Python Data Visualization
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
Watch Online
Watch Online Python Data Visualization
All Course Lessons (144)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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 |
Unlock unlimited learning
Get instant access to all 143 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.
Learn more about subscriptionComments
0 commentsSimilar courses
Eve: Building RESTful APIs with MongoDB and Flask
Distributed Tasks Demystified with Celery, SQS & Python
HTMX + Flask: Modern Python Web Apps, Hold the JavaScript
Deployment from Scratch
Want to join the conversation?
Sign in to comment