Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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1335 lines
43 KiB
1335 lines
43 KiB
from datetime import datetime, timedelta
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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DatetimeIndex,
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Index,
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NaT,
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Period,
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PeriodIndex,
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RangeIndex,
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Series,
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Timedelta,
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TimedeltaIndex,
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Timestamp,
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isna,
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timedelta_range,
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to_timedelta,
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)
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import pandas._testing as tm
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from pandas.core import nanops
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def get_objs():
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indexes = [
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tm.makeBoolIndex(10, name="a"),
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tm.makeIntIndex(10, name="a"),
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tm.makeFloatIndex(10, name="a"),
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tm.makeDateIndex(10, name="a"),
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tm.makeDateIndex(10, name="a").tz_localize(tz="US/Eastern"),
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tm.makePeriodIndex(10, name="a"),
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tm.makeStringIndex(10, name="a"),
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tm.makeUnicodeIndex(10, name="a"),
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]
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arr = np.random.randn(10)
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series = [Series(arr, index=idx, name="a") for idx in indexes]
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objs = indexes + series
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return objs
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objs = get_objs()
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class TestReductions:
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@pytest.mark.parametrize("opname", ["max", "min"])
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@pytest.mark.parametrize("obj", objs)
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def test_ops(self, opname, obj):
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result = getattr(obj, opname)()
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if not isinstance(obj, PeriodIndex):
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expected = getattr(obj.values, opname)()
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else:
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expected = pd.Period(ordinal=getattr(obj.asi8, opname)(), freq=obj.freq)
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try:
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assert result == expected
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except TypeError:
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# comparing tz-aware series with np.array results in
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# TypeError
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expected = expected.astype("M8[ns]").astype("int64")
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assert result.value == expected
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@pytest.mark.parametrize("opname", ["max", "min"])
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@pytest.mark.parametrize(
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"dtype, val",
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[
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("object", 2.0),
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("float64", 2.0),
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("datetime64[ns]", datetime(2011, 11, 1)),
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("Int64", 2),
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("boolean", True),
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],
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)
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def test_nanminmax(self, opname, dtype, val, index_or_series):
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# GH#7261
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klass = index_or_series
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if dtype in ["Int64", "boolean"] and klass == pd.Index:
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pytest.skip("EAs can't yet be stored in an index")
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def check_missing(res):
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if dtype == "datetime64[ns]":
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return res is pd.NaT
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elif dtype == "Int64":
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return res is pd.NA
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else:
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return pd.isna(res)
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obj = klass([None], dtype=dtype)
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assert check_missing(getattr(obj, opname)())
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assert check_missing(getattr(obj, opname)(skipna=False))
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obj = klass([], dtype=dtype)
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assert check_missing(getattr(obj, opname)())
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assert check_missing(getattr(obj, opname)(skipna=False))
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if dtype == "object":
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# generic test with object only works for empty / all NaN
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return
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obj = klass([None, val], dtype=dtype)
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assert getattr(obj, opname)() == val
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assert check_missing(getattr(obj, opname)(skipna=False))
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obj = klass([None, val, None], dtype=dtype)
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assert getattr(obj, opname)() == val
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assert check_missing(getattr(obj, opname)(skipna=False))
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@pytest.mark.parametrize("opname", ["max