If you are already familiar with the module/package loading methods of Python and R, the following table is relatively easy to find.
Python is referenced in the following table as a module. Some modules are not native modules. Please use pip install * to install;
For the same reason, in order to facilitate indexing, R also refers to:: indicates the function and the name of the package where the function is located. If it does not contain :: indicates that it is in the default package of R, such as::, please use install.packages("*") to finish the installation.
Connector & IO
Mechine Learning
| Category |
Subcategory | Python |
| LDA | sklearn.discriminant_analysis.LinearDiscriminantAnalysis | |
| QDA | sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis | |
| SVM (Support Vector Machine) | Support Vector Classifier (SVC) | sklearn.svm.SVC |
| SVM (Support Vector Machine) | Non-support vector classifier (nonSVC) | sklearn.svm.NuSVC |
| SVM (Support Vector Machine) | Linear Support Vector Classifier (Lenear SVC) | sklearn.svm.LinearSVC |
| Based on proximity | K-proximity classifier | sklearn.neighbors.KNeighborsClassifier |
| Based on proximity | Radius proximity classifier | sklearn.neighbors.RadiusNeighborsClassifier |
| Based on proximity | Nearest Centroid Classifier | sklearn.neighbors.NearestCentroid |
| Bayes | Naive Bayes | sklearn.naive_bayes.GaussianNB |
| Bayes | Multinomial Naive Bayes | sklearn.naive_bayes.MultinomialNB |
| Bayes | Bernoulli Naive Bayes | sklearn.naive_bayes.BernoulliNB |
| DecisionTree | DecisionTree Classifier | sklearn.tree.DecisionTreeClassifier |
| DecisionTree | DecisionTree Regressor | sklearn.tree.DecisionTreeRegressor |
| Assemble Method | Bagging Random Forest Classifier | sklearn.ensemble.RandomForestClassifier |
| Assemble Method | Bagging Random Forest Regressor | sklearn.ensemble.RandomForestRegressor |
| Assemble Method | Boosting Gradient Boosting | xgboost Module |
| Assemble Method | Boosting AdaBoost | sklearn.ensemble.AdaBoostClassifier |
| Cluster | kmeans | scipy.cluster.kmeans.kmeans |
| Cluster | Hierarchical Cluster | scipy.cluster.hierarchy.fcluster |
| Cluster | DBSCAN | sklearn.cluster.DBSCAN |
| Cluster | Birch | sklearn.cluster.Birch |
| Cluster | K-Medoids Cluster | pyclust.KMedoids(Unknown reliability) |
| Association Rule | Apriori Algorithm | apriori(Unknown reliability, not support py3), |
| Association Rule | FP-Growth Algorithm | fp-growth(Unknown reliability, not support py3), |
| Neural Network | Neural Network | neurolab.net, keras.* |
| Neural Network | Deep Learning | keras.* |
Database
| Category | Python |
|---|---|
| MySQL | mysql-connector-python(Official) |
| Oracle | cx_Oracle |
| Redis | redis |
| MongoDB | pymongo |
| neo4j | py2neo |
| Cassandra | cassandra-driver |
| ODBC | pyodbc |
| JDBC | Unknown[Jython Only] |
IO
| Category | Python |
|---|---|
| excel | xlsxWriter, pandas.(from/to)_excel, openpyxl |
| csv | csv.writer |
| json | json |
| 图片 | PIL |
Statistics
| Category | Python |
|---|---|
| 描述性统计汇总 | scipy.stats.descirbe |
| 均值 | scipy.stats.gmean(几何平均数), scipy.stats.hmean(调和平均数), numpy.mean, numpy.nanmean, pandas.Series.mean |
| 中位数 | numpy.median, numpy.nanmediam, pandas.Series.median |
| 众数 | scipy.stats.mode, pandas.Series.mode |
| 分位数 | numpy.percentile, numpy.nanpercentile, pandas.Series.quantile |
| 经验累积函数(ECDF) | statsmodels.tools.ECDF |
| 标准差 | scipy.stats.std, scipy.stats.nanstd, numpy.std, pandas.Series.std |
| 方差 | numpy.var, pandas.Series.var |
| 变异系数 | scipy.stats.variation |
| 协方差 | numpy.cov, pandas.Series.cov |
| (Pearson)相关系数 | scipy.stats.pearsonr, numpy.corrcoef, pandas.Series.corr |
| 峰度 | scipy.stats.kurtosis, pandas.Series.kurt |
| 偏度 | scipy.stats.skew, pandas.Series.skew |
| 直方图 | numpy.histogram, numpy.histogram2d, numpy.histogramdd |
Regression (including statistics and machine learning)
| 类别 | Python |
|---|---|
| 普通最小二乘法回归(ols) | statsmodels.ols, sklearn.linear_model.LinearRegression |
| 广义线性回归(gls) | statsmodels.gls |
| 分位数回归(Quantile Regress) | statsmodels.QuantReg |
| 岭回归 | sklearn.linear_model.Ridge |
| LASSO | sklearn.linear_model.Lasso |
| 最小角回归 | sklearn.linear_modle.LassoLars |
| 稳健回归 | statsmodels.RLM |
Hypothetical Test
| 类别 | Python |
|---|---|
| t检验 | statsmodels.stats.ttest_ind, statsmodels.stats.ttost_ind, statsmodels.stats.ttost.paired; scipy.stats.ttest_1samp, scipy.stats.ttest_ind, scipy.stats.ttest_ind_from_stats, scipy.stats.ttest_rel |
| ks检验(检验分布) | scipy.stats.kstest, scipy.stats.kstest_2samp |
| wilcoxon(非参检验，差异检验) | scipy.stats.wilcoxon, scipy.stats.mannwhitneyu |
| Shapiro-Wilk正态性检验 | scipy.stats.shapiro |
| Pearson相关系数检验 | scipy.stats.pearsonr |
Time series
| Category | Python |
|---|---|
| AR | statsmodels.ar_model.AR |
| ARIMA | statsmodels.arima_model.arima |
| VAR | statsmodels.var_model.var |