Python Data Visualization

Name: Python Data Visualization
Price: 9 USD
Availability: InStock

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.

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

Python Data Visualization

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