PythonDataScienceHandbook
https://github.com/jakevdp/PythonDataScienceHandbook.git
파일 탐색기
최종 버전 다운로드 (.zip)- BicycleWeather.csv
- births.csv
- california_cities.csv
- president_heights.csv
- Seattle2014.csv
- state-abbrevs.csv
- state-areas.csv
- state-population.csv
- 02.05-broadcasting.png
- 03.08-split-apply-combine.png
- 05.01-classification-1.png
- 05.01-classification-2.png
- 05.01-classification-3.png
- 05.01-clustering-1.png
- 05.01-clustering-2.png
- 05.01-dimesionality-1.png
- 05.01-dimesionality-2.png
- 05.01-regression-1.png
- 05.01-regression-2.png
- 05.01-regression-3.png
- 05.01-regression-4.png
- 05.02-samples-features.png
- 05.03-2-fold-CV.png
- 05.03-5-fold-CV.png
- 05.03-bias-variance-2.png
- 05.03-bias-variance.png
- 05.03-learning-curve.png
- 05.03-validation-curve.png
- 05.05-gaussian-NB.png
- 05.06-gaussian-basis.png
- 05.08-decision-tree-levels.png
- 05.08-decision-tree-overfitting.png
- 05.08-decision-tree.png
- 05.09-digits-pca-components.png
- 05.09-digits-pixel-components.png
- 05.09-PCA-rotation.png
- 05.10-LLE-vs-MDS.png
- 05.11-expectation-maximization.png
- 05.12-covariance-type.png
- array_vs_list.png
- cint_vs_pyint.png
- Data_Science_VD.png
- PDSH-cover-small.png
- PDSH-cover.png
- 00.00-Preface.ipynb
- 01.00-IPython-Beyond-Normal-Python.ipynb
- 01.01-Help-And-Documentation.ipynb
- 01.02-Shell-Keyboard-Shortcuts.ipynb
- 01.03-Magic-Commands.ipynb
- 01.04-Input-Output-History.ipynb
- 01.05-IPython-And-Shell-Commands.ipynb
- 01.06-Errors-and-Debugging.ipynb
- 01.07-Timing-and-Profiling.ipynb
- 01.08-More-IPython-Resources.ipynb
- 02.00-Introduction-to-NumPy.ipynb
- 02.01-Understanding-Data-Types.ipynb
- 02.02-The-Basics-Of-NumPy-Arrays.ipynb
- 02.03-Computation-on-arrays-ufuncs.ipynb
- 02.04-Computation-on-arrays-aggregates.ipynb
- 02.05-Computation-on-arrays-broadcasting.ipynb
- 02.06-Boolean-Arrays-and-Masks.ipynb
- 02.07-Fancy-Indexing.ipynb
- 02.08-Sorting.ipynb
- 02.09-Structured-Data-NumPy.ipynb
- 03.00-Introduction-to-Pandas.ipynb
- 03.01-Introducing-Pandas-Objects.ipynb
- 03.02-Data-Indexing-and-Selection.ipynb
- 03.03-Operations-in-Pandas.ipynb
- 03.04-Missing-Values.ipynb
- 03.05-Hierarchical-Indexing.ipynb
- 03.06-Concat-And-Append.ipynb
- 03.07-Merge-and-Join.ipynb
- 03.08-Aggregation-and-Grouping.ipynb
- 03.09-Pivot-Tables.ipynb
- 03.10-Working-With-Strings.ipynb
- 03.11-Working-with-Time-Series.ipynb
- 03.12-Performance-Eval-and-Query.ipynb
- 03.13-Further-Resources.ipynb
- 04.00-Introduction-To-Matplotlib.ipynb
- 04.01-Simple-Line-Plots.ipynb
- 04.02-Simple-Scatter-Plots.ipynb
- 04.03-Errorbars.ipynb
- 04.04-Density-and-Contour-Plots.ipynb
- 04.05-Histograms-and-Binnings.ipynb
- 04.06-Customizing-Legends.ipynb
- 04.07-Customizing-Colorbars.ipynb
- 04.08-Multiple-Subplots.ipynb
- 04.09-Text-and-Annotation.ipynb
- 04.10-Customizing-Ticks.ipynb
- 04.11-Settings-and-Stylesheets.ipynb
- 04.12-Three-Dimensional-Plotting.ipynb
- 04.13-Geographic-Data-With-Basemap.ipynb
- 04.14-Visualization-With-Seaborn.ipynb
- 04.15-Further-Resources.ipynb
- 05.00-Machine-Learning.ipynb
- 05.01-What-Is-Machine-Learning.ipynb
- 05.02-Introducing-Scikit-Learn.ipynb
- 05.03-Hyperparameters-and-Model-Validation.ipynb
- 05.04-Feature-Engineering.ipynb
- 05.05-Naive-Bayes.ipynb
- 05.06-Linear-Regression.ipynb
- 05.07-Support-Vector-Machines.ipynb
- 05.08-Random-Forests.ipynb
- 05.09-Principal-Component-Analysis.ipynb
- 05.10-Manifold-Learning.ipynb
- 05.11-K-Means.ipynb
- 05.12-Gaussian-Mixtures.ipynb
- 05.13-Kernel-Density-Estimation.ipynb
- 05.14-Image-Features.ipynb
- 05.15-Learning-More.ipynb
- 06.00-Figure-Code.ipynb
- helpers_05_08.py
- Index.ipynb
- add_book_info.py
- add_navigation.py
- fix_kernelspec.py
- generate_contents.py
- README.md
- favicon.ico
- icons.css
- icons.eot
- icons.svg
- icons.ttf
- icons.woff
- analytics.html
- disqus_thread.html
- about.html
- archives.html
- article.html
- base.html
- booksection.html
- index.html
- ipynb.css
- main.css
- main.less
- page.html
- pygments.css
- tag.html
- README.md
- .gitignore
- copy_notebooks.py
- fabfile.py
- Makefile
- pelicanconf.py
- publishconf.py
- README.md
- .gitignore
- .gitmodules
- LICENSE-CODE
- LICENSE-TEXT
- README.md
- requirements.txt
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/nkjadhav/PythonDataScienceHandbook
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd PythonDataScienceHandbook
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. Python
쉬움 추천사전 준비물
pip install -r requirements.txt
requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.
jupyter notebook
브라우저에서 노트북(.ipynb) 파일들을 열람하고 실행할 수 있는 Jupyter 화면을 켭니다.
에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.
// repository documentation
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