Python Data Visualization

4h 36m 12s
English
Paid
September 4, 2024

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.

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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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# Title Duration
1 Motivation 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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