function_mapping_python.md

Function Mappings: From Python to DolphinDB

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:

• 1. Python built-in function
• 2. NumPy
• 3. Pandas
• 4. SciPy
• 5. Statsmodels
• 6. sklearn
• 7. TA-lib

1. Python built-in function

Python	DolphinDB
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	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

3. Pandas

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

4. SciPy

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

5. Statsmodels

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

6. sklearn

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

7. TA-lib

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 support@dolphindb.com or comment below to send us feedback or report any problems you find.