demo + utils venv

utils/venv/lib/python3.6/site-packages/pandas/tests/reductions/__init__.py

@@ -0,0 +1,4 @@
+"""
+Tests for reductions where we want to test for matching behavior across
+Array, Index, Series, and DataFrame methods.
+"""

utils/venv/lib/python3.6/site-packages/pandas/tests/reductions/test_stat_reductions.py

@@ -0,0 +1,202 @@
+# -*- 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()