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Bekzod Ruzmetov

Bekzod's instruction is very clear and concise. I went from having zero knowledge of Matplotlib to creating highly customized visualizations within hours. Prerequisites in Python and Pandas are not necessarily needed but understanding the basics in both will maximize your experience in this course. I recommend to open a blank notebook and following along with Bekzod, pausing along the way read the help documentation he references, as well as read any code snippets you may not understand right away. It takes a little longer to finish the course but it's more than worth it. I'm looking forward to additional courses offered by Bekzod." - Jeff Dowden

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Bekzod's instruction is very clear and concise. I went from having zero knowledge of Matplotlib to creating highly customized visualizations within hours. Prerequisites in Python and Pandas are not necessarily needed but understanding the basics in both will maximize your experience in this course. I recommend to open a blank notebook and following along with Bekzod, pausing along the way read the help documentation he references, as well as read any code snippets you may not understand right away. It takes a little longer to finish the course but it's more than worth it. I'm looking forward to additional courses offered by Bekzod." - Jeff Dowden

"I learn a lot from the lesson until now. This lesson improves my understanding of OOP. It is so easy, interesting and amazing to use python to visualize data from the perspective of OOP." - Haitao Lyu

"This course is completely amazing. Direct to the point and use real data not simulation with numpy as usually others did. Great job Bekzod. " - Hartanto

"'I've used Matplotlib and Seaborn for a number of years. I was reviewing this to see if it was a good introduction for people I work with. The answer, yes. It's a very good introduction that covers some of the critical details necessary to navigate Matplotlib in order to customize plots." - Stephen Basco

..

After completing this course you will master Matplotlib on an intuition level and feel comfortable visualizing and customizing Matplotlib, Seaborn and Pandas charts of any complexities. More specifically, this course is a great resource if you are interested in:

  1. How Matplotlib Works

  2. How to create charts from simple to scientific ones with Matplotlib, Pandas and Seaborn

  3. How to customize charts of any complexities with ease

To achieve the objectives, I split this course into the following sections:

Matplotlib Anatomy

As the name implies, in this section you will learn how Matplotlib works and how a variety of charts are generated.

It gives you a solid understanding and a lot of aha-moments when it comes to creating and / or customizing charts that you haven't dealt with before.

Create 2D Charts

In this section, you will generate plethora of charts using Matplotlib OOP, and Pandas and mix them together to achieve the maximum efficiency and granular control over graphs.

Axes Statistical Charts

Here we will learn how to make statistical charts such as Auto Correlation, Boxplots, Violinplots and KDE plots with Matplotlib OOP and Pandas.

Seaborn

Seaborn, a high-level interface to Matplotlib helps make statistical plots with ease and charm. It is a must-know library for data exploration and super easy to learn. And in this section, we will create Regression plots, Count plots, Barplots, Factorplots, Jointplots, Boxplots, Violin plots and more.

Course Summary and Exercises

This section has dual purposes.

For one, it is a good summary of the course and provides you with exercises to test your knowledge and then provide solutions for comparison.

Secondly, If you are short-on time, you can start here and then move to other sections if you seek more granular coverage of the topic or when you have more time available.

x

  • Seaborn 0.8.1 or above

  • Pandas 0.22 or above

  • Enroll now

    What's inside

    Learning objectives

    • Learn matplotlib anatomy
    • Customize charts of any complexity with ease
    • Create a variety of charts, bar charts, line charts, stacked charts, donut and pie charts, histograms, kde plots, violinplots, boxplots, auto correlation plots, scatter plots, heatmaps
    • Feel comfortable managing various matplotlib artists such as legends, annotations, texts, patches, lines, collections, containers, axis
    • Create statistical charts with seaborn
    • Visualize data with matplotlib in oop
    • Dual axis charts

    Syllabus

    INTRODUCTION
    Introduction
    Instructor's message
    Matplotlib Anatomy
    Read more
    What you will need
    Understand how matplotlib works in the back scene, so you can get better at creating appealing graphs for websites and magazines
    Line2D: Add Lines
    Line2D: Properties
    Rectangle: Add Patches
    Rectangle: Properties
    FancyBboxPatch: Properties
    Text: Add Text
    Text: Properties

    You probably have gone through so many quizzes in your life and I don't want it to be one of them. I just have one question, if you don't mind.

    Annotations: Add Text
    Annotations: Properties
    Legends: Add Legends
    Legends: Properties
    Axis: Labels and Spines

    Yup. This time more than one question ahead. But, I am sure you will get all the questions right, because you have watched the videos. I am not worried about you. It is the other guy I am worried about :)

    Axis: Ticks
    Axis: Tick Formatters and Locators
    CREATE 2D CHARTS
    Line charts
    Bar charts: Basics
    Bar charts: Grouped
    Bar charts: Stacked
    Scatter plots
    Pie charts
    Donut charts
    Histograms
    Polar charts
    Dual Axis charts
    AXES STATISTICAL CHARTS
    Autocorrelation
    KDE plots
    Boxplots
    Violinplots
    Heatmap and Colorbar
    SEABORN
    Regplot
    Countplot
    Barplot
    Boxplot
    Violinplot and Swarmplot
    Factorplot
    Distplot
    Jointplot
    Pairplot
    Jump here if you want a short-cut
    Legends
    Ticks
    Patches
    Lines
    Annotations
    PathCollections (Scatter Plot Markers)
    Axes Spines

    Good to know

    Know what's good
    , what to watch for
    , and possible dealbreakers
    Delves deep into Matplotlib anatomy, fortifying your understanding of chart generation
    Develops skills in Matplotlib, Pandas, and Seaborn to visualize data comprehensively
    Teaches techniques to customize charts of any complexity, catering to specific visualization needs
    Covers a wide range of chart types, including bar charts, line charts, histograms, and KDE plots, to meet diverse visualization requirements
    Empowers learners with a thorough understanding of Matplotlib's functionalities, enabling them to create and modify charts with ease
    Suitable for beginners seeking to establish a solid foundation in data visualization using Matplotlib

    Save this course

    Save Complete Course on Data Visualization, Matplotlib and Python to your list so you can find it easily later:
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    Activities

    Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete Course on Data Visualization, Matplotlib and Python with these activities:
    Review Python and Pandas basics
    Review Python and Pandas basics to enhance prerequesite knowledge and solidify foundational concepts
    Browse courses on Python
    Show steps
    • Revisit Python syntax, data types, and control flow
    • Refresh Pandas data structures, like DataFrames and Series
    Join a study group or discussion forum
    Join a study group or discussion forum to connect with peers, ask questions, and share knowledge
    Browse courses on Matplotlib
    Show steps
    • Identify relevant study groups or discussion forums
    • Actively participate in discussions and ask for help when needed
    Compile notes from multiple sources
    This will build a stronger foundation to improve your readiness at the beginning of the course, when none of the material covered is new to you.
    Browse courses on Data Visualization
    Show steps
    • Gather all relevant materials
    • Review the material and identify key concepts
    • Organize the material into a logical structure
    • Create a study plan
    12 other activities
    Expand to see all activities and additional details
    Show all 15 activities
    Explore Matplotlib's documentation
    Explore Matplotlib's documentation to familiarize yourself with its capabilities and best practices
    Browse courses on Matplotlib
    Show steps
    • Read the Matplotlib User Guide
    • Follow tutorials on creating specific chart types
    • Experiment with different Matplotlib features
    Practice Matplotlib Functions
    Practicing Matplotlib functions will help you improve your understanding of how Matplotlib works, and will also help you refine your programming skills.
    Browse courses on Matplotlib
    Show steps
    • Find a set of Matplotlib functions to practice.
    • Write a Python script to practice using the functions.
    • Run the script and check the output.
    • Repeat steps 2-3 until you are comfortable using the functions.
    Learn Matplotlib Axes
    Examining tutorials about Matplotlib Axes will help give you a better grounding in Matplotlib and how to use it.
    Browse courses on Matplotlib
    Show steps
    • Search for tutorials on Matplotlib Axes
    • Follow the steps in the tutorials
    • Create your own custom charts using Matplotlib Axes
    Discuss Matplotlib with Peers
    Discussing Matplotlib with peers will help you to learn from others, share your own knowledge, and get feedback on your work.
    Browse courses on Matplotlib
    Show steps
    • Find a peer group or online forum where you can discuss Matplotlib
    • Ask questions about Matplotlib
    • Share your own knowledge and experience with Matplotlib
    • Help others to troubleshoot their Matplotlib code
    Create Python Matplotlib Visualization
    Creating a Matplotlib visualization will help you solidify your understanding of how Matplotlib works, and will also help you refine your programming skills.
    Browse courses on Matplotlib
    Show steps
    • Choose a dataset to visualize.
    • Import the Matplotlib library.
    • Create a figure and axes object.
    • Plot the data using the appropriate Matplotlib function.
    • Add a title, labels, and legend to the plot.
    Create various chart types with Matplotlib
    Create various chart types with Matplotlib to reinforce practical skills and deepen understanding
    Browse courses on Matplotlib
    Show steps
    • Generate line charts, bar charts, and scatter plots
    • Customize chart elements, such as labels, colors, and legends
    • Create more complex charts, such as histograms and heatmaps
    Practice Customizing Matplotlib Charts
    Practicing customizing Matplotlib charts will help you to develop your skills in using Matplotlib to create visually appealing and informative charts.
    Browse courses on Matplotlib
    Show steps
    • Find a dataset to visualize
    • Create a basic chart using Matplotlib
    • Customize the chart to change the appearance of the lines, markers, and axes
    • Add a title, labels, and a legend to the chart
    • Save the chart as an image or PDF
    Write a Blog Post on Matplotlib
    Writing a blog post on Matplotlib will help you solidify your understanding of Matplotlib, and will also help you improve your writing and communication skills.
    Browse courses on Matplotlib
    Show steps
    • Choose a topic for your blog post.
    • Research the topic and gather information.
    • Write the blog post.
    • Edit and proofread the blog post.
    • Publish the blog post.
    Create Data Visualizations with Matplotlib
    Creating your own data visualizations will give you hands-on experience with Matplotlib and help you to develop your understanding of how to use it to create effective visuals.
    Browse courses on Matplotlib
    Show steps
    • Choose a dataset to visualize
    • Select the appropriate chart type for your data
    • Create your chart using Matplotlib
    • Customize your chart to make it visually appealing
    • Save your chart as an image or PDF
    Create a Data Visualization Project
    Creating a data visualization project will give you the opportunity to apply your skills in Matplotlib to a real-world problem.
    Browse courses on Matplotlib
    Show steps
    • Define the goal of your project
    • Choose a dataset to visualize
    • Select the appropriate chart type for your data
    • Create your chart using Matplotlib
    • Customize your chart to make it visually appealing
    • Write a report or give a presentation on your project
    Develop a data visualization project
    Develop a data visualization project to apply and demonstrate your Matplotlib skills
    Browse courses on Matplotlib
    Show steps
    • Identify a dataset and define a visualization goal
    • Select appropriate Matplotlib charts and customize them
    • Create a presentation or report to showcase your project
    Contribute to the Matplotlib Project
    Contributing to the Matplotlib project will give you the opportunity to learn more about Matplotlib and its development process, and to make a valuable contribution to the open source community.
    Browse courses on Matplotlib
    Show steps
    • Find a bug or feature request that you want to work on
    • Fork the Matplotlib repository
    • Make changes to the code
    • Submit a pull request
    • Respond to feedback from the Matplotlib team

    Career center

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