Source code for statsmodels.stats.descriptivestats

from statsmodels.compat.python import lrange, lmap, iterkeys, iteritems
import numpy as np
from scipy import stats
from statsmodels.iolib.table import SimpleTable
from statsmodels.tools.decorators import nottest

def _kurtosis(a):
    '''wrapper for scipy.stats.kurtosis that returns nan instead of raising Error

    missing options
    '''
    try:
        res = stats.kurtosis(a)
    except ValueError:
        res = np.nan
    return res

def _skew(a):
    '''wrapper for scipy.stats.skew that returns nan instead of raising Error

    missing options
    '''
    try:
        res = stats.skew(a)
    except ValueError:
        res = np.nan
    return res

_sign_test_doc = '''
    Signs test.

    Parameters
    ----------
    samp : array-like
        1d array. The sample for which you want to perform the signs
        test.
    mu0 : float
        See Notes for the definition of the sign test. mu0 is 0 by
        default, but it is common to set it to the median.

    Returns
    ---------
    M, p-value

    Notes
    -----
    The signs test returns

    M = (N(+) - N(-))/2

    where N(+) is the number of values above `mu0`, N(-) is the number of
    values below.  Values equal to `mu0` are discarded.

    The p-value for M is calculated using the binomial distrubution
    and can be intrepreted the same as for a t-test. The test-statistic
    is distributed Binom(min(N(+), N(-)), n_trials, .5) where n_trials
    equals N(+) + N(-).

    See Also
    ---------
    scipy.stats.wilcoxon
    '''

