# Python Data Science Handbook
[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/jakevdp/PythonDataScienceHandbook/master?filepath=notebooks%2FIndex.ipynb)
[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb)
This repository contains the entire [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do), in the form of (free!) Jupyter notebooks.
![cover image](notebooks/figures/PDSH-cover.png)
## How to Use this Book
- Read the book in its entirety online at https://jakevdp.github.io/PythonDataScienceHandbook/
- Run the code using the Jupyter notebooks available in this repository's [notebooks](notebooks) directory.
- Launch executable versions of these notebooks using [Google Colab](http://colab.research.google.com): [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb)
- Launch a live notebook server with these notebooks using [binder](https://beta.mybinder.org/): [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/jakevdp/PythonDataScienceHandbook/master?filepath=notebooks%2FIndex.ipynb)
- Buy the printed book through [O'Reilly Media](http://shop.oreilly.com/product/0636920034919.do)
## About
The book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases.
The book introduces the core libraries essential for working with data in Python: particularly [IPython](http://ipython.org), [NumPy](http://numpy.org), [Pandas](http://pandas.pydata.org), [Matplotlib](http://matplotlib.org), [Scikit-Learn](http://scikit-learn.org), and related packages.
Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project,
[A Whirlwind Tour of Python](https://github.com/jakevdp/WhirlwindTourOfPython): it's a fast-paced introduction to the Python language aimed at researchers and scientists.
See [Index.ipynb](http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb) for an index of the notebooks available to accompany the text.
## Software
The code in the book was tested with Python 3.5, though most (but not all) will also work correctly with Python 2.7 and other older Python versions.
The packages I used to run the code in the book are listed in [requirements.txt](requirements.txt) (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use).
To install the requirements using [conda](http://conda.pydata.org), run the following at the command-line:
```
$ conda install --file requirements.txt
```
To create a stand-alone environment named ``PDSH`` with Python 3.5 and all the required package versions, run the following:
```
$ conda create -n PDSH python=3.5 --file requirements.txt
```
You can read more about using conda environments in the [Managing Environments](http://conda.pydata.org/docs/using/envs.html) section of the conda documentation.
## License
### Code
The code in this repository, including all code samples in the notebooks listed above, is released under the [MIT license](LICENSE-CODE). Read more at the [Open Source Initiative](https://opensource.org/licenses/MIT).
### Text
The text content of the book is released under the [CC-BY-NC-ND license](LICENSE-TEXT). Read more at [Creative Commons](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode).
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python 数据科学手册
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python 数据科学手册 (264个子文件)
main.css 5KB
pygments.css 4KB
icons.css 2KB
ipynb.css 552B
births.csv 258KB
births.csv 258KB
BicycleWeather.csv 229KB
california_cities.csv 58KB
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state-population.csv 57KB
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data.csv 113B
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about.html 1KB
analytics.html 1KB
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04.13-Geographic-Data-With-Basemap.ipynb 2.61MB
05.11-K-Means.ipynb 2.01MB
06.00-Figure-Code.ipynb 1.76MB
05.11-K-Means.ipynb 1.67MB
06.00-Figure-Code.ipynb 1.6MB
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05.10-Manifold-Learning.ipynb 951KB
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05.07-Support-Vector-Machines.ipynb 776KB
03.11-Working-with-Time-Series.ipynb 663KB
04.12-Three-Dimensional-Plotting.ipynb 647KB
05.09-Principal-Component-Analysis.ipynb 565KB
05.14-Image-Features.ipynb 553KB
05.12-Gaussian-Mixtures.ipynb 535KB
03.11-Working-with-Time-Series.ipynb 508KB
05.09-Principal-Component-Analysis.ipynb 498KB
04.12-Three-Dimensional-Plotting.ipynb 495KB
04.04-Density-and-Contour-Plots.ipynb 486KB
05.08-Random-Forests.ipynb 474KB
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05.14-Image-Features.ipynb 382KB
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05.06-Linear-Regression.ipynb 379KB
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03.09-Pivot-Tables.ipynb 155KB
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04.08-Multiple-Subplots.ipynb 147KB
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04.00-Introduction-To-Matplotlib.ipynb 107KB
03.07-Merge-and-Join.ipynb 105KB
02.05-Computation-on-arrays-broadcasting.ipynb 100KB
05.13-Kernel-Density-Estimation.ipynb 99KB
04.00-Introduction-To-Matplotlib.ipynb 99KB
03.08-Aggregation-and-Grouping.ipynb 77KB
04.05-Histograms-and-Binnings.ipynb 75KB
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02.07-Fancy-Indexing.ipynb 62KB
02.07-Fancy-Indexing.ipynb 62KB
02.08-Sorting.ipynb 61KB
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