Master Data Visualization with Python and Matplotlib 3

Master Data Visualization with Python and Matplotlib 3, available at $19.99, has an average rating of 3.35, with 109 lectures, 3 quizzes, based on 47 reviews, and has 203 subscribers.

You will learn about Use Matplotlib for data visualization with the Python programming language. Construct different types of plot such as lines and scatters, bar plots, and histograms. Use Matplotlib 3’s animation and interactive capabilities to spice up your data visualizations Use Matplotlib 3’s animation and interactive capabilities to spice up your data visualizations Visualize data using PyPlot; plot functions, create complex subplots and troubleshoot issues. Build interactive plots with Matplotlib 3. Understand and implement event handling and GUI widgets and learn how to turn interactive plots into videos. Build Matplotlib 3D graphs functionality to visualize data with multiple variables and dimensions. Draw on plots, ranging from inserting lines, adding text, and drawing different shapes and annotations. Draw special-purpose advanced plots such as non-Cartesian plots, vector fields, violin graphs, and more. This course is ideal for individuals who are This Course is perfect for: or Python Developers, Data Scientists, and Data Analysts who are familiar with Matplotlib and want to master their skill on an advanced level to get better in creating effective and interactive data visualizations. It is particularly useful for This Course is perfect for: or Python Developers, Data Scientists, and Data Analysts who are familiar with Matplotlib and want to master their skill on an advanced level to get better in creating effective and interactive data visualizations.

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Summary

Title: Master Data Visualization with Python and Matplotlib 3

Price: $19.99

Average Rating: 3.35

Number of Lectures: 109

Number of Quizzes: 3

Number of Published Lectures: 109

Number of Published Quizzes: 3

Number of Curriculum Items: 112

Number of Published Curriculum Objects: 112

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Use Matplotlib for data visualization with the Python programming language.

Construct different types of plot such as lines and scatters, bar plots, and histograms.

Use Matplotlib 3’s animation and interactive capabilities to spice up your data visualizations

Use Matplotlib 3’s animation and interactive capabilities to spice up your data visualizations

Visualize data using PyPlot; plot functions, create complex subplots and troubleshoot issues.

Build interactive plots with Matplotlib 3. Understand and implement event handling and GUI widgets and learn how to turn interactive plots into videos.

Build Matplotlib 3D graphs functionality to visualize data with multiple variables and dimensions.

Draw on plots, ranging from inserting lines, adding text, and drawing different shapes and annotations.

Draw special-purpose advanced plots such as non-Cartesian plots, vector fields, violin graphs, and more.

Who Should Attend

This Course is perfect for:

Python Developers, Data Scientists, and Data Analysts who are familiar with Matplotlib and want to master their skill on an advanced level to get better in creating effective and interactive data visualizations.

Target Audiences

This Course is perfect for:

Python Developers, Data Scientists, and Data Analysts who are familiar with Matplotlib and want to master their skill on an advanced level to get better in creating effective and interactive data visualizations.

Matplotlib is a multi-platform data visualization tool for creating advanced-level and interactive data visualizations that showcase insights from your datasets. One of Matplotlib’s most important features is its ability to work well with many operating systems and graphics backends. Matplotlib helps in customizing your data plots, building 3D plots and tackling real-world data with ease. Python’s elegant syntax and dynamic typing, along with its interpreted nature, make it a perfect language for data visualization. If you’re a Python Developer or a data scientist looking to create advanced-level Data Visualizations that showcase insights from your datasets with Matplotlib 3, then this Course is perfect for you!

This comprehensive 4-in-1 course follows a step-by-step approach to entering the world of data Visualization with Python and Matplotlib 3. To begin with, you’ll use various aspects of data visualization with Matplotlib to construct different types of plot such as lines and scatters, bar plots, and histograms. You’ll use Matplotlib 3’s animation and interactive capabilities to spice up your data visualizations with a real-world dataset of stocks. Finally, you’ll master Matplotlib by exploring the advanced features and making complex data visualization concepts seem very easy.

By the end of the course, you’ll become a data visualizations expert with Matplotlib 3 by learning effective and practical data visualization recipes.

