8th day of python challenges 111-117

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

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

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

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+"""
+Tests for statistical reductions of 2nd moment or higher: var, skew, kurt, ...
+"""
+import inspect
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import DataFrame, Series
+from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
+import pandas.util.testing as tm
+
+
+class TestDatetimeLikeStatReductions:
+    @pytest.mark.parametrize("box", [Series, pd.Index, DatetimeArray])
+    def test_dt64_mean(self, tz_naive_fixture, box):
+        tz = tz_naive_fixture
+
+        dti = pd.date_range("2001-01-01", periods=11, tz=tz)
+        # shuffle so that we are not just working with monotone-increasing
+        dti = dti.take([4, 1, 3, 10, 9, 7, 8, 5, 0, 2, 6])
+        dtarr = dti._data
+
+        obj = box(dtarr)
+        assert obj.mean() == pd.Timestamp("2001-01-06", tz=tz)
+        assert obj.mean(skipna=False) == pd.Timestamp("2001-01-06", tz=tz)
+
+        # dtarr[-2] will be the first date 2001-01-1
+        dtarr[-2] = pd.NaT
+
+        obj = box(dtarr)
+        assert obj.mean() == pd.Timestamp("2001-01-06 07:12:00", tz=tz)
+        assert obj.mean(skipna=False) is pd.NaT
+
+    @pytest.mark.parametrize("box", [Series, pd.Index, PeriodArray])
+    def test_period_mean(self, box):
+        # GH#24757
+        dti = pd.date_range("2001-01-01", periods=11)
+        # shuffle so that we are not just working with monotone-increasing
+        dti = dti.take([4, 1, 3, 10, 9, 7, 8, 5, 0, 2, 6])
+
+        # use hourly frequency to avoid rounding errors in expected results
+        #  TODO: flesh this out with different frequencies
+        parr = dti._data.to_period("H")
+        obj = box(parr)
+        with pytest.raises(TypeError, match="ambiguous"):
+            obj.mean()
+        with pytest.raises(TypeError, match="ambiguous"):
+            obj.mean(skipna=True)
+
+        # parr[-2] will be the first date 2001-01-1
+        parr[-2] = pd.NaT
+
+        with pytest.raises(TypeError, match="ambiguous"):
+            obj.mean()
+        with pytest.raises(TypeError, match="ambiguous"):
+            obj.mean(skipna=True)
+
+    @pytest.mark.parametrize("box", [Series, pd.Index, TimedeltaArray])
+    def test_td64_mean(self, box):
+        tdi = pd.TimedeltaIndex([0, 3, -2, -7, 1, 2, -1, 3, 5, -2, 4], unit="D")
+
+        tdarr = tdi._data
+        obj = box(tdarr)
+
+        result = obj.mean()
+        expected = np.array(tdarr).mean()
+        assert result == expected
+
+        tdarr[0] = pd.NaT
+        assert obj.mean(skipna=False) is pd.NaT
+
+        result2 = obj.mean(skipna=True)
+        assert result2 == tdi[1:].mean()
+
+        # exact equality fails by 1 nanosecond
+        assert result2.round("us") == (result * 11.0 / 10).round("us")
+
+
+class TestSeriesStatReductions:
+    # 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(range(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 inspect.getfullargspec(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=range(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()