Data Visualization with Python

Learning Objectives

In this course you will learn about:

• Data visualization and some of the best practices when creating plots and visuals.
• The history and architecture of Matplotlib, and how to do basic plotting with Matplotlib.
• Generating different visualization tools using Matplotlib such as line plots, area plots, histograms, bar charts, box plots, and pie charts.
• Seaborn, another data visualization library in Python, and how to use it to create attractive statistical graphics.
• Folium, and how to use to create maps and visualize geospatial data.

Syllabus

Module 1 - Introduction to Visualization Tools

• Introduction to Data Visualization
• Introduction to Matplotlib
• Basic Plotting with Matplotlib
• Dataset on Immigration to Canada
• Line Plots

Module 2 - Basic Visualization Tools

• Area Plots
• Histograms
• Bar Charts

Module 3 - Specialized Visualization Tools

• Pie Charts
• Box Plots
• Scatter Plots
• Bubble Plots

Module 4 - Extra Visualization Tools

• Waffle Charts
• Word Clouds
• Seaborn and Regression Plots

Module 5 - Creating Maps and Visualizing Geospatial Data

• Introduction to Folium and Map Styles
• Maps with Markers
• Choropleth Maps

Module 1 - Introduction to Visualization Tools

Learning Objectives

In this lesson you will learn about:

• Data visualization and some of the best practices to keep in mind when creating plots and visuals.
• The history and the architecture of Matplotlib.
• Basic plotting with Matplotlib.
• The dataset on immigration to Canada, which will be used extensively throughout the course.
• Generating line plots using Matplotlib.

Introduction to Data Visualization

Why Build Visuals ?

Best Practices

Example

Example - Remove Background

Example - Remove Border

Example - Remove Redundant Legend

Example - Remove 3D

Example - Remove Text Bolding

Example - Reduce Color

Example - Remove Wedges

Example - Thicken Lines

Example - Emphasize Bacon

Comparaison

More Examples

https://www.darkhorseanalytics.com/

Introduction to Matplotlib

Matplotlib - History

Matplotlib - Archictecture

Backend Layer

Artist Layer

Putting the Artist Layer to Use

Scripting Layer

Further Reading

https://aosabook.org/en/matplotlib.html

Basic Plotting with Matplotlib

Matplotlib - Jupyter Notebook

Jupyter notebook is open source web application that allows you to create and share documents that contain live code visualizations and some explonatory text as well. Jupyter has some specialized support for Matplotlib

Matplotlib - Plot Function

In this cours, you can already create visuals tools such as:

• histograms
• bar charts
• box plots
• and any more using one function Plot

Matplotlib - Backends - inline

magic function %matplotlib inline, the limitation of this backend is we can not modify figure when it is rendered.

Matplotlib - Backends - Notebook

With a notebook backend in place, if a plt function is called, it checks if an active figure exists, and any functions you call will be applied to this active figure. If figure does not exist, it renders a news figure.

Matplotlib - Pandas

Pandas is also a built-in implementation of it. there, plotting in pandas is as simple as calling the plot function on a given pandas series or dataframe.

Matplotlib - Pandas - Line

Matplotlib - Pandas - Histogram

Dataset on Immigration to Canada

Dataset

Immigration Data to Canada

Read Data into Pandas DataFrame

Display DataFrame

Line Plots

Dataset - Recap

Dataset - Precessed

Creating Line Plot

Module 2 - Basic Visualization Tools

Learning Objectives

In this lesson you will learn about:

• Area plots, and how to create them with Matplotlib.
• Histograms, and how to create them with Matplotlib.
• Bar charts, and how to create them with Matplotlib.

Area Plots

Dataset - Recap

Dataset - Processed

Generating Area Plots

Area Plots

Histograms

Dataset Recap

Dataset - Processed

Histograms

Numpy - Histograms

Bar Charts

Dataset - Recap

Dataset - Processed

Bar Chart

Module 3 - Specialized Visualization Tools

Learning Objectives

In this lesson you will learn about:

• Pie charts, and how to create them with Matplotlib.
• Box plots, and how to create them with Matplotlib.
• Scatter plots and bubble plots, and how to create them with Matplotlib.

Pie Charts

Dataset - Recap

Dataset - Processed

Pie Chart

Outcomes

Pie Charts Flows

https://www.surveygizmo.com/survey-blog/pie-chart-or-bar-graph

Box Plots

Dataset - Recap

Dataset - Processed

Box Plots

Scatter Plots

Dataset - Recap

Dataset - Processed

Scatter Plots

Module 4 - Extra Visualization Tools

Learning Objectives

In this lesson you will learn about:

• Generating advanced visualization tools such waffle charts and word clouds.
• Seaborn, and how to use it to generate attractive regression plots.

Waffle Charts

Word Clouds

Seaborn and Regression Plots

Regression Plots

Module 5 - Creating Maps and Visualizing

Learning Objectives

In this lesson you will learn about:

• Folium, a data visualization library in Python.
• Creating maps of different regions of the world and how to superimpose markers on top of a map.
• Creating Choropleth maps with Folium

Introduction to Folium

Creating a World Map

Creating a Map of Canada

Map Styles - Stamen Toner

Map Styles - Stamen Terrain

Maps with Markers

Add a Marker

Label the Marker

Choropleth Maps

Geojson File

Creating the Map

Dataset - Recap

Dataset - Processed

Creating the Map