Master Data Visualization with Python and Matplotlib 3

Name: Master Data Visualization with Python and Matplotlib 3
Rating: 4.0 (48 reviews)

Become a data visualizations expert with Python and Matplotlib 3 by learning effective data visualization recipes.

Created byPackt Publishing

Last updated 4/2019

English

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.

Course content

4 sections • 109 lectures • 8h 48m total length

The Course Overview2:21
This video gives an overview of entire course.
Understanding Data, Dimensionality, and Why We Plot8:22
This video explains why do we need to plot data andwhat plots are appropriate for what kind of data.
Identify what we want to learn from our data
Select the appropriate kind of plot
Implement the plot using matplotlib
Setting Up Your Environment7:10
This video explains how to get your machine ready to develop plots using matplotlib.
Choose your Python distribution
Install your Python environment
Install matplotlib and related tools
Beginning with the Most Basic Plots9:39
In this video, we will see how we can make some of the most common types of plots.
Explain line/scatter plots
Explain histograms and bar charts
Label the plots and axes
Differentiating Line and Scatter Plots14:07
This video explains how the simplest kind of data is co relational and how x versus y. plot() and scatter() can be used to visualize this kind of data.
Make line plots
Make scatter plots
Learn to choose plot() or scatter()
Constructing Bar Plots and Histograms15:38
In this video, we will see how we can represent ordinal data, or the distribution of scalar datausing bar charts and histograms.
Make bar graphs
Make histograms
Tweak histograms for better clarity
Exploring Images and Contours9:18
In this video, we will see how we can represent 3D scalar fields (that is, data with x,y, and z components) with images and contours.
Make image plots
Make contour plots
Label contours and combine them with images
Working on Plots with Uncertainties3:53
This video explains that not all numbers are created equal: some have uncertainties associated with them!
Add error bars to line plots
Customize error bar appearance
Add error bars to bar plots
Looking at Other Useful Plot Types6:15
This video explains what if a region or area is what we want to show and Also, what if a scatter plot is too dense.
Make area plots
Make hexbin plots
Make 2D histograms
Making Multiple Panel Plots7:39
In this video, we will see how we can show multiple plots together.
Make multiple panel plots
Customize plot layout
Use the object oriented interface for multiple plots
Using Color Bars and Legends6:10
In this video, we will see how we can annotate the colors or styles in our plots.
Make legends
Customize legend appearance
Make colorbars with labels and ranges set
Workingwith the Components of a Matplotlib Plot3:20
In this video, we will see what components are in a matplotlib plot and how do they work together.
Take a look at figure anatomy
Examine the components of figure
See how components fit together
Figure and Axes – How Do They Work?7:27
In this video, we will learn which container objects are used for a plot,how can we construct and use them.
Create figure andaxes objects
Place axes, change their layout, clear them
Access current figure and axes objects with gcf() and gca()
Working with Transformations7:25
In this video, we will see which coordinate systems are used in a matplotlib plot and how does a data point get translated to a position in the plot image.
Learn the four coordinate systems used in matplotlib
Use transformations to go to display coordinates
Use inverse transformations to go from display coordinates
Controlling Axes and Ticks9:36
In this video, we will see how matplotlib constructs the x and y axis andhow does it decide where the ticks go.
Learn the difference between axis and axes
Learn how to place major and minor ticks manually
Learn how to use ticker locators to automatically position your ticks
Ticker Formatting8:52
In this video, we will learn how matplotlib formats the labels for the ticks on the x and y axis.
Change tick labels manually
Learn to ticker formatters
Build complex ticker formatters with functions
Working on Back Ends6:57
In this video, we will see how matplotlib actually displays a plot on the screen or write to a file.
See what backends matplotlib provides
See how to switch backends
Use interactive backends to tweak plots on the fly
The Jupyter Notebook9:19
In this video, we will see how we can use Jupyter and the notebook to present and work with matplotlib effectively.
See how the notebook is launched
See how notebook files work
Use nbconvert to make slideshows, PDF documents, and web pages
Using Pandas to Manipulate Tabular Data12:00
In this video, we will see how we can work with Pandas to make data manipulation easier.
See the Series object for 1D data
Explain the Dataframe object for 2D data
How to do algebra and other operations on Pandas objects
Slicing and Dicing Pandas Data10:55
In this video, we will see how we can use the rich features of Pandas Dataframes to select and manipulate data.
Use SQL-like joins to combine data
Apply functions to Pandas data
Group Pandas data to filter out regions
Pandas Built-in Plotting9:36
In this video, we will see how we can use Pandas together with matplotlib to make plots efficiently.
Plot Series data
Plot Dataframes
Add tables to plots
Test your knowledge

