CF

Python Data Visualization

4h 36m 12s
English
Paid

Unlock the potential of Python data visualization with ease and confidence. Have you been overwhelmed by the myriad of Python plotting libraries? Struggled to create a "simple" plot and found yourself unable to proceed? Do you aspire to build sophisticated, interactive data visualizations in Python? If you've nodded along to these questions, this course is tailored just for you.

Embarking on Your Visualization Journey

The Python data visualization ecosystem is rich with numerous libraries, each offering powerful and unique capabilities. Yet, the challenge often lies in deciphering which library best suits your needs. This course stands out by introducing you to many of the most popular Python visualization libraries, giving you a foundational understanding of each.

Course Learning Outcomes

Throughout this course, you will embark on a journey starting with the basics and advancing to complex visualization tasks. You'll discover which library aligns best with your coding style and data visualization needs, ensuring you can choose the right tool for the job.

By the course's end, you'll possess a comprehensive working knowledge of visualizing data in Python using several libraries. You'll also gain insights into general visualization principles, enhancing the clarity and impact of your plots.

Diving Deeper into Visualization Tools

Beyond foundational content, this course dives into advanced and interactive visualization dashboard technologies, equipping you with the skills to create dynamic and engaging data displays.

Core Topics Covered

  • Review the Python visualization landscape
  • Explore core visualization concepts
  • Utilize Matplotlib for building and customizing visualizations
  • Craft simple plots using Pandas and tailor them to your needs
  • Explore Seaborn for statistical visualizations
  • Create compelling visualizations with Altair
  • Generate interactive plots using the Plotly library
  • Design interactive dashboards utilizing Streamlit
  • Construct custom and flexible dashboards via Plotly's Dash framework

Embark on this illuminating Python Data Visualization course and transform your data into powerful, compelling stories that inform and engage.

Additional

https://github.com/talkpython/python-data-visualization

About the Author: Talk Python Training

Talk Python Training thumbnail

Talk Python Training is the paid course platform of Michael Kennedy, the host of the long-running Talk Python To Me podcast — one of the most-listened-to podcasts in the Python ecosystem. The course platform extends Michael's interview-based knowledge of the field into structured video courses taught by Michael and a curated set of guest instructors.

The course catalog covers the full Python landscape: web development with Django, Flask, FastAPI, and the broader async-Python stack; data science and pandas; LLM / RAG application development; testing and CI/CD; deployment patterns; the data-engineering side of Python; and a long list of practical Python patterns aimed at working developers. Few platforms cover the language with this much breadth from inside the Python community itself.

The CourseFlix listing under this source carries over 18 Talk Python Training courses spanning that range. Material is paid; Talk Python Training runs on per-course pricing on the original platform. Courses are aimed at developers using Python as a serious primary language rather than as a scripting tool.

