This page shows the corresponding DolphinDB functions for selected Python functions.
The DolphinDB functions listed below are supported in version 2.00 (refer to DolphinDB manual for more information).
The following python libraries are covered:
| Python | DolphinDB |
|---|---|
| all | all |
| any | any |
| in | in |
| == | eq |
| equals | eqObj |
| abs | abs |
| len | strlen / size |
| pow | pow |
| 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 |
| NumPy | DolphinDB |
|---|---|
| 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 |
| Pandas | DolphinDB |
|---|---|
| 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 |
| SciPy | DolphinDB |
|---|---|
| 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 |
| Statsmodels | DolphinDB |
|---|---|
| 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 |
| sklearn | DolphinDB |
|---|---|
| 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 |
| TA-lib | DolphinDB |
|---|---|
| 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 |
The functions listed above are DolphinDB built-in functions. More TA-lib functions are provided in DolphinDB ta module. Refer to DolphinDB tutorial: Technical Analysis Indicator Library for more information.
Note: Contact us via [email protected] or comment below to send us feedback or report any problems you find.