Python Data Visualization course

Code and examples from our Python Data Visualization course.

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

View the full course outline.

Developers and Data Analysts that have some experience with python but have not developed a competency in a python visualization library. This course is also helpful for those that feel restricted by their current plotting tools and wish to explore other options.

Note: All software used during this course, including editors, Python language, etc., are 100% free and open source. You won't have to buy anything to take the course.

Data sciense is one of the hottest topic of the year and data visualization is a core skillset needed to properly communicate your results and discoveries. Take this course to get good at a wide variety of modern Python-based visualization libraries.

项目提供统一启动入口 launcher.py，从同一份配置（examples_manifest.json + launch_schema.json）读取示例元数据并启动各类示例：

# 1. 安装依赖
pip install -r requirements.txt

# 2. 查看所有可用示例（从 manifest 实时读取）
python launcher.py list

# 3. 交互式选择（推荐）
python launcher.py

# 4. 或直接启动指定示例
python launcher.py run dash-full   # 启动完整 Dash 应用
python launcher.py run st-1        # 启动 Streamlit 示例
python launcher.py run nb-ch5-1    # 打开 Notebook

启动前可单独执行环境检查，明确区分四类问题，并额外校验 README 与配置的一致性：

# 检查所有示例的环境就绪状态 + README 是否与配置漂移
python launcher.py check

# 检查指定示例（仅环境，不校验 README）
python launcher.py check dash-1

检查结果会按 缺依赖 / 缺数据文件 / 缺入口脚本 / 缺启动器 四类分别列出，并给出对应解决方法。若发现 README 与配置不一致，会提示运行 python launcher.py generate-readme。

启动入口由两个 JSON 配置文件驱动，职责分离、互不干扰：

新增示例只改 examples_manifest.json，不需要动 launch_schema.json，也不需要改 launcher.py。

下表由 examples_manifest.json 自动生成，运行 python launcher.py generate-readme 更新。

每种类型的最小配置如下（由 launch_schema.json 自动生成）：

Dash (type: dash)

"dash-your-id": {
  "type": "dash",
  "file": "dash_simple_app_1.py",
  "title": "简短标题",
  "desc": "一句话描述",
  "data_files": ["EPA_fuel_economy_summary.csv"],
  "deps": ["pandas", "dash", "plotly"]
}

Notebook (type: notebook)

"nb-chX-Y": {
  "type": "notebook",
  "file": "ch3-exercise-1.ipynb",
  "title": "简短标题",
  "desc": "一句话描述",
  "data_files": ["EPA_fuel_economy.csv"],
  "deps": ["pandas", "numpy", "matplotlib"]
}

Static (type: static)

"static-your-id": {
  "type": "static",
  "file": "dash_html_gen.py",
  "title": "简短标题",
  "desc": "一句话描述",
  "data_files": [],
  "deps": ["dash", "plotly"]
}

Streamlit (type: streamlit)

"st-your-id": {
  "type": "streamlit",
  "file": "st_simple_1.py",
  "title": "简短标题",
  "desc": "一句话描述",
  "data_files": ["EPA_fuel_economy_summary.csv"],
  "deps": ["pandas", "streamlit", "plotly"]
}

示例 ID 命名约定：dash-* / st-* / nb-chX-Y（第X章第Y题）。

如果需要支持一种全新的示例类型（例如 bokeh 或 gradio），在 launch_schema.json 中追加一条类型规则：

"bokeh": {
  "command": "{python}",
  "args_template": ["-m", "bokeh", "serve", "{file}"],
  "cwd": "code",
  "check_package": "bokeh",
  "fallbacks": []
}

然后在 examples_manifest.json 中使用 "type": "bokeh" 即可，无需修改任何 Python 代码。