matplotlib, the first step to answering the above questions is by building up knowledge on topics like:

The anatomy of a Matplotlib plot: what is a subplot What are the Axes What exactly is a figure

Plot creation, which could raise questions about what module you exactly need to import (pylab or pyplot), how you exactly should go about initializing the figure and the Axes of your plot, how to use matplotlib in Jupyter notebooks, etc.

Plotting routines, from simple ways to plot your data to more advanced ways of visualizing your data.

Basic plot customizations, with a focus on plot legends and text, titles, axes labels and plot layout.

Saving, showing, clearing, . your plots: show the plot, save one or more figures to, for example, pdf files, clear the axes, clear the figure or close the plot, etc.

Lastly, you'll briefly cover two ways in which you can customize Matplotlib: with style sheets and the settings.

What Does A Matplotlib Python Plot Look Like

At first sight, it will seem that there are quite some components to consider when you start plotting with this Python data visualization library. You'll probably agree that it's confusing and somes even discouraging seeing the amount of code that is necessary for some plots, not knowing where to start yourself and which components you should use.

Luckily, this library is very flexible and has a lot of handy, built-in defaults that will help you out tremendously. As such, you don't need much to get started: you need to make the necessary imports, prepare some data, and you can start plotting with the help of the plot function!

Developers

Programmers

Aspiring Data Scientist

Computer Science students




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