FM / methods

/usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods/

/usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
test_align.py	5359	0644	edit dl rm
test_append.py	9600	0644	edit dl rm
test_argsort.py	2249	0644	edit dl rm
test_asfreq.py	3616	0644	edit dl rm
test_asof.py	5515	0644	edit dl rm
test_astype.py	2628	0644	edit dl rm
test_at_time.py	2629	0644	edit dl rm
test_autocorr.py	999	0644	edit dl rm
test_between.py	1197	0644	edit dl rm
test_between_time.py	5086	0644	edit dl rm
test_clip.py	3384	0644	edit dl rm
test_combine.py	627	0644	edit dl rm
test_combine_first.py	3411	0644	edit dl rm
test_compare.py	3734	0644	edit dl rm
test_convert_dtypes.py	9914	0644	edit dl rm
test_count.py	1123	0644	edit dl rm
test_cov_corr.py	5215	0644	edit dl rm
test_describe.py	4640	0644	edit dl rm
test_diff.py	2331	0644	edit dl rm
test_drop.py	2986	0644	edit dl rm
test_droplevel.py	629	0644	edit dl rm
test_drop_duplicates.py	6850	0644	edit dl rm
test_duplicated.py	996	0644	edit dl rm
test_equals.py	1295	0644	edit dl rm
test_explode.py	3538	0644	edit dl rm
test_fillna.py	6513	0644	edit dl rm
test_first_and_last.py	2042	0644	edit dl rm
test_head_tail.py	343	0644	edit dl rm
test_interpolate.py	27757	0644	edit dl rm
test_isin.py	3445	0644	edit dl rm
test_nlargest.py	7121	0644	edit dl rm
test_pct_change.py	2968	0644	edit dl rm
test_quantile.py	6810	0644	edit dl rm
test_rank.py	20287	0644	edit dl rm
test_reindex_like.py	1245	0644	edit dl rm
test_rename.py	3378	0644	edit dl rm
test_rename_axis.py	1503	0644	edit dl rm
test_replace.py	15559	0644	edit dl rm
test_reset_index.py	4636	0644	edit dl rm
test_round.py	1970	0644	edit dl rm
test_searchsorted.py	2121	0644	edit dl rm
test_shift.py	12595	0644	edit dl rm
test_sort_index.py	12277	0644	edit dl rm
test_sort_values.py	7981	0644	edit dl rm
test_to_dict.py	742	0644	edit dl rm
test_to_period.py	1755	0644	edit dl rm
test_to_timestamp.py	2616	0644	edit dl rm
test_truncate.py	5557	0644	edit dl rm
test_tz_convert.py	1008	0644	edit dl rm
test_tz_localize.py	3108	0644	edit dl rm
test_unstack.py	4208	0644	edit dl rm
test_update.py	4213	0644	edit dl rm
test_value_counts.py	8136	0644	edit dl rm
__init__.py	225	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/pandas/tests/series/methods/test_describe.py (4640B)

import numpy as np

from pandas import Period, Series, Timedelta, Timestamp, date_range
import pandas._testing as tm

class TestSeriesDescribe:
    def test_describe(self):
        s = Series([0, 1, 2, 3, 4], name="int_data")
        result = s.describe()
        expected = Series(
            [5, 2, s.std(), 0, 1, 2, 3, 4],
            name="int_data",
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        )
        tm.assert_series_equal(result, expected)

s = Series([True, True, False, False, False], name="bool_data")
        result = s.describe()
        expected = Series(
            [5, 2, False, 3], name="bool_data", index=["count", "unique", "top", "freq"]
        )
        tm.assert_series_equal(result, expected)

s = Series(["a", "a", "b", "c", "d"], name="str_data")
        result = s.describe()
        expected = Series(
            [5, 4, "a", 2], name="str_data", index=["count", "unique", "top", "freq"]
        )
        tm.assert_series_equal(result, expected)

s = Series(
            [
                Timedelta("1 days"),
                Timedelta("2 days"),
                Timedelta("3 days"),
                Timedelta("4 days"),
                Timedelta("5 days"),
            ],
            name="timedelta_data",
        )
        result = s.describe()
        expected = Series(
            [5, s[2], s.std(), s[0], s[1], s[2], s[3], s[4]],
            name="timedelta_data",
            index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
        )
        tm.assert_series_equal(result, expected)

s = Series(
            [Period("2020-01", "M"), Period("2020-01", "M"), Period("2019-12", "M")],
            name="period_data",
        )
        result = s.describe()
        expected = Series(
            [3, 2, s[0], 2],
            name="period_data",
            index=["count", "unique", "top", "freq"],
        )
        tm.assert_series_equal(result, expected)

def test_describe_empty_object(self):
        # https://github.com/pandas-dev/pandas/issues/27183
        s = Series([None, None], dtype=object)
        result = s.describe()
        expected = Series(
            [0, 0, np.nan, np.nan],
            dtype=object,
            index=["count", "unique", "top", "freq"],
        )
        tm.assert_series_equal(result, expected)

result = s[:0].describe()
        tm.assert_series_equal(result, expected)
        # ensure NaN, not None
        assert np.isnan(result.iloc[2])
        assert np.isnan(result.iloc[3])

