Python Data Visualization

4h 36m 12s
English
Paid

Course description

Have you ever been confused by all the different python plotting libraries? Have you tried to make a "simple" plot and gotten stuck and been unable to move forward? Do you want to make sophisticated, interactive data visualizations in python? If you answer yes, to any of these questions, then this course is for you.

Read more about the course

The python data visualization landscape has many different libraries. They are all powerful and useful but it can be confusing to determine what works best for you. This course is unique because you will learn about many of the most popular python visualization libraries. You will start by learning how to use each library to build simple visualizations. You will also explore more complex usage and identify the scenarios where each library shines.

By the end of this course, you will have a basic working knowledge of how to visualize data in python using multiple libraries. You will also learn which library is best for you and your coding style. Along the way, you'll learn general visualization concepts to make your plots more effective.

In addition to the overview material, we will cover some of the more complex, interactive visualization dashboard technologies.

In this course, you will:

  • Review the python visualization landscape
  • Explore core visualization concepts
  • Use matplotlib to build and customize visualizations
  • Build and customize simple plots with pandas
  • Learn about seaborn and use it for statistical visualizations
  • Create visualizations using Altair
  • Generate interactive plots using the Plotly library
  • Design interactive dashboards using Streamlit
  • Construct highly custom and flexible dashboards using Plotly's Dash framework

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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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