function_mapping_py.md

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

  • 1. python built-in function
  • 2. numpy
  • 3. pandas
  • 4. scipy
  • 5. statsmodels
  • 6. sklearn
  • 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。