def test_describe_with_tz(self, tz_naive_fixture):
        # GH 21332
        tz = tz_naive_fixture
        name = str(tz_naive_fixture)
        start = Timestamp(2018, 1, 1)
        end = Timestamp(2018, 1, 5)
        s = Series(date_range(start, end, tz=tz), name=name)
        result = s.describe(datetime_is_numeric=True)
        expected = Series(
            [
                5,
                Timestamp(2018, 1, 3).tz_localize(tz),
                start.tz_localize(tz),
                s[1],
                s[2],
                s[3],
                end.tz_localize(tz),
            ],
            name=name,
            index=["count", "mean", "min", "25%", "50%", "75%", "max"],
        )
        tm.assert_series_equal(result, expected)

def test_describe_with_tz_warns(self):
        name = tz = "CET"
        start = Timestamp(2018, 1, 1)
        end = Timestamp(2018, 1, 5)
        s = Series(date_range(start, end, tz=tz), name=name)

with tm.assert_produces_warning(FutureWarning):
            result = s.describe()

expected = Series(
            [
                5,
                5,
                s.value_counts().index[0],
                1,
                start.tz_localize(tz),
                end.tz_localize(tz),
            ],
            name=name,
            index=["count", "unique", "top", "freq", "first", "last"],
        )
        tm.assert_series_equal(result, expected)

def test_datetime_is_numeric_includes_datetime(self):
        s = Series(date_range("2012", periods=3))
        result = s.describe(datetime_is_numeric=True)
        expected = Series(
            [
                3,
                Timestamp("2012-01-02"),
                Timestamp("2012-01-01"),
                Timestamp("2012-01-01T12:00:00"),
                Timestamp("2012-01-02"),
                Timestamp("2012-01-02T12:00:00"),
                Timestamp("2012-01-03"),
            ],
            index=["count", "mean", "min", "25%", "50%", "75%", "max"],
        )
        tm.assert_series_equal(result, expected)

import numpy as np from pandas import Period, Series, Timedelta, Timestamp, date_range import pandas._testing as tm class TestSeriesDescribe: def test_describe(self): s = Series([0, 1, 2, 3, 4], name="int_data") result = s.describe() expected = Series( [5, 2, s.std(), 0, 1, 2, 3, 4], name="int_data", index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], ) tm.assert_series_equal(result, expected) s = Series([True, True, False, False, False], name="bool_data") result = s.describe() expected = Series( [5, 2, False, 3], name="bool_data", index=["count", "unique", "top", "freq"] ) tm.assert_series_equal(result, expected) s = Series(["a", "a", "b", "c", "d"], name="str_data") result = s.describe() expected = Series( [5, 4, "a", 2], name="str_data", index=["count", "unique", "top", "freq"] ) tm.assert_series_equal(result, expected) s = Series( [ Timedelta("1 days"), Timedelta("2 days"), Timedelta("3 days"), Timedelta("4 days"), Timedelta("5 days"), ], name="timedelta_data", ) result = s.describe() expected = Series( [5, s[2], s.std(), s[0], s[1], s[2], s[3], s[4]], name="timedelta_data", index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"], ) tm.assert_series_equal(result, expected) s = Series( [Period("2020-01", "M"), Period("2020-01", "M"), Period("2019-12", "M")], name="period_data", ) result = s.describe() expected = Series( [3, 2, s[0], 2], name="period_data", index=["count", "unique", "top", "freq"], ) tm.assert_series_equal(result, expected) def test_describe_empty_object(self): # https://github.com/pandas-dev/pandas/issues/27183 s = Series([None, None], dtype=object) result = s.describe() expected = Series( [0, 0, np.nan, np.nan], dtype=object, index=["count", "unique", "top", "freq"], ) tm.assert_series_equal(result, expected) result = s[:0].describe() tm.assert_series_equal(result, expected) # ensure NaN, not None assert np.isnan(result.iloc[2]) assert np.isnan(result.iloc[3]) def test_describe_with_tz(self, tz_naive_fixture): # GH 21332 tz = tz_naive_fixture name = str(tz_naive_fixture) start = Timestamp(2018, 1, 1) end = Timestamp(2018, 1, 5) s = Series(date_range(start, end, tz=tz), name=name) result = s.describe(datetime_is_numeric=True) expected = Series( [ 5, Timestamp(2018, 1, 3).tz_localize(tz), start.tz_localize(tz), s[1], s[2], s[3], end.tz_localize(tz), ], name=name, index=["count", "mean", "min", "25%", "50%", "75%", "max"], ) tm.assert_series_equal(result, expected) def test_describe_with_tz_warns(self): name = tz = "CET" start = Timestamp(2018, 1, 1) end = Timestamp(2018, 1, 5) s = Series(date_range(start, end, tz=tz), name=name) with tm.assert_produces_warning(FutureWarning): result = s.describe() expected = Series( [ 5, 5, s.value_counts().index[0], 1, start.tz_localize(tz), end.tz_localize(tz), ], name=name, index=["count", "unique", "top", "freq", "first", "last"], ) tm.assert_series_equal(result, expected) def test_datetime_is_numeric_includes_datetime(self): s = Series(date_range("2012", periods=3)) result = s.describe(datetime_is_numeric=True) expected = Series( [ 3, Timestamp("2012-01-02"), Timestamp("2012-01-01"), Timestamp("2012-01-01T12:00:00"), Timestamp("2012-01-02"), Timestamp("2012-01-02T12:00:00"), Timestamp("2012-01-03"), ], index=["count", "mean", "min", "25%", "50%", "75%", "max"], ) tm.assert_series_equal(result, expected)