[docs]@nottest def sign_test(samp, mu0=0): samp = np.asarray(samp) pos = np.sum(samp > mu0) neg = np.sum(samp < mu0) M = (pos-neg)/2. p = stats.binom_test(min(pos,neg), pos+neg, .5) return M, p
sign_test.__doc__ = _sign_test_doc class Describe(object): ''' Calculates descriptive statistics for data. Defaults to a basic set of statistics, "all" can be specified, or a list can be given. Parameters ---------- dataset : array-like 2D dataset for descriptive statistics. ''' def __init__(self, dataset): self.dataset = dataset #better if this is initially a list to define order, or use an # ordered dict. First position is the function # Second position is the tuple/list of column names/numbers # third is are the results in order of the columns self.univariate = dict( obs = [len, None, None], mean = [np.mean, None, None], std = [np.std, None, None], min = [np.min, None, None], max = [np.max, None, None], ptp = [np.ptp, None, None], var = [np.var, None, None], mode_val = [self._mode_val, None, None], mode_bin = [self._mode_bin, None, None], median = [np.median, None, None], skew = [stats.skew, None, None], uss = [lambda x: np.sum(np.asarray(x)**2, axis=0), None, None], kurtosis = [stats.kurtosis, None, None], percentiles = [self._percentiles, None, None], #BUG: not single value #sign_test_M = [self.sign_test_m, None, None], #sign_test_P = [self.sign_test_p, None, None] ) #TODO: Basic stats for strings #self.strings = dict( #unique = [np.unique, None, None], #number_uniq = [len( #most = [ #least = [ #TODO: Multivariate #self.multivariate = dict( #corrcoef(x[, y, rowvar, bias]), #cov(m[, y, rowvar, bias]), #histogram2d(x, y[, bins, range, normed, weights]) #) self._arraytype = None self._columns_list = None def _percentiles(self,x): p = [stats.scoreatpercentile(x,per) for per in (1,5,10,25,50,75,90,95,99)] return p def _mode_val(self,x): return stats.mode(x)[0][0] def _mode_bin(self,x): return stats.mode(x)[1][0] def _array_typer(self): """if not a sctructured array""" if not(self.dataset.dtype.names): """homogeneous dtype array""" self._arraytype = 'homog' elif self.dataset.dtype.names: """structured or rec array""" self._arraytype = 'sctruct' else: assert self._arraytype == 'sctruct' or self._arraytype == 'homog' def _is_dtype_like(self, col): """ Check whether self.dataset.[col][0] behaves like a string, numbern unknown. `numpy.lib._iotools._is_string_like` """ def string_like(): #TODO: not sure what the result is if the first item is some type of # missing value try: self.dataset[col][0] + '' except (TypeError, ValueError): return False return True def number_like(): try: self.dataset[col][0] + 1.0 except (TypeError, ValueError): return False return True if number_like()==True and string_like()==False: return 'number' elif number_like()==False and string_like()==True: return 'string' else: assert (number_like()==True or string_like()==True), '\ Not sure of dtype'+str(self.dataset[col][0]) #@property def summary(self, stats='basic', columns='all', orientation='auto'): """ Return a summary of descriptive statistics. Parameters ----------- stats: list or str The desired statistics, Accepts 'basic' or 'all' or a list. 'basic' = ('obs', 'mean', 'std', 'min', 'max') 'all' = ('obs', 'mean', 'std', 'min', 'max', 'ptp', 'var', 'mode', 'meadian', 'skew', 'uss', 'kurtosis', 'percentiles') columns : list or str The columns/variables to report the statistics, default is 'all' If an object with named columns is given, you may specify the column names. For example """ #NOTE # standard array: Specifiy column numbers (NEED TO TEST) # percentiles currently broken # mode requires mode_val and mode_bin separately if self._arraytype == None: self._array_typer() if stats == 'basic': stats = ('obs', 'mean', 'std', 'min', 'max') elif stats == 'all': #stats = self.univariate.keys() #dict doesn't keep an order, use full list instead stats = ['obs', 'mean', 'std', 'min', 'max', 'ptp', 'var', 'mode_val', 'mode_bin', 'median', 'uss', 'skew', 'kurtosis', 'percentiles'] else: for astat in stats: pass #assert astat in self.univariate #hack around percentiles multiple output #bad naming import scipy.stats #BUG: the following has all per the same per=99 ##perdict = dict(('perc_%2d'%per, [lambda x: # scipy.stats.scoreatpercentile(x, per), None, None]) ## for per in (1,5,10,25,50,75,90,95,99)) def _fun(per): return lambda x: scipy.stats.scoreatpercentile(x, per) perdict = dict(('perc_%02d' % per, [_fun(per), None, None]) for per in (1,5,10,25,50,75,90,95,99)) if 'percentiles' in stats: self.univariate.update(perdict) idx = stats.index('percentiles') stats[idx:idx+1] = sorted(iterkeys(perdict)) #JP: this doesn't allow a change in sequence, sequence in stats is #ignored #this is just an if condition if any([aitem[1] for aitem in iteritems(self.univariate) if aitem[0] in stats]): if columns == 'all': self._columns_list = [] if self._arraytype == 'sctruct': self._columns_list = self.dataset.dtype.names #self._columns_list = [col for col in # self.dataset.dtype.names if #(self._is_dtype_like(col)=='number')] else: self._columns_list = lrange(self.dataset.shape[1]) else: self._columns_list = columns if self._arraytype == 'sctruct': for col in self._columns_list: assert (col in self.dataset.dtype.names) else: assert self._is_dtype_like(self.dataset) == 'number' columstypes = self.dataset.dtype #TODO: do we need to make sure they dtype is float64 ? for astat in stats: calc = self.univariate[astat] if self._arraytype == 'sctruct': calc[1] = self._columns_list calc[2] = [calc[0](self.dataset[col]) for col in self._columns_list if (self._is_dtype_like(col) == 'number')] #calc[2].append([len(np.unique(self.dataset[col])) for col # in self._columns_list if # self._is_dtype_like(col)=='string'] else: calc[1] = ['Col '+str(col) for col in self._columns_list] calc[2] = [calc[0](self.dataset[:,col]) for col in self._columns_list] return self.print_summary(stats, orientation=orientation) else: return self.print_summary(stats, orientation=orientation) def print_summary(self, stats, orientation='auto'): #TODO: need to specify a table formating for the numbers, using defualt title = 'Summary Statistics' header = stats stubs = self.univariate['obs'][1] data = [[self.univariate[astat][2][col] for astat in stats] for col in range(len(self.univariate['obs'][2]))] if (orientation == 'varcols') or \ (orientation == 'auto' and len(stubs) < len(header)): #swap rows and columns data = lmap(lambda *row: list(row), *data) header, stubs = stubs, header part_fmt = dict(data_fmts = ["%#8.4g"]*(len(header)-1)) table = SimpleTable(data, header, stubs, title=title, txt_fmt = part_fmt) return table def sign_test(self, samp, mu0=0): return sign_test(samp, mu0) sign_test.__doc__ = _sign_test_doc #TODO: There must be a better way but formating the stats of a fuction that # returns 2 values is a problem. #def sign_test_m(samp,mu0=0): #return self.sign_test(samp,mu0)[0] #def sign_test_p(samp,mu0=0): #return self.sign_test(samp,mu0)[1] if __name__ == "__main__": #unittest.main() data4 = np.array([[1,2,3,4,5,6], [6,5,4,3,2,1], [9,9,9,9,9,9]]) t1 = Describe(data4) #print(t1.summary(stats='all')) noperc = ['obs', 'mean', 'std', 'min', 'max', 'ptp', #'mode', #'var', 'median', 'skew', 'uss', 'kurtosis'] #TODO: mode var raise exception, #TODO: percentile writes list in cell (?), huge wide format print(t1.summary(stats=noperc)) print(t1.summary()) print(t1.summary( orientation='varcols')) print(t1.summary(stats=['mean', 'median', 'min', 'max'], orientation=('varcols'))) print(t1.summary(stats='all')) data1 = np.array([(1,2,'a','aa'), (2,3,'b','bb'), (2,4,'b','cc')], dtype = [('alpha',float), ('beta', int), ('gamma', '|S1'), ('delta', '|S2')]) data2 = np.array([(1,2), (2,3), (2,4)], dtype = [('alpha',float), ('beta', float)]) data3 = np.array([[1,2,4,4], [2,3,3,3], [2,4,4,3]], dtype=float) class TestSimpleTable(object): #from statsmodels.iolib.table import SimpleTable, default_txt_fmt def test_basic_1(self): print('test_basic_1') t1 = Describe(data1) print(t1.summary()) def test_basic_2(self): print('test_basic_2') t2 = Describe(data2) print(t2.summary()) def test_basic_3(self): print('test_basic_3') t1 = Describe(data3) print(t1.summary()) def test_basic_4(self): print('test_basic_4') t1 = Describe(data4) print(t1.summary()) def test_basic_1a(self): print('test_basic_1a') t1 = Describe(data1) print(t1.summary(stats='basic', columns=['alpha'])) def test_basic_1b(self): print('test_basic_1b') t1 = Describe(data1) print(t1.summary(stats='basic', columns='all')) def test_basic_2a(self): print('test_basic_2a') t2 = Describe(data2) print(t2.summary(stats='all')) def test_basic_3(aself): t1 = Describe(data3) print(t1.summary(stats='all')) def test_basic_4a(self): t1 = Describe(data4) print(t1.summary(stats='all'))