Contents and Overview

This training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Matplotlib for Python Developers, covers understanding the basic fundamentals of plotting and data visualization using Matplotlib. In this course, we hit the ground running and quickly learn how to make beautiful, illuminating figures with Matplotlib and a handful of other Python tools. We understand data dimensionality and set up an environment by beginning with basic plots. We enter into the exciting world of data visualization and plotting. You’ll work with line and scatter plots and construct bar plots and histograms. You’ll also explore images, contours, and histograms in depth. Plot scaffolding is a very interesting topic wherein you’ll be taken through axes and figures to help you design excellent plots. You’ll learn how to control axes and ticks, and change fonts and colors. You’ll work on backends and transformations. Then lastly you’ll explore the most important companions for Matplotlib, Pandas, and Jupyter used widely for data manipulation, analysis, and visualization. By the end of this course, you’ll be able to construct effective and beautiful data plots using the Matplotlib library for the Python programming language.

The second course, Developing Advanced Plots with Matplotlib, covers exploring advanced plots and functions with Matplotlib. In this video course, you’ll get hands-on with customizing your data plots with the help of Matplotlib. You’ll start with customizing plots, making a handful of special-purpose plots, and building 3D plots. You’ll explore non-trivial layouts, Pylab customization, and more on tile configuration. You’ll be able to add text, put lines in plots, and also handle polygons, shapes, and annotations. Non-Cartesian and vector plots are exciting to construct, and you’ll explore them further in this tutorial. You’ll delve into niche plots and visualizing ordinal and tabular data. In this video, you’ll be exploring 3D plotting, one of the best features when it comes to 3D data visualization, along with Jupyter Notebook, widgets, and creating movies for enhanced data representation. Geospatial plotting will be also be explored. Finally, you’ll learn how to create interactive plots with the help of Jupyter. By the end of this video tutorial, you’ll be able to construct advanced plots with additional customization techniques and 3D plot types.

The third course, Data Visualization Recipes with Python and Matplotlib 3, covers practical recipes for creating interactive data visualizations easily with Matplotlib 3. This course cuts down all the complexities and unnecessary details. It boils it down to the things you really need to get those visualizations going quickly and efficiently. The course gives you practical recipes to do what exactly needs to be done in the minimum amount of time. All the examples are based on real-world data with practical visualization solutions. By the end of the course, you’ll be able to get the most out of data visualizations where Matplotlib 3 is concerned.

The fourth course, Mastering Matplotlib 3, covers mastering the power of data visualization with Matplotlib 3. This course will help you delve into the latest version of Matplotlib, 3, in a step-by-step and engaging manner. Through this course, you will master advanced Matplotlib concepts and will be able to tackle any Data Visualization project with ease and with increasing complexity. By the end of the course, you will have honed your expertise and mastered data visualization using the full potential of Matplotlib 3.

By the end of the course, you’ll become a data visualizations expert with Matplotlib 3 by learning effective and practical data visualization recipes.

About the Authors

Benjamin Keller is currently a Ph.D. candidate at McMaster University and achieved his BSc in Physics with a minor in Computer Science from the University of Calgary in 2011. His current research involves numerical modeling of galaxy evolution over cosmological timescales. As an undergraduate at the U of C, he worked on stacking radio polarization to examine faint extragalactic sources. He also worked in the POSSUM Working Group 2 to determine the requirements for stacking applications for the Australian SKA Pathfinder (ASKAP) radio telescope. His current research is focused on developing and improving subgrid models used in simulations of galaxy formation and evolution. He is particularly interested in questions involving stellar feedback (supernovae, stellar winds, and so on) and its impact on galaxies and their surrounding intergalactic medium.

Harish Garg is a co-founder and software professional with more than 18 years of software industry experience. He currently runs a software consultancy that specializes in the data analytics and data science domain. He has been programming in Python for more than 12 years and has been using Python for data analytics and data science for 6 years. He has developed numerous courses in the data science domain and has also published a book involving data science with Python, including Matplotlib.

Amaya Nayak is a Data Science Domain consultant with BignumWorks Software LLP. She has more than 10 years’ experience in the fields of Python programming, data analysis, and visualization using Python and JavaScript, using tools such as D3.js, Matplotlib, ggplot, and more. With over 5 years’ experience as a data scientist, she works on various data analysis tasks such as statistical data, data munging, data extraction, data visualization, and data validation.

Course Curriculum

Chapter 1: Matplotlib for Python Developers

Lecture 1: The Course Overview

Lecture 2: Understanding Data, Dimensionality, and Why We Plot

Lecture 3: Setting Up Your Environment

Lecture 4: Beginning with the Most Basic Plots

Lecture 5: Differentiating Line and Scatter Plots

Lecture 6: Constructing Bar Plots and Histograms

Lecture 7: Exploring Images and Contours

Lecture 8: Working on Plots with Uncertainties

Lecture 9: Looking at Other Useful Plot Types

Lecture 10: Making Multiple Panel Plots

Lecture 11: Using Color Bars and Legends

Lecture 12: Workingwith the Components of a Matplotlib Plot

Lecture 13: Figure and Axes – How Do They Work?