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#1: Motivation
All Course Lessons (144)
#Lesson TitleDurationAccess
1
Motivation Demo
00:26
2
Statistics aren't enough
00:54
3
Why visualize data?
01:01
4
Why Python?
00:48
5
Python visualization ecosystem
00:37
6
Course objectives
00:54
7
Topic outline
01:22
8
Python check
01:07
9
Source code
00:23
10
Meet your instructor
00:54
11
Intro to Visualization concepts
00:48
12
Aesthetics
01:22
13
Data types
00:52
14
Visualization variables
01:14
15
Colors
01:34
16
Small multiple plots
01:03
17
Analysis types
01:15
18
Working with data
01:09
19
Matplotlib introduction
00:30
20
Matplotlib history
01:00
21
Matplotlib landscape
00:47
22
System setup
02:38
23
Data set
01:50
24
Figure overview
01:08
25
Interface types
01:42
26
Launching notebooks
01:13
27
Reading data
02:04
28
Pyplot example
02:13
29
Object Oriented API
04:47
30
Histograms
03:35
31
Figures and Axes
05:36
32
Saving images
01:52
33
Quick reference
01:15
34
Line plots
04:20
35
Bar charts
01:50
36
Scatter plots
05:26
37
Styles
02:52
38
Regression
03:16
39
Customizing multiple plots
03:35
40
References
01:41
41
Summary
01:41
42
Pandas introduction
00:22
43
Pandas overview
00:53
44
API overview
01:34
45
Basic API examples
05:42
46
API summary
01:03
47
Specialized hist and boxplot API
01:00
48
Advanced specialized plots
05:02
49
Advanced plot summary
01:04
50
Pandas conclusion
01:15
51
Introduction to Seaborn
00:30
52
Seaborn overview
01:42
53
Getting started
00:59
54
Figure and axes level plots
01:58
55
Data set changes
01:54
56
Displot
04:17
57
Catplot
03:33
58
Relplot
01:47
59
Seaborn API summary
01:24
60
Displot relplot and facetting
04:41
61
Catplot API summary
03:56
62
Specialized plots
01:09
63
Heatmap
04:33
64
Pair and jointplot
04:32
65
Customizing Seaborn summary
01:26
66
Seaborn summary
01:16
67
Introduction to Altair
00:43
68
Overview
01:02
69
Vega lite
01:17
70
Installing
00:58
71
Shorthand API
01:27
72
Basic shorthand API
03:48
73
Additional examples of the basic API
02:57
74
Longhand API
03:39
75
Longhand overview
01:38
76
Data type
01:27
77
Types viz alterations
01:25
78
Concat charts
02:34
79
Faceting
01:23
80
Layers
02:14
81
Multiple chart summary
00:59
82
Amazon data set
02:53
83
Amazon authors
05:20
84
Reference example
01:10
85
Conclusion
01:19
86
Introduction to Plotly
00:35
87
Overview
01:07
88
API intro
01:09
89
Installing
00:54
90
Basic plotting
03:04
91
Customizing
02:43
92
Additional plot types
03:43
93
API overview
01:34
94
Scatter plots
03:18
95
Line bar area
02:39
96
Regression treemap heatmap
04:54
97
Facetting
03:23
98
Annotations
02:43
99
Annotation summary
00:51
100
Conclusion
01:11
101
Introduction
00:32
102
Background
00:58
103
Installation
00:57
104
Basic app concepts
00:59
105
Simple app example
02:33
106
Streamlit running overview
02:07
107
API summary
01:33
108
Widget Intro
02:44
109
Widget interactivity
01:14
110
User input
02:34
111
Show charts
03:01
112
Sidebar intro
02:44
113
Sidebar details
02:30
114
Conclusion
01:10
115
Intro
00:35
116
Overview
00:47
117
Why Dash?
00:55
118
Getting started
00:35
119
Program structure
01:03
120
First app
02:49
121
Running app
02:20
122
Component overview
01:40
123
HTML
03:43
124
Interactive app
03:41
125
Interactive app demo
01:48
126
Callback reference
00:42
127
Final app overview
00:41
128
Full app part 1
03:33
129
Full app data filtering
04:28
130
Full app demo
02:13
131
Advanced topics
00:58
132
Conclusion
01:23
133
Course review
01:15
134
Objectives
01:14
135
Data vis concepts
01:04
136
Matplotlib
01:24
137
Pandas
01:00
138
Seaborn
01:12
139
Altair
01:08
140
Plotly
00:48
141
Streamlit
00:50
142
Dash
00:58
143
My workflow
01:07
144
Thank you
00:35
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Frequently asked questions

What prerequisites should I have before starting this course?
Before enrolling in the course, you should have a basic understanding of Python programming. Familiarity with Python libraries and general coding concepts will be beneficial as the course covers various Python visualization libraries and involves coding exercises.
What types of projects will I work on during the course?
Throughout the course, you will work on projects that involve creating a variety of plots, including histograms, line plots, bar charts, and scatter plots using libraries like Matplotlib and Seaborn. You'll also explore advanced visualizations and interactive dashboard technologies.
Who is the target audience for this course?
The course is designed for individuals who want to enhance their data visualization skills using Python. It's suitable for those who have been overwhelmed by the number of plotting libraries available and wish to gain confidence in creating both simple and complex visualizations.
How does the course depth compare to other data visualization courses?
This course offers a comprehensive introduction to the Python data visualization ecosystem by covering multiple libraries like Matplotlib and Seaborn. It starts with basic concepts and progresses to advanced visualizations, making it suitable for both beginners and those looking to deepen their understanding.
What specific visualization tools will I learn in this course?
The course covers a range of visualization tools including Matplotlib and Seaborn. You'll learn about different types of plots such as histograms, line plots, and scatter plots, as well as advanced visualization techniques and interactive dashboard technologies.
What topics are not covered in this course?
The course does not cover non-Python data visualization tools or libraries that are outside of the Python ecosystem. It focuses specifically on Python-based libraries and does not include topics like R or JavaScript visualization frameworks.
How much time should I expect to commit to complete the course?
The course consists of 144 lessons, and while the total runtime is not specified, you should expect to spend a significant amount of time on each lesson to fully grasp the concepts, practice coding exercises, and work on projects. Planning for several weeks of study is advisable.