# -*- coding: utf-8 -*- """ Tests for statistical reductions of 2nd moment or higher: var, skew, kurt, ... """ import numpy as np import pytest from pandas.compat import lrange import pandas.util._test_decorators as td import pandas as pd from pandas import DataFrame, Series, compat import pandas.util.testing as tm class TestSeriesStatReductions(object): # Note: the name TestSeriesStatReductions indicates these tests # were moved from a series-specific test file, _not_ that these tests are # intended long-term to be series-specific def _check_stat_op(self, name, alternate, string_series_, check_objects=False, check_allna=False): with pd.option_context('use_bottleneck', False): f = getattr(Series, name) # add some NaNs string_series_[5:15] = np.NaN # mean, idxmax, idxmin, min, and max are valid for dates if name not in ['max', 'min', 'mean']: ds = Series(pd.date_range('1/1/2001', periods=10)) with pytest.raises(TypeError): f(ds) # skipna or no assert pd.notna(f(string_series_)) assert pd.isna(f(string_series_, skipna=False)) # check the result is correct nona = string_series_.dropna() tm.assert_almost_equal(f(nona), alternate(nona.values)) tm.assert_almost_equal(f(string_series_), alternate(nona.values)) allna = string_series_ * np.nan if check_allna: assert np.isnan(f(allna)) # dtype=object with None, it works! s = Series([1, 2, 3, None, 5]) f(s) # GH#2888 items = [0] items.extend(lrange(2 ** 40, 2 ** 40 + 1000)) s = Series(items, dtype='int64') tm.assert_almost_equal(float(f(s)), float(alternate(s.values))) # check date range if check_objects: s = Series(pd.bdate_range('1/1/2000', periods=10)) res = f(s) exp = alternate(s) assert res == exp # check on string data if name not in ['sum', 'min', 'max']: with pytest.raises(TypeError): f(Series(list('abc'))) # Invalid axis. with pytest.raises(ValueError): f(string_series_, axis=1) # Unimplemented numeric_only parameter. if 'numeric_only' in compat.signature(f).args: with pytest.raises(NotImplementedError, match=name): f(string_series_, numeric_only=True) def test_sum(self): string_series = tm.makeStringSeries().rename('series') self._check_stat_op('sum', np.sum, string_series, check_allna=False) def test_mean(self): string_series = tm.makeStringSeries().rename('series') self._check_stat_op('mean', np.mean, string_series) def test_median(self): string_series = tm.makeStringSeries().rename('series') self._check_stat_op('median', np.median, string_series) # test with integers, test failure int_ts = Series(np.ones(10, dtype=int), index=lrange(10)) tm.assert_almost_equal(np.median(int_ts), int_ts.median()) def test_prod(self): string_series = tm.makeStringSeries().rename('series') self._check_stat_op('prod', np.prod, string_series) def test_min(self): string_series = tm.makeStringSeries().rename('series') self._check_stat_op('min', np.min, string_series, check_objects=True) def test_max(self): string_series = tm.makeStringSeries().rename('series') self._check_stat_op('max', np.max, string_series, check_objects=True) def test_var_std(self): string_series = tm.makeStringSeries().rename('series') datetime_series = tm.makeTimeSeries().rename('ts') alt = lambda x: np.std(x, ddof=1) self._check_stat_op('std', alt, string_series) alt = lambda x: np.var(x, ddof=1) self._check_stat_op('var', alt, string_series) result = datetime_series.std(ddof=4) expected = np.std(datetime_series.values, ddof=4) tm.assert_almost_equal(result, expected) result = datetime_series.var(ddof=4) expected = np.var(datetime_series.values, ddof=4) tm.assert_almost_equal(result, expected) # 1 - element series with ddof=1 s = datetime_series.iloc[[0]] result = s.var(ddof=1) assert pd.isna(result) result = s.std(ddof=1) assert pd.isna(result) def test_sem(self): string_series = tm.makeStringSeries().rename('series') datetime_series = tm.makeTimeSeries().rename('ts') alt = lambda x: np.std(x, ddof=1) / np.sqrt(len(x)) self._check_stat_op('sem', alt, string_series) result = datetime_series.sem(ddof=4) expected = np.std(datetime_series.values, ddof=4) / np.sqrt(len(datetime_series.values)) tm.assert_almost_equal(result, expected) # 1 - element series with ddof=1 s = datetime_series.iloc[[0]] result = s.sem(ddof=1) assert pd.isna(result) @td.skip_if_no_scipy def test_skew(self): from scipy.stats import skew string_series = tm.makeStringSeries().rename('series') alt = lambda x: skew(x, bias=False) self._check_stat_op('skew', alt, string_series) # test corner cases, skew() returns NaN unless there's at least 3 # values min_N = 3 for i in range(1, min_N + 1): s = Series(np.ones(i)) df = DataFrame(np.ones((i, i))) if i < min_N: assert np.isnan(s.skew()) assert np.isnan(df.skew()).all() else: assert 0 == s.skew() assert (df.skew() == 0).all() @td.skip_if_no_scipy def test_kurt(self): from scipy.stats import kurtosis string_series = tm.makeStringSeries().rename('series') alt = lambda x: kurtosis(x, bias=False) self._check_stat_op('kurt', alt, string_series) index = pd.MultiIndex( levels=[['bar'], ['one', 'two', 'three'], [0, 1]], codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]] ) s = Series(np.random.randn(6), index=index) tm.assert_almost_equal(s.kurt(), s.kurt(level=0)['bar']) # test corner cases, kurt() returns NaN unless there's at least 4 # values min_N = 4 for i in range(1, min_N + 1): s = Series(np.ones(i)) df = DataFrame(np.ones((i, i))) if i < min_N: assert np.isnan(s.kurt()) assert np.isnan(df.kurt()).all() else: assert 0 == s.kurt() assert (df.kurt() == 0).all()