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docker
docker
 
 
README.md
README.md
 
 
adf_test_statsmodels.ipynb
adf_test_statsmodels.ipynb
 
 
bayesian_modeling_pymc3.ipynb
bayesian_modeling_pymc3.ipynb
 
 
bayesian_modeling_pystan.ipynb
bayesian_modeling_pystan.ipynb
 
 
chi-squared_test.ipynb
chi-squared_test.ipynb
 
 
confidence_weighted_learning.ipynb
confidence_weighted_learning.ipynb
 
 
decision_tree.ipynb
decision_tree.ipynb
 
 
doc2vec_gensim.ipynb
doc2vec_gensim.ipynb
 
 
facter_analysis.ipynb
facter_analysis.ipynb
 
 
fisher_exact_test.ipynb
fisher_exact_test.ipynb
 
 
gaussian_mixture_model.ipynb
gaussian_mixture_model.ipynb
 
 
kernel_density_estimation_pandas.ipynb
kernel_density_estimation_pandas.ipynb
 
 
kernel_density_estimation_scipy.ipynb
kernel_density_estimation_scipy.ipynb
 
 
kolmogorov-smirnov_test.ipynb
kolmogorov-smirnov_test.ipynb
 
 
matplotlib_teq.ipynb
matplotlib_teq.ipynb
 
 
multilabel_classifier_mlknn_sklearn.ipynb
multilabel_classifier_mlknn_sklearn.ipynb
 
 
multiple_linear_regression_analysis.ipynb
multiple_linear_regression_analysis.ipynb
 
 
non-correlation_test.ipynb
non-correlation_test.ipynb
 
 
pandas_teqs.ipynb
pandas_teqs.ipynb
 
 
plotly.ipynb
plotly.ipynb
 
 
principal_component_analysis.ipynb
principal_component_analysis.ipynb
 
 
random_forest.ipynb
random_forest.ipynb
 
 
shap.ipynb
shap.ipynb
 
 
shapiro-wilk_test.ipynb
shapiro-wilk_test.ipynb
 
 
soft_confidence_weighted_learning_ver1.ipynb
soft_confidence_weighted_learning_ver1.ipynb
 
 
soft_confidence_weighted_learning_ver2.ipynb
soft_confidence_weighted_learning_ver2.ipynb
 
 
study_201804_plotly.ipynb
study_201804_plotly.ipynb
 
 
support_vector_machine.ipynb
support_vector_machine.ipynb
 
 
t-sne.ipynb
t-sne.ipynb
 
 
t-stats_confidence_interval.ipynb
t-stats_confidence_interval.ipynb
 
 
two_samples_f_test.ipynb
two_samples_f_test.ipynb
 
 
two_samples_t_test.ipynb
two_samples_t_test.ipynb
 
 
two_samples_welch_t_test.ipynb
two_samples_welch_t_test.ipynb
 
 
two_samples_wilcoxon_test.ipynb
two_samples_wilcoxon_test.ipynb
 
 

README.md

samples_python

Description

Pythonによるデータサイエンス関連のサンプルコード集.
基本的にjupyter notebookで実行プログラムと結果を保存する.
各ノートブックにはPythonのバージョンとライブラリバージョンを確認できるよう以下の実行セルを含めること.

Pythonバージョン

!python --version

ライブラリバージョン

!pip freeze

How to run

docker build -t samples_python docker/.
docker run -v `pwd`:/tmp/work -w=/tmp/work -p 8888:8888 --rm -it samples_python jupyter notebook --no-browser --ip=* --notebook-dir=/tmp/work --allow-root

if use Plotly, add jupyter notebook option --NotebookApp.iopub_data_rate_limit=1.0e10.