# Python 到 DolphinDB 的函数映射 本篇介绍 Python 部分模块到 DolphinDB 函数库的不完全映射。 下文涉及的 python 模块如下： - python built-in function - numpy - pandas - scipy - statsmodels - sklearn - TA-lib - [Python 到 DolphinDB 的函数映射](#python-到-dolphindb-的函数映射) - [1. python built-in function](#1--python-built-in-function) - [2. numpy](#2--numpy) - [3. pandas](#3--pandas) - [4. scipy](#4--scipy) - [5. statsmodels](#5--statsmodels) - [6. sklearn](#6--sklearn) - [7. TA-lib](#7--ta-lib) ## 1. python built-in function | Python 函数 | DolphinDB 函数 | | ----------- | -------------- | | all | all | | any | any | | in | in | | == | eq | | equals | eqObj | | abs | abs | | len | strlen / size | | pow | pow | | print | print | | set | set | | dict | dict | | str | string | | int | int | | bool | bool | | round | round | | slice | slice | | type | type / typestr | | zip | loop(pair, x, y) | | join | concat | | format | strReplace | | sort | isort | | rjust /zfill | lpad /rpad | | lead / lag | move | | itertools.product | cross + join | ## 2. numpy | numpy 函数 | DolphinDB 函数 | | -------------------------------------------------------- | --------------------------- | | numpy.median | med | | numpy.var(ddof=1) | var | | numpy.var | varp | | numpy.cov | covarMatrix | | numpy.cov(fweights) | wcovar | | numpy.std(ddof=1) | std | | numpy.std | stdp | | numpy.percentile / pandas.Series.percentile | percentile | | numpy.quantile / pandas.Series.quantile | quantile | | numpy.quantile | quantileSeries | | numpy.corrcoef | corrMatrix | | numpy.random.beta | randBeta | | numpy.random.binomial | randBinomial | | numpy.random.chisquare | randChiSquare | | numpy.random.exponential | randExp | | numpy.random.f | randF | | numpy.random.gamma | randGamma | | numpy.random.logistic | randLogistic | | numpy.random.normal | randNormal | | numpy.random.multivariate_normal | randMultivariateNormal | | numpy.random.poisson | randPoisson | | numpy.random.standard_t | randStudent | | numpy.random.rand | rand | | numpy.argsort | isort/isort! | | numpy.averge(weight) | wavg | | numpy.random.uniform | randUniform | | numpy.random.weibull | randWeibull | | numpy.max | max | | numpy.min | min | | numpy.mean | mean/avg | | numpy.sum | sum | | nump.random.normal | norm | | nump.clip | winsorize | ## 3. pandas | pandas 函数 | DolphinDB 函数 | | ------------------------------------------------------------ | ----------------------- | | df\[column] | at | | pandas.Series.loc / pandas.DataFrame.loc | loc | | pandas.Series.iat / pandas.DataFrame.iat | cell | | pandas.Series.iloc / pandas.DataFrame.iloc | cells | | pandas.Series.align / pandas.DataFrame.align | align | | pandas.unique / pandas.DataFrame.unique / pandas.Series.unique | distinct | | pandas.concat | concatMatrix | | pandas.DataFrame.add / pandas.Series.add | withNullFill + add | | pandas.DataFrame.sub / pandas.Series.sub | withNullFill + sub | | pandas.DataFrame.mul / pandas.Series.mul | withNullFill + mul | | pandas.DataFrame.div / pandas.Series.div | withNullFill + div / ratio | | pandas.DataFrame.pivot | pivot / panel | | pandas.DataFrame.melt | unpivot | | pandas.DataFrame.merge / pandas.DataFrame.join | merge | | pandas.DataFrame.ewm.var | ewmVar | | pandas.Series.cov | covar | | pandas.DataFrame.ewm.cov | ewmCov | | pandas.ewmstd | ewmStd | | pandas.DataFrame.corr / pandas.Series.corr | corr | | pandas.DataFrame.std / pandas.Series.std | std | | pandas.DataFrame.median / pandas.Series.median | med | | pandas.DataFrame.ewm.corr | ewmCorr | | pandas.DataFrame.max / pandas.Series.max | max | | pandas.DataFrame.min / pandas.Series.min | min | | pandas.DataFrame.mean / pandas.Series.mean | mean/avg | | pandas.DataFrame.ewm.mean | ewmMean | | pandas.DataFrame.sum / pandas.Series.sum | sum | | pandas.DataFrame.prod / pandas.Series.prod | prod | | pandas.DataFrame.nunique / pandas.Series.nunique | nunique | | pandas.DataFrame.hist / pandas.Series.hist | plotHist | | pandas.DataFrame.sem / pandas.Series.sem | sem | | pandas.DataFrame.mad / pandas.Series.mad | mad (useMedian=false) | | pandas.DataFrame.kurt(kurtosis) / pandas.Series.kurt(kurtosis) | kurtosis | | pandas.DataFrame.skew / pandas.Series.kurt(skew) | skew | | pandas.DataFrame.count / pandas.Series.count | count | | pandas.DataFrame.idxmax / pandas.Series.idxmax | imax | | pandas.DataFrame.idxmin / pandas.Series.idxmin | imin | | pandas.DataFrame.cummax / pandas.Series.cummax | cummax | | pandas.DataFrame.cummin / pandas.Series.cummin | cummin | | pandas.DataFrame.cumsum / pandas.Series.cumsum | cumsum | | pandas.DataFrame.cumprod / pandas.Series.cumprod | cumprod | | pandas.DataFrame.nlargest(nsmallest) / pandas.Series.nlargest(nsmallest) | top + order by / aggrTopN | | pandas.DataFrame.diff / pandas.Series.diff | eachPost, deltas | | pandas.DataFrame.quantile / pandas.Series.quantile | quantile | | pandas.DataFrame.transpose | transpose | | pandas.Series.resample / pandas.DataFrame.resample | resample | | pandas.Series.copy / pandas.DataFrame.copy | copy | | pandas.Series.describe / pandas.DataFrame.describe 类似 | stat | | pandas.DataFrame.isnull/pandas.DataFrame.isna | isNull | | pandas.DataFrame.notnull/pandas.DataFrame.notna | isValid | | pandas.Series.between | between | | pandas.Series.is_monotonic_decreasing | isMonotonicIncreasing | | pandas.Series.is_monotonic_increasing | isMonotonicDecreasing | | pandas.DataFrame.mask / pandas.Series.mask | mask | | pandas.DataFrame.bfill / pandas.Series.bfill | bfill/bfill! | | pandas.DataFrame.ffill / pandas.Series.ffill | ffill/ffill! | | pandas.DataFrame.interpolate / pandas.Series.interpolate | interpolate | | pandas.DataFrame.interpolate(method='linear') / pandas.Series.interpolate(method='linear') | lfill/lfill! | | pandas.DataFrame.fillna / pandas.Series.fillna | nullFill/nullFill! | | pandas.DataFrame.sort_values / pandas.Series.sort_values | sort/sort! | | pandas.DataFrame.head / pandas.Series.head | head | | pandas.DataFrame.tail / pandas.Series.tail | tail | | pandas.DataFrame.drop / pandas.Series.drop | dropColumns! | | pandas.DataFrame.dropna / pandas.Series.dropna | dropna | | pandas.DataFrame.rename | rename! | | pandas.DataFrame.append / pandas.Series.append | append! | | pandas.DataFrame.keys / pandas.Series.keys | rowNames / columnNames | | pandas.DataFrame.astype / pandas.Series.astype | cast | | pandas.DataFrame.isin / pandas.Series.isin | in | | pandas.Series.str.isspace | isSpace | | pandas.Series.str.isalnum | isAlNum | | pandas.Series.str.isalpha | isAlpha | | pandas.Series.str.isnumeric | isNumeric | | pandas.Series.str.isdecimal | isDecimal | | pandas.Series.str.isdigit | isDigit | | pandas.Series.str.islower | isLower | | pandas.Series.str.isupper | isUpper | | pandas.Series.str.istitle | isTitle | | pandas.Series.str.startswith | startsWith | | pandas.Series.str.endswith | endsWith | | pandas.Series.str.find | regexFind | | pandas.Series.str.replace | strReplace | | pandas.Series.duplicated /pandas.DataFrame.duplicated | isDuplicated | | pandas.Series.rank / pandas.DataFrame.rank | rank | | pandas.Series.rank(method='dense') / pandas.DataFrame.rank(method='dense') | denseRank | | pandas.read_csv | loadText / loadTextEx | | pandas.to_csv | saveText | | pandas.read_json | fromJson | | pandas.DataFrame.to_json / pandas.Series.to_json | toJson | | pandas.DataFrame.groupby.aggFunc | regroup, group by | | pandas.to_datetime | temporalParse | | pandas.DataFrame.rolling / pandas.Series.rolling | moving | | pandas.rolling_mean | mavg | | pandas.rolling_std | mstd | | pandas.rolling_median | mmed | | pandas.DataFrame.shift / pandas.Series.shift | move / tmove / prev / next | ## 4. scipy | scipy 函数 | DolphinDB 函数 | | -------------------------------------------------------- | --------------------------- | | scipy.stats.percentileofscore | percentileRank | | scipy.stats.spearmanr(X, Y)\[0] | spearmanr(X, Y) | | scipy.spatial.distance.euclidean | euclidean | | scipy.stats.beta.cdf(X, a, b) | cdfBeta(a, b, X) | | scipy.stats.binom.cdf(X, trials, p) | cdfBinomial(trials, p, X) | | scipy.stats.chi2.cdf(x, df) | cdfChiSquare(df, X) | | scipy.stats.expon.cdf(x, scale=mean) | cdfExp(mean, X) | | scipy.stats.f.cdf(X, dfn, dfd) | cdfF(dfn, dfd, X) | | scipy.stats.gamma.cdf(X, shape, scale=scale) | cdfGamma(shape, scale, X) | | scipy.stats.logistic.cdf(X, loc=mean,scale=scale) | cdfLogistic(mean, scale, X) | | scipy.stats.norm.cdf(X, loc=mean, scale=stdev) | cdfNormal(mean,stdev,X) | | scipy.stats.poisson.cdf(X, mu=mean) | cdfPoisson(mean, X) | | scipy.stats.t.cdf(X, df) | cdfStudent(df, X) | | scipy.stats.uniform.cdf(X, loc=lower, scale=upper-lower) | cdfUniform(lower, upper, X) | | scipy.stats.weibull_min.cdf(X, alpha, scale=beta) | cdfWeibull(alpha, beta, X) | | scipy.stats.zipfian.cdf(X, exponent, num) | cdfZipf(num, exponent, X) | | scipy.stats.beta.ppf(X, a, b) | invBeta | | scipy.stats.binom.ppf(X, trials, p) | invBinomial | | scipy.stats.chi2.ppf(x, df) | invChiSquare | | scipy.stats.expon.ppf(x, scale=mean) | invExp | | scipy.stats.f.ppf(X, dfn, dfd) | invF | | scipy.stats.gamma.ppf(X, shape, scale=scale) | invGamma | | scipy.stats.logistic.ppf(X, loc=mean,scale=scale) | invLogistic | | scipy.stats.norm.ppf(X, loc=mean, scale=stdev) | invNormal | | scipy.stats.poisson.ppf(X, mu=mean) | invPoisson | | scipy.stats.t.ppf(X, df) | invStudent | | scipy.stats.uniform.ppf(X, loc=lower, scale=upper-lower) | invUniform | | scipy.stats.weibull_min.ppf(X, alpha, scale=beta) | invWeibull | | scipy.stats.chisquare | chiSquareTest | | scipy.stats.f_oneway | fTest | | scipy.stats.ttest_ind | tTest | | scipy.stats.ks_2samp | ksTest | | scipy.stats.shapiro | shapiroTest | | scipy.stats.mannwhitneyu | mannWhitneyUTest | | scipy.stats.mstats.winsorize | winsorize | | scipy. stats.kurtosis | kurtosis | | scipy.stats.skew | skew | | scipy.stats.sem | sem | | scipy.stats.zscore(ddof=1) | zscore | ## 5. statsmodels | statsmodels 函数 | DolphinDB 函数 | | -------------------------------------- | -------------- | | statsmodels.api.tsa.acf | acf | | statsmodels.tsa.seasonal.STL | stl | | statsmodels.stats.weightstats.ztest | zTest | | statsmodels.multivariate.manova.MANOVA | manova | | statsmodels.api.stats.anova_lm | anova | | statsmodels.regression.linear_model.OLS | olsolsEx | | statsmodels.regression.linear_model.WLS | wls | ## 6. sklearn | sklearn 函数 | DolphinDB 函数 | | ------------------------------------------------------- | ---------------------- | | sklearn.linear_model.LinearRegression().fit(Y, X).coef_ | beta(X, Y) | | sklearn.metrics.mutual_info_score | mutualInfo | | sklearn.ensemble.AdaBoostClassifier | adaBoostClassifier | | sklearn.ensemble.AdaBoostRegressor | adaBoostRegressor | | sklearn.ensemble.RandomForestClassifier | randomForestClassifier | | sklearn.ensemble.RandomForestRegressor | randomForestRegressor | | sklearn.naive_bayes.GaussianNB | gaussianNB | | sklearn.naive_bayes.MultinomialNB | multinomialNB | | sklearn.linear_model.LogisticRegression | logisticRegression | | sklearn.mixture.GaussianMixture | gmm | | sklearn.cluster.k_means | kmeans | | sklearn.neighbors.KNeighborsClassifier | knn | | sklearn.linear_model.ElasticNet | elasticNet | | sklearn.linear_model.Lasso | lasso | | sklearn.linear_model.Ridge | ridge | | sklearn.decomposition.PCA | pca | ## 7. TA-lib | TA-lib 函数 | DolphinDB 函数 | | ------------------------------------------------- | --------------- | | talib.MA | ma | | talib.EMA | ema | | talib.WMA | wma | | talib.SMA | sma | | talib.TRIMA | trima | | talib.TEMA | tema | | talib.DEMA | dema | | talib.KAMA | kama | | talib.T3 | t3 | | talib.LINEARREG_SLOPE / talib.LINEARREG_INTERCEPT | linearTimeTrend | | talib.TRANGE | trueRange | 以上仅列出 DolphinDB 的内置的 TA-lib 函数。有关 TA-lib 指标函数的更多详细信息，参考 [DolphinDB 的 ta-lib 模块](../modules/ta/ta.md)。