以下文为例：

1. Copy HTML Source code from the web page.
2. Transform the HTML code to Markdown code:
   https://codebeautify.org/html-to-markdown
3. Correcting the relative links:

import re
# Note! the "r" char before is required.
pattern = re.compile(r"\((0[0-9]\.[^)]*\.html)\)", re.M|re.I)
# Note! the "r" char before is required.
pt.sub(r"(https://jakevdp.github.io/PythonDataScienceHandbook/\1)", s)

4. Use the Corrected Markdown source code as you want.

https://jakevdp.github.io/PythonDataScienceHandbook/index.html

Python Data Science Handbook

This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks.

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license.

If you find this content useful, please consider supporting the work by buying the book!

Table of Contents¶

Preface¶

1. IPython: Beyond Normal Python¶

• Help and Documentation in IPython
• Keyboard Shortcuts in the IPython Shell
• IPython Magic Commands
• Input and Output History
• IPython and Shell Commands
• Errors and Debugging
• Profiling and Timing Code
• More IPython Resources

2. Introduction to NumPy¶

• Understanding Data Types in Python
• The Basics of NumPy Arrays
• Computation on NumPy Arrays: Universal Functions
• Aggregations: Min, Max, and Everything In Between
• Computation on Arrays: Broadcasting
• Comparisons, Masks, and Boolean Logic
• Fancy Indexing
• Sorting Arrays
• Structured Data: NumPy's Structured Arrays

3. Data Manipulation with Pandas¶

• Introducing Pandas Objects
• Data Indexing and Selection
• Operating on Data in Pandas
• Handling Missing Data
• Hierarchical Indexing
• Combining Datasets: Concat and Append
• Combining Datasets: Merge and Join
• Aggregation and Grouping
• Pivot Tables
• Vectorized String Operations
• Working with Time Series
• High-Performance Pandas: eval() and query()
• Further Resources

4. Visualization with Matplotlib¶

• Simple Line Plots
• Simple Scatter Plots
• Visualizing Errors
• Density and Contour Plots
• Histograms, Binnings, and Density
• Customizing Plot Legends
• Customizing Colorbars
• Multiple Subplots
• Text and Annotation
• Customizing Ticks
• Customizing Matplotlib: Configurations and Stylesheets
• Three-Dimensional Plotting in Matplotlib
• Geographic Data with Basemap
• Visualization with Seaborn
• Further Resources

5. Machine Learning¶

• What Is Machine Learning?
• Introducing Scikit-Learn
• Hyperparameters and Model Validation
• Feature Engineering
• In Depth: Naive Bayes Classification
• In Depth: Linear Regression
• In-Depth: Support Vector Machines
• In-Depth: Decision Trees and Random Forests
• In Depth: Principal Component Analysis
• In-Depth: Manifold Learning
• In Depth: k-Means Clustering
• In Depth: Gaussian Mixture Models
• In-Depth: Kernel Density Estimation
• Application: A Face Detection Pipeline
• Further Machine Learning Resources

Appendix: Figure Code¶