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What you'll get

  • Govt. Verified Certification
  • Job Credibility
  • Certification Valid for Life
  • Lifetime Access To E-learning

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Course Content

Total: 127 lectures
  • Introduction
  • Working in interactive mode
  • Working in non-interactive mode
  • Reading from external files and plotting
  • Changing and resetting default environment variables
  • Introduction
  • Line plot
  • Bar plot
  • Scatter plot
  • Bubble plot
  • Stacked plot
  • Pie plot
  • Table chart
  • Polar plot
  • Histogram
  • Box plot
  • Violin plot
  • Reading and displaying images
  • Heatmap
  • Hinton diagram
  • Contour plot
  • Triangulations
  • Stream plot
  • Path
  • Introduction
  • Plotting multiple graphs on the same axes
  • Plotting subplots on the same figure
  • Plotting multiple figures in a session
  • Logarithmic scale
  • Using units of measurement
  • Introduction
  • Color, line style, and marker customization
  • Working with standard colormaps
  • User-defined colors and colormaps
  • Working with legend
  • Customizing labels and titles
  • Using autoscale and axis limits
  • Customizing ticks and ticklabels
  • Customizing spines
  • Twin axes
  • Using hatch
  • Using annotation
  • Using style sheets
  • Introduction
  • Plotting a correlation matrix using pyplot and object-oriented APIs
  • Plotting patches using object-oriented API
  • Plotting collections using object-oriented API
  • Using property cycler
  • Using Path effects
  • Using transforms
  • Taking control of axes positions
  • GridSpec for figure layout
  • Using origin and extent for image orientation
  • Geographical plotting using geopandas
  • Introduction
  • Using mathematical expressions with a font dictionary
  • Annotating a point on a polar plot
  • Using ConnectionPatch
  • Using a text box
  • Plotting area under an integral curve
  • Defining custom markers
  • Fractions, regular mathematical expressions, and symbols
  • Word embeddings in two dimensions
  • Introduction
  • Saving the figure in various formats
  • Avoiding truncation while saving the figure
  • Saving partial figures
  • Managing image resolution
  • Managing transparency for web applications
  • Creating multi-page PDF reports
  • Introduction
  • Events and callbacks
  • Widgets
  • Animation
  • Introduction
  • Using the Slider and Button Widgets of Matplotlib
  • Using the Slider and Button widgets of Tkinter GUI
  • Embedding Matplotlib in a Tkinter GUI application
  • Using the Slider and Button widgets of WxPython GUI
  • Embedding Matplotlib in to a wxPython GUI application
  • Using the Slider and Button widgets of Qt's GUI
  • Embedding Matplotlib in to a Qt GUI application
  • Introduction
  • Line plot
  • Scatter plot
  • Bar plot
  • Polygon plot
  • Contour plot
  • Surface plot
  • Wireframe plot
  • Triangular surface plot
  • Plotting 2D data in 3D
  • 3D visualization of linearly non-separable data in 2D
  • Word embeddings
  • Introduction
  • Understanding attributes in axisartist
  • Defining curvilinear grids in rectangular boxes
  • Defining polar axes in rectangular boxes
  • Using floating axes for a rectangular plot
  • Creating polar axes using floating axes
  • Plotting planetary system data on floating polar axes
  • Introduction
  • Plotting twin axes using the axisartist and axesgrid1 toolkits
  • Using AxesDivider to plot a scatter plot and associated histograms
  • Using AxesDivider to plot a colorbar
  • Using ImageGrid to plot images with a colorbar in a grid
  • Using inset_locator to zoom in on an image
  • Using inset_locator to plot inset axes
  • Introduction
  • Plotting basic map features
  • Plotting projections
  • Using grid lines and labels
  • Plotting locations on the map
  • Plotting country maps with political boundaries
  • Plotting country maps using GeoPandas and cartopy
  • Plotting populated places of the world
  • Plotting the top five and bottom five populated countries
  • Plotting temperatures across the globe
  • Plotting time zones
  • Plotting an animated map
  • Introduction
  • Relational plots
  • Categorical plots
  • Distribution plots
  • Regression plots
  • Multi-plot grids
  • Matrix plots

Description

Welcome to this course "Certificate in Matplotlib 3" where you will learn about Matplotlib which is a multi-perform data visualization tool for designing any level of interactive data visualisation. So, for knowing more about matplotlib check throughout the course.

Here you will learn about the overview of the course, you will explore images and contours and the working on plots with many uncertainties are also a part of this course. You can look at other useful types of plots, you can make multiple panel plots using colour bars and legends.

Working of Figure and axes are also discussed.

Working with Transformations, controlling Axes and Ticks, ticker Formatting, Working on Back Ends, The Jupiter Notebook, Using Pandas to Manipulate Tabular Data, Slicing and Dicing Pandas Data, pandas Built in Plotting are also explained in this topic.

Some advanced plots with matplotlib like:

  • Customizing Pylab in Style
  • Color Deep Dive
  • Working on Non-Trivial Layouts
  • The Matplotlib Configuration Files
  • Putting Lines in Place
  • Adding Text on Your Plots
  • Playing with Polygons and Shapes
  • Versatile Annotating
  • Non-Cartesian Plots
  • Plotting Vector Fields
  • Statistics with Boxes and Violins
  • Visualizing Ordinal and Tabular Data
  • Plotting with 3D Axes
  • Looking at Various 3D Plot Types
  • The Basemap Methods
  • Plotting on Map Projections
  • Adding Geography
  • Interactive Plots in the Jupyter Notebook
  • Event Handling with Plot Callbacks
  • GUI Neutral Widgets
  • Making Movies

These topics are explained in a very brief manner. You will learn more things about this topic so carry on with this course.

You are requiring some programming experience with data analysis and machine learning. It would be helpful to your course. But these requirements are not necessary as you will learn this course from the very beginning to the advanced level.

This course is for Developers, students pursuing computer science, programming enthusiasts, coding enthusiasts, data scientists and data analysts.

So, what are you waiting for to come and enroll your name in this course?

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