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151 lines
4.7 KiB
151 lines
4.7 KiB
import numpy as np
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from pandas import (
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Period,
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Series,
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Timedelta,
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Timestamp,
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date_range,
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)
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import pandas._testing as tm
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class TestSeriesDescribe:
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def test_describe_ints(self):
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ser = Series([0, 1, 2, 3, 4], name="int_data")
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result = ser.describe()
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expected = Series(
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[5, 2, ser.std(), 0, 1, 2, 3, 4],
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name="int_data",
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index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
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)
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tm.assert_series_equal(result, expected)
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def test_describe_bools(self):
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ser = Series([True, True, False, False, False], name="bool_data")
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result = ser.describe()
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expected = Series(
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[5, 2, False, 3], name="bool_data", index=["count", "unique", "top", "freq"]
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)
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tm.assert_series_equal(result, expected)
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def test_describe_strs(self):
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ser = Series(["a", "a", "b", "c", "d"], name="str_data")
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result = ser.describe()
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expected = Series(
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[5, 4, "a", 2], name="str_data", index=["count", "unique", "top", "freq"]
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)
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tm.assert_series_equal(result, expected)
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def test_describe_timedelta64(self):
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ser = Series(
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[
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Timedelta("1 days"),
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Timedelta("2 days"),
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Timedelta("3 days"),
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Timedelta("4 days"),
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Timedelta("5 days"),
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],
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name="timedelta_data",
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)
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result = ser.describe()
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expected = Series(
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[5, ser[2], ser.std(), ser[0], ser[1], ser[2], ser[3], ser[4]],
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name="timedelta_data",
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index=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
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)
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tm.assert_series_equal(result, expected)
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def test_describe_period(self):
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ser = Series(
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[Period("2020-01", "M"), Period("2020-01", "M"), Period("2019-12", "M")],
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name="period_data",
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)
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result = ser.describe()
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expected = Series(
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[3, 2, ser[0], 2],
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name="period_data",
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index=["count", "unique", "top", "freq"],
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)
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tm.assert_series_equal(result, expected)
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def test_describe_empty_object(self):
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# https://github.com/pandas-dev/pandas/issues/27183
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s = Series([None, None], dtype=object)
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result = s.describe()
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expected = Series(
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[0, 0, np.nan, np.nan],
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dtype=object,
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index=["count", "unique", "top", "freq"],
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)
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tm.assert_series_equal(result, expected)
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result = s[:0].describe()
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tm.assert_series_equal(result, expected)
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# ensure NaN, not None
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assert np.isnan(result.iloc[2])
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assert np.isnan(result.iloc[3])
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def test_describe_with_tz(self, tz_naive_fixture):
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# GH 21332
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tz = tz_naive_fixture
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name = str(tz_naive_fixture)
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start = Timestamp(2018, 1, 1)
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end = Timestamp(2018, 1, 5)
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s = Series(date_range(start, end, tz=tz), name=name)
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result = s.describe(datetime_is_numeric=True)
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expected = Series(
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[
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5,
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Timestamp(2018, 1, 3).tz_localize(tz),
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start.tz_localize(tz),
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s[1],
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s[2],
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s[3],
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end.tz_localize(tz),
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],
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name=name,
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index=["count", "mean", "min", "25%", "50%", "75%", "max"],
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)
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tm.assert_series_equal(result, expected)
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def test_describe_with_tz_warns(self):
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name = tz = "CET"
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start = Timestamp(2018, 1, 1)
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end = Timestamp(2018, 1, 5)
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s = Series(date_range(start, end, tz=tz), name=name)
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with tm.assert_produces_warning(FutureWarning):
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result = s.describe()
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expected = Series(
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[
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5,
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5,
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s.value_counts().index[0],
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1,
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start.tz_localize(tz),
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end.tz_localize(tz),
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],
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name=name,
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index=["count", "unique", "top", "freq", "first", "last"],
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)
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tm.assert_series_equal(result, expected)
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def test_datetime_is_numeric_includes_datetime(self):
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s = Series(date_range("2012", periods=3))
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result = s.describe(datetime_is_numeric=True)
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expected = Series(
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[
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3,
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Timestamp("2012-01-02"),
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Timestamp("2012-01-01"),
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Timestamp("2012-01-01T12:00:00"),
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Timestamp("2012-01-02"),
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Timestamp("2012-01-02T12:00:00"),
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Timestamp("2012-01-03"),
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],
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index=["count", "mean", "min", "25%", "50%", "75%", "max"],
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)
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tm.assert_series_equal(result, expected)
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