What you'll 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.
Requirements
Prior Python programming experience is a requirement, whereas experience with Data Analysis and Machine Learning analysis will be helpful.
Description
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 OverviewThis 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 AuthorsBenjamin 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.
Overview
Section 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
Section 2: Developing Advanced Plots with Matplotlib
Lecture 22 The Course Overview
Lecture 23 Customizing Pylab in Style
Lecture 24 Color Deep Dive
Lecture 25 Working on Non-Trivial Layouts
Lecture 26 The Matplotlib Configuration Files
Lecture 27 Putting Lines in Place
Lecture 28 Adding Text on Your Plots
Lecture 29 Playing with Polygons and Shapes
Lecture 30 Versatile Annotating
Lecture 31 Non-Cartesian Plots
Lecture 32 Plotting Vector Fields
Lecture 33 Statistics with Boxes and Violins
Lecture 34 Visualizing Ordinal and Tabular Data
Lecture 35 Plotting with 3D Axes
Lecture 36 Looking at Various 3D Plot Types
Lecture 37 The Basemap Methods
Lecture 38 Plotting on Map Projections
Lecture 39 Adding Geography
Lecture 40 Interactive Plots in the Jupyter Notebook
Lecture 41 Event Handling with Plot Callbacks
Lecture 42 GUI Neutral Widgets
Lecture 43 Making Movies
Section 3: Data Visualization Recipes with Python and Matplotlib 3
Lecture 44 Course Overview
Lecture 45 Getting Data into Matplotlib
Lecture 46 Drawing Scatter Plots
Lecture 47 Creating Line Plots
Lecture 48 Visualizing Data with Bar Charts
Lecture 49 Drawing Subplots
Lecture 50 Creating Histograms
Lecture 51 Building Heatmaps
Lecture 52 Plotting Data on Box Plots
Lecture 53 Drawing Pie Charts
Lecture 54 Customizing Labels, Titles, and Legends
Lecture 55 Adding Grids and Customizing Ticks
Lecture 56 Using Matplotlib Styles
Lecture 57 Creating Custom Styles
Lecture 58 Plot Annotation
Lecture 59 Build Plots from the Ground-Up Using Plot Scaffolding
Lecture 60 Building Custom Plots Using Figures
Lecture 61 Customizing Plot Axes
Lecture 62 Building 3D Graphs Using Wireframe
Lecture 63 Creating 3D Scatter Plots
Lecture 64 Drawing 3D Bar Charts
Lecture 65 Customizing Wireframes
Lecture 66 Drawing Animated Graphs
Lecture 67 Building an Animated Histogram
Lecture 68 Creating Animated subplots
Lecture 69 Adding Interactivity to Plots
Lecture 70 Creating Visualizations that Update Interactively with Data
Lecture 71 Change the Plot Sizes
Lecture 72 Save Plot Image to a File
Lecture 73 Create Legend Outside the Plot
Lecture 74 Display Plots Inline in a Notebook
Lecture 75 Clear a Plot
Lecture 76 Change Font Sizes of Plot Elements
Lecture 77 Troubleshoot Value Errors
Section 4: Mastering Matplotlib 3
Lecture 78 The Course Overview
Lecture 79 Creating Plots Using the Plot Function
Lecture 80 Creating Subplots
Lecture 81 Subplot Parameters
Lecture 82 Learn How Pyplot Works
Lecture 83 Troubleshooting Pyplot
Lecture 84 Creating Interactive Plots
Lecture 85 Event Handling with Plot Callbacks
Lecture 86 GUI Neutral Widgets
Lecture 87 Converting Interactive Plots into Videos
Lecture 88 Customizing Pylab in Style
Lecture 89 Color Deep Dive
Lecture 90 Working on Non-Trivial Layouts
Lecture 91 The Matplotlib Configuration Files
Lecture 92 Stylesheets
Lecture 93 Putting Lines in Place
Lecture 94 Adding Text to Your Plots
Lecture 95 Playing with Polygons and Shapes
Lecture 96 Versatile Annotating
Lecture 97 Non-Cartesian Plots
Lecture 98 Plotting Vector Fields
Lecture 99 Statistics with Boxes and Violins
Lecture 100 Visualizing Ordinal and Tabular Data
Lecture 101 Plotting with 3D Axes
Lecture 102 Looking at Various 3D Plot Types
Lecture 103 The Basemap Methods
Lecture 104 Plotting on Map Projections
Lecture 105 Adding Geography
Lecture 106 Visualizing Categorical Data
Lecture 107 Plotting Distributions
Lecture 108 Visualizing Data on Multi-Plot Grids
Lecture 109 Customizing Plots
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.


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