The Course Overview3:41
This video provides an overview of the entire course.
Customizing Pylab in Style11:03
In this video, we will see how we can customize the appearance of plots without manually tweaking each one.
What style sheets does matplotlib provide
How we can change stylesheets for single or multiple plots
How we can make our own custom style sheets
Color Deep Dive13:15
In this video, we will see how does matplotlib’s colormapsandcolor cycler works.
Learn how to create our own colormaps
Learn how to reset the color cycler
Learn how to inject our own color cycle
Working on Non-Trivial Layouts7:32
In this video, we will see how we can layout subplots in a non-uniform grid.
How we can make simple plot arrays with subplot2grid()
How we can make more sophisticated arrays with GridSpec()
What tools does GridSpec() offer to customize the appearance of our plot
The Matplotlib Configuration Files6:07
In this video, we will see howmatplotlib configured.
Find out where to find the matplotlib configuration files
See the available options in the default template
Modify the configuration file to customize matplotlib’s behaviour
Putting Lines in Place5:58
In this video, we will see how we can add lines, grids, and boxes to annotate a plot.
Learn how to add horizontal and vertical lines
Learn how to add boxes
Learn how to add gridlines
Adding Text on Your Plots7:07
Adding Text on Your Plots
Learn how to add text to axes
Learn how to customize this text
Learn how to add text to axes
Playing with Polygons and Shapes8:55
In this video, we will see how we can add various shapes and polygons to our plots.
Learn the different kinds of shapes we can add
Learn about z-order for overlapping shapes
Learn about making custom polygons
Versatile Annotating6:49
In this video, we will see how we can annotate our plots with arrows and text together.
Learn how to add arrow annotations
Learn the different kinds of arrow annotations
Learn how to change the shape of arrows
Non-Cartesian Plots8:02
In this video, we will see how we can plot data on non-linear scales or non-Cartesian axes.
Learn how to make polar plots
Learn how to use logarithmic, symlog, and logistic plots
Learn when to choose these tools
Plotting Vector Fields12:26
In this video, we will show how we can plot 4D vector plotsand how can we show streamlines.
Learn how to plot vector fields
Learn how to tweak and annotate these vectors
Learn how to make stream plots
Statistics with Boxes and Violins9:20
In this video, we will see how we can use box and violin plots to show statistical information.
Learn how to plot means, IQRs, and outliers with box plots
Learn how to make violin plots to show distributions
Learn how to customize the appearance of these plots
Visualizing Ordinal and Tabular Data7:29
In this video, we will show, how can we show small data sets with pie charts and tables.
Learn how to make pie charts
Learn how to modify their appearance
Learn how to append tables to plots
Plotting with 3D Axes5:40
In this video, we will see how can we generate and manipulate 3D axes.
Import the libraries needed for 3D axes
Add a 3D projection axes to a figure
Use the interactive notebook backend
Looking at Various 3D Plot Types10:42
In this video, we will see what different kinds of 3D plots we can make with matplotlib.
Plot basic lines and points
Plot surfaces and wireframes
Combine 2D projections and 3D plots
The Basemap Methods11:55
In this video, we will show how we can use basemap to generate plots on the earth’s surface.
Install and import basemap
Set up a basemap object
Choose among different map projections
Plotting on Map Projections11:02
In this video, we will show how we can actually plot our data on a map projection.
Convert between latitude, longitude, and screen coordinates
Plot points and lines on a map
Plot great circles and terminators
Adding Geography9:15
In this video, we will see how we can add features from real maps like topography, geography, and so on to a basemap plot.
Add coastlines, rivers, and lakes
Add political boundaries and topographic reliefs
Add satellite images
Interactive Plots in the Jupyter Notebook7:43
In this video, we will see how we can use the ipywidgets library to quickly and easily make interactive plots.
Install ipywidgets and use interact on our function
Use different argument types to generate different widgets
Use decorators to make interactive functions
Event Handling with Plot Callbacks8:37
In this video, we will see how can we add mouse and keyboard interactivity to our plots and their contents.
Attach a callback to a figure to register mouse events
Use different kind of mouse and keyboard events
Use selector events to modify individual plot elements
GUI Neutral Widgets11:22
In this video, we will see how we can add buttons, sliders, and other widgets in to any interactive matplotlib backend.
Add simple slider and button widgets
Use these widgets with callbacks to modify our plots
Use these widgets with the GTK backend
Making Movies7:45
In this video, we will see how can we make animated plots and movies, and save these plots as GIF and MP4 files.
Use the FuncAnimation method to animate a plot
Change the speed, frame rate, and repetition of our movie
Export our plots as GIF and MP4 files
Test your knowledge

