PyIDD

Identify the Distribution of your Data
This package helps you for identifing the distribution of your data. For example if you want to know whether your data follow Normal distribution, you can use this package!

Installation

pip install pyidd

Usage

import pyidd
import statsmodels.api as sm

# Load sample dataset (data is an one dimensional array)
data = sm.datasets.elnino.load_pandas().data.set_index('YEAR').values.ravel()

# verbose=0 means silent while fitting and verbose=1 means say anything you do while fitting
p = pyidd.PyIDD(verbose=1)
p.fit(data)

# After fitting, you can get distribution sorted by Sum of Squered Error that fit your data, so the first distribution is the distribution that is closer to your data
p.get_distributions()

# You can also plot distributions and your data to see this visualy. top=10 means that you want to plot top 10 distributions that match your data
p.plot(top=10)

Distributions

We use almost all scipy.stats continiuse distributions (Notics: The distributions that can not fit to your data get ignored). These distributions are listed below:

• alpha
• anglit
• arcsine
• argus
• beta
• betaprime
• bradford
• burr
• burr12
• cauchy
• chi
• chi2
• cosine
• crystalball
• dgamma
• dweibull
• erlang
• expon
• exponnorm
• exponweib
• exponpow
• f
• fatiguelife
• fisk
• foldcauchy
• foldnorm
• genlogistic
• gennorm
• genpareto
• genexpon
• genextreme
• gausshyper
• gamma
• gengamma
• genhalflogistic
• geninvgauss
• gompertz
• gumbel_r
• gumbel_l
• halfcauchy
• halflogistic
• halfnorm
• halfgennorm
• hypsecant
• invgamma
• invgauss
• invweibull
• johnsonsb
• johnsonsu
• kappa4
• kappa3
• ksone
• kstwo
• kstwobign
• laplace
• levy
• levy_l
• logistic
• loggamma
• loglaplace
• lognorm
• loguniform
• lomax
• maxwell
• mielke
• moyal
• nakagami
• ncx2
• ncf
• nct
• norm
• norminvgauss
• pareto
• pearson3
• powerlaw
• powerlognorm
• powernorm
• rdist
• rayleigh
• rice
• recipinvgauss
• semicircular
• skewnorm
• t
• trapezoid
• triang
• truncexpon
• truncnorm
• tukeylambda
• uniform
• vonmises
• vonmises_line
• wald
• weibull_min
• weibull_max
• wrapcauchy