Job Credibility
Certification Valid for Life
Live Classes
Certificate of Completion

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

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?

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

₹3,500 ₹15,000

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