Lecture 14: Working with Transformations

Lecture 15: Controlling Axes and Ticks

Lecture 16: Ticker Formatting

Lecture 17: Working on Back Ends

Lecture 18: The Jupyter Notebook

Lecture 19: Using Pandas to Manipulate Tabular Data

Lecture 20: Slicing and Dicing Pandas Data

Lecture 21: Pandas Built-in Plotting

Chapter 2: Developing Advanced Plots with Matplotlib

Lecture 1: The Course Overview

Lecture 2: Customizing Pylab in Style

Lecture 3: Color Deep Dive

Lecture 4: Working on Non-Trivial Layouts

Lecture 5: The Matplotlib Configuration Files

Lecture 6: Putting Lines in Place

Lecture 7: Adding Text on Your Plots

Lecture 8: Playing with Polygons and Shapes

Lecture 9: Versatile Annotating

Lecture 10: Non-Cartesian Plots

Lecture 11: Plotting Vector Fields

Lecture 12: Statistics with Boxes and Violins

Lecture 13: Visualizing Ordinal and Tabular Data

Lecture 14: Plotting with 3D Axes

Lecture 15: Looking at Various 3D Plot Types

Lecture 16: The Basemap Methods

Lecture 17: Plotting on Map Projections

Lecture 18: Adding Geography

Lecture 19: Interactive Plots in the Jupyter Notebook

Lecture 20: Event Handling with Plot Callbacks

Lecture 21: GUI Neutral Widgets

Lecture 22: Making Movies

Chapter 3: Data Visualization Recipes with Python and Matplotlib 3

Lecture 1: Course Overview

Lecture 2: Getting Data into Matplotlib

Lecture 3: Drawing Scatter Plots

Lecture 4: Creating Line Plots

Lecture 5: Visualizing Data with Bar Charts

Lecture 6: Drawing Subplots

Lecture 7: Creating Histograms

Lecture 8: Building Heatmaps

Lecture 9: Plotting Data on Box Plots

Lecture 10: Drawing Pie Charts

Lecture 11: Customizing Labels, Titles, and Legends

Lecture 12: Adding Grids and Customizing Ticks

Lecture 13: Using Matplotlib Styles

Lecture 14: Creating Custom Styles

Lecture 15: Plot Annotation

Lecture 16: Build Plots from the Ground-Up Using Plot Scaffolding

Lecture 17: Building Custom Plots Using Figures

Lecture 18: Customizing Plot Axes

Lecture 19: Building 3D Graphs Using Wireframe

Lecture 20: Creating 3D Scatter Plots

Lecture 21: Drawing 3D Bar Charts

Lecture 22: Customizing Wireframes

Lecture 23: Drawing Animated Graphs

Lecture 24: Building an Animated Histogram

Lecture 25: Creating Animated subplots

Lecture 26: Adding Interactivity to Plots

Lecture 27: Creating Visualizations that Update Interactively with Data

Lecture 28: Change the Plot Sizes

Lecture 29: Save Plot Image to a File

Lecture 30: Create Legend Outside the Plot

Lecture 31: Display Plots Inline in a Notebook

Lecture 32: Clear a Plot

Lecture 33: Change Font Sizes of Plot Elements

Lecture 34: Troubleshoot Value Errors

Chapter 4: Mastering Matplotlib 3

Lecture 1: The Course Overview

Lecture 2: Creating Plots Using the Plot Function

Lecture 3: Creating Subplots

Lecture 4: Subplot Parameters

Lecture 5: Learn How Pyplot Works

Lecture 6: Troubleshooting Pyplot

Lecture 7: Creating Interactive Plots

Lecture 8: Event Handling with Plot Callbacks

Lecture 9: GUI Neutral Widgets

Lecture 10: Converting Interactive Plots into Videos

Lecture 11: Customizing Pylab in Style

Lecture 12: Color Deep Dive

Lecture 13: Working on Non-Trivial Layouts

Lecture 14: The Matplotlib Configuration Files

Lecture 15: Stylesheets

Lecture 16: Putting Lines in Place

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 4 votes

3 stars: 7 votes

4 stars: 19 votes

5 stars: 16 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!