Course Overview4:02
This video gives a glimpse of the entire course.
Getting Data into Matplotlib3:29
Learn to create plots with Matplotlib3 by getting data from various means.
Plot data from lists
Plot data from NumPy
Plot data from Pandas Dataframes
Drawing Scatter Plots5:11
Learn to create Scatter plots with Matplotlib 3.
Import dataset
Draw scatter plots
Customize scatter plots
Creating Line Plots4:17
Learn to create line plots with Matplotlib 3.
Import dataset
Draw line plots
Customize line plots
Visualizing Data with Bar Charts3:36
Learn to create Scatter plots with Matplotlib 3.
Import dataset
Draw bar plots
Customize bar plots
Drawing Subplots4:53
Learn to visualize and compare datasets using subplots.
Import dataset
Create subplots
Various ways to create subplots
Creating Histograms4:09
Learn to create Histograms with Matplotlib 3.
Import dataset
Draw Histograms
Customize Histograms
Building Heatmaps1:48
Learn to create Heatmaps with Matplotlib 3.
Import dataset
Draw Heatmaps
Customize Heatmaps
Plotting Data on Box Plots1:31
Learn to create box plots with Matplotlib 3.
Import dataset
Draw box plots
Customize box plots
Drawing Pie Charts4:05
Learn to create pie charts with Matplotlib 3.
Import dataset
Draw pie charts
Customize pie charts
Customizing Labels, Titles, and Legends5:52
Learn how to customize the Matplotlib plots
Customize Labels
Customize titles
Customize legends
Adding Grids and Customizing Ticks3:10
Learn how to add grids to plots and customize ticks
Learn how to add grids to plots
Customize grids
Customize ticks
Using Matplotlib Styles3:09
Learn what Matplotlib styles and how to use them
Introduce styles
List of styles
Apply styles
Creating Custom Styles3:20
Learn to customize styles
Modify configuration files
Add your own style
Apply your style
Plot Annotation2:10
Lean plot annotation
Introduce Plot Annotation
Various types of annotations
Apply annotations
Build Plots from the Ground-Up Using Plot Scaffolding2:48
Learn to build plots in matplotlib using plot scaffolding.
Introduce plot scaffolding
Build a plot step by step
Building Custom Plots Using Figures2:05
Learn to build custom plots using figures
Introduce figure method
Build plots using figures method
Customizing Plot Axes1:57
Learn to customize plot axes
Axes and optimizations
Apply the axes customization
Building 3D Graphs Using Wireframe2:08
Learn to building 3D graphs using wireframe
Import modules and datasets
Create 3d wireframe graphs
Creating 3D Scatter Plots1:20
Create 3D scatter plots
Import modules and datasets
Create 3d scatter plots
Drawing 3D Bar Charts1:11
Draw 3D bar charts
Import modules and datasets
Create 3d bar charts
Customizing Wireframes1:38
Learn to Customize wireframes
Import modules and datasets
Customize wireframes
Drawing Animated Graphs2:20
Learn to draw animated graphs with data from your datasets
Import modules
Read-in datasets
Create animated plots
Building an Animated Histogram1:37
Learn to build animated histograms
Import modules
Read-in datasets
Create animated histograms
Creating Animated subplots1:07
Learn to create animated subplots
Import modules
Read-in datasets
Build animated subplots
Adding Interactivity to Plots1:12
Learn how to make your plots interactive.
Import modules
Read-in datasets
Add interactivity to your plots.
Creating Visualizations that Update Interactively with Data1:47
Learn to create plots that update data interactively.
Import modules
Read-in datasets
Build interactive plots.
Change the Plot Sizes0:59
Learn to change the plot sizes
Import modules and dataset
Plot data
Change plot size
Save Plot Image to a File1:47
How to save plot image to a file
Import modules and dataset
Save plots to an image file
Save plots to a pdf file.
Create Legend Outside the Plot1:32
Move legend outside the plot area
Import modules and dataset
Display default legend
Move legend out of plot area
Display Plots Inline in a Notebook1:37
How to display plots inline in a jupyter notebook
Matplotlib jupyter notebook magic command
Behavior without the command
Behavior with the inline command
Clear a Plot0:51
Learn how to use matpotlib clear plot methods
Introduce various plot clearing methods
Demonstrate plot clearing methods
Change Font Sizes of Plot Elements2:18
How to change font properties of the plot elements
Change font sizes
Change font type
Change font color
Troubleshoot Value Errors2:24
Learn to fix matplotlib value errors
Type of value errors
Introduce value errors
Fix value error
Test your knowledge

