Python Data Visualization Solutions [Video]

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Python Data Visualization Solutions [Video]

Dimitry Foures, Giuseppe Vettigli, Igor Milovanović

Create attractive visualizations using Python’s most popular libraries
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Video Details

ISBN 139781787122802
Course Length3 hours 27 minutes

Video Description

Effective visualization can help you get better insights from your data, and help you make better and more informed business decisions.

This video starts by showing you how to set up matplotlib and other Python libraries that are required for most parts of the course, before moving on to discuss various widely used diagrams and charts such as Gantt Charts. As you will go through the course, you will get to know about various 3D diagrams and animations. As maps are irreplaceable to display geo-spatial data, this course will show you how to build them. In the last section, we’ll take you on a thorough walkthrough of incorporating matplotlib into various environments and how to create Gantt charts using Python.

With practical, precise, and reproducible videos, you will get a better understanding of the data visualization concepts, how to apply them, and how you can overcome any challenge while implementing them.

Style and Approach

This course follows a step-by-step, recipe-based approach so you understand various aspects of data visualization. The topics are explained sequentially through a code snippet and the resulting visualization.

Table of Contents

Knowing Your Data
The Course Overview
Importing Data from CSV
Importing Data from Microsoft Excel Files
Importing Data from Fix-Width Files
Importing Data from Tab Delimited Files
Importing Data from a JSON Resource
Importing Data from a Database
Cleaning Up Data from Outliers
Importing Image Data into NumPy Arrays
Generating Controlled Random Datasets
Smoothing Noise in Real-World Data
Drawing Your First Plots and Customizing Them
Defining Plot Types and Drawing Sine and Cosine Plots
Defining Axis Lengths and Limits
Defining Plot Line Styles, Properties, and Format Strings
Setting Ticks, Labels, and Grids
Adding Legends and Annotations
Moving Spines to Center
Making Histograms
Making Bar Charts with Error Bars
Making Pie Charts Count
Plotting with Filled Areas
Drawing Scatter Plots with Colored Markers
More Plots and Customizations
Adding a Shadow to the Chart Line
Adding a Data Table to the Figure
Using Subplots
Customizing Grids
Creating Contour Plots
Filling an Under-Plot Area
Drawing Polar Plots
Visualizing the filesystem Tree Using a Polar Bar
Making 3D Visualizations
Creating 3D Bars
Creating 3D Histograms
Animating with OpenGL
Plotting Charts with Images and Maps
Plotting with Images
Displaying Images with Other Plots in the Figure
Plotting Data on a Map Using Basemap
Generating CAPTCHA
Using Right Plots to Understand Data
Understanding Logarithmic Plots
Creating a Stem Plot
Drawing Streamlines of Vector Flow
Using Colormaps
Using Scatter Plots and Histograms
Plotting the Cross Correlation Between Two Variables
The Importance of Autocorrelation
More on matplotlib Gems
Drawing Barbs
Making a Box-and-Whisker Plot
Making Gantt Charts
Making Error Bars
Making Use of Text and Font Properties
Understanding the Difference between pyplot and OO API

What You Will Learn

  • Explore your data using the capabilities of standard Python Data Library 
  • Draw your first chart and customize it
  • Use the most popular data visualization Python libraries
  • Make 3D visualizations mainly using mplot3d
  • Create charts with images and maps
  • Understand the most appropriate charts to describe your data
  • Get to know the matplotlib’s hidden gems

Authors

Table of Contents

Knowing Your Data
The Course Overview
Importing Data from CSV
Importing Data from Microsoft Excel Files
Importing Data from Fix-Width Files
Importing Data from Tab Delimited Files
Importing Data from a JSON Resource
Importing Data from a Database
Cleaning Up Data from Outliers
Importing Image Data into NumPy Arrays
Generating Controlled Random Datasets
Smoothing Noise in Real-World Data
Drawing Your First Plots and Customizing Them
Defining Plot Types and Drawing Sine and Cosine Plots
Defining Axis Lengths and Limits
Defining Plot Line Styles, Properties, and Format Strings
Setting Ticks, Labels, and Grids
Adding Legends and Annotations
Moving Spines to Center
Making Histograms
Making Bar Charts with Error Bars
Making Pie Charts Count
Plotting with Filled Areas
Drawing Scatter Plots with Colored Markers
More Plots and Customizations
Adding a Shadow to the Chart Line
Adding a Data Table to the Figure
Using Subplots
Customizing Grids
Creating Contour Plots
Filling an Under-Plot Area
Drawing Polar Plots
Visualizing the filesystem Tree Using a Polar Bar
Making 3D Visualizations
Creating 3D Bars
Creating 3D Histograms
Animating with OpenGL
Plotting Charts with Images and Maps
Plotting with Images
Displaying Images with Other Plots in the Figure
Plotting Data on a Map Using Basemap
Generating CAPTCHA
Using Right Plots to Understand Data
Understanding Logarithmic Plots
Creating a Stem Plot
Drawing Streamlines of Vector Flow
Using Colormaps
Using Scatter Plots and Histograms
Plotting the Cross Correlation Between Two Variables
The Importance of Autocorrelation
More on matplotlib Gems
Drawing Barbs
Making a Box-and-Whisker Plot
Making Gantt Charts
Making Error Bars
Making Use of Text and Font Properties
Understanding the Difference between pyplot and OO API

Video Details

ISBN 139781787122802
Course Length3 hours 27 minutes
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