The Course Overview3:54
This video will give you an overview about the course.
Creating Plots Using the Plot Function4:06
Learn to use the plot function to create and customize plots in Matplotlib 3.
Plot data by supplying data as lists
Visualize data from CSV files read through Pandas with Matplotlib 3
Customize plots
Creating Subplots2:01
Learn to visualize datasets on subplots using Matplotlib 3.
Define the number of sub plots, rows, and columns
Assign data to each subplot
Draw the subplots and compare them
Subplot Parameters2:13
Learn about subplot parameters by drawing a stacked bar plot using subplot function.
Define the datasets for the stacked plot
Define the figure and the subplots
Bring them all together in a single plot
Learn How Pyplot Works2:10
Explore how Pyplot works.
Understand what a Pyplot is
Explore anatomy of Pyplot
Explore the main components of plot
Troubleshooting Pyplot3:05
Learn few tips and tricks about Pyplot.
Learn how to move legend out of plot area
Change size of the plot area
Save plots to external files
Creating Interactive Plots2:56
Learn to build interactive plots with Matplotlib 3.
Introduce the sub-modules from Matplotlib that we need for adding interactivity to plots
Explore the code for the interactive plot
Demo the interactive features of the plot
Event Handling with Plot Callbacks2:06
Learn to build plots that update dynamically using event handling and plot callbacks.
Import required modules
Define a dataset that updates dynamically
Draw the plot with event handling and plot callbacks
GUI Neutral Widgets1:56
Learn to use Matplotlib GUI widgets.
Import widgets class from Matplotlib
Demo the code for drawing the plot with previous and next buttons
Demonstrate the working of the Previous and Next buttons and view their impact on the plot
Converting Interactive Plots into Videos2:12
Learn how to output interactive plots to video file.
Define a MPEG writer object
Draw the plot
Use MPEG writer to create a video file
Customizing Pylab in Style3:46
Learn to Customize Plots with Style.
Import the required modules and datasets
Introduce style sheets
Demo the impact of using style sheets on a plot’s elements
Color Deep Dive2:20
Learn how to change the color of plot elements.
Import the required modules and datasets
List all the ways to change color of the plot elements
Demo a couple of ways to show how to change the color of the plot
Working on Non-Trivial Layouts2:03
Work on Non-Trivial Layouts.
Import the required modules and datasets
See the demo to create complex plot layouts
The Matplotlib Configuration Files2:27
Learn about Matplotlib Configuration Files and how to modify them.
Import the required modules and datasets
Show where to find configuration files
Demo how to modify them
Stylesheets2:32
Learn how to work with style sheets and create your own style sheet.
Import the required modules and datasets
Create and apply your own style sheet
Putting Lines in Place3:00
Learn to draw lines on plots with Matplotlib 3.
Import the required modules and datasets
Draw the plot
Demo drawing lines on the plot
Adding Text to Your Plots1:40
Learn to add texts to plots.
Import the required modules and datasets
Draw the plot
Demo drawing text on the plot
Playing with Polygons and Shapes2:02
Draw polygons and shapes on plots.
Import the required modules and datasets
Draw the plot
Demo drawing polygons and shapes on the plot
Versatile Annotating1:57
Learn ways to annotate plots.
Import the required modules and datasets
Draw the plot
Demo the plot annotation
Non-Cartesian Plots2:00
Learn to create Non-Cartesian Plots with Matplotlib3.
Import the modules and prepare the dataset
Create non-cartesian plots
Plotting Vector Fields1:20
Learn to create Plotting Vector Fields with Matplotlib3.
Import the modules and prepare the dataset
Create vector field plots
Statistics with Boxes and Violins1:34
Learn to create Statistics with Boxes and Violins with Matplotlib3.
Import the modules and prepare the dataset
Plot statistics with boxes and violins
Visualizing Ordinal and Tabular Data2:01
Learn to create Visualizing Ordinal and Tabular Data with Matplotlib3.
Import the modules and prepare the dataset
Visualize ordinal and tabular data
Plotting with 3D Axes2:03
Learn Plotting with 3D Axes.
Import the modules and prepare the dataset
Create plots with 3D Axes
Looking at Various 3D Plot Types2:09
Learn to plot various 3D Plot Types.
Import the modules and prepare the dataset
Visualize various 3D plot types
The Basemap Methods1:59
Learn to create plots with Basemap Methods.
Import the modules and prepare the dataset
Plot with basemap methods
Plotting on Map Projections1:32
Learn to Plot on Map Projections.
Import the modules and prepare the dataset
Plot on map projections
Adding Geography1:43
Learn to Add Geography to plots.
Import the modules and prepare the dataset
Add geography to plots
Visualizing Categorical Data2:45
Learn to Visualize categorical data.
Import the modules and prepare the data
Visualize categorical data with Matplotlib
Plotting Distributions1:40
Learn to plot distributions.
Import the modules and prepare the data
Plot distributions
Visualizing Data on Multi-Plot Grids1:47
Learn to Visualize data on multi-plot grids.
Import the modules and prepare the data
Visualize data on multi-plot grids
Customizing Plots2:32
Learn to Customize plots.
Import the modules and prepare the data
Customize plots

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 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.

Who this course is for:

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.

Master Data Visualization with Python and Matplotlib 3

What you'll learn

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Course content

Matplotlib for Python Developers21 lectures • 2hr 56min

Developing Advanced Plots with Matplotlib22 lectures • 3hr 12min

Data Visualization Recipes with Python and Matplotlib 334 lectures • 1hr 27min

Mastering Matplotlib 332 lectures • 1hr 14min

Requirements

Description

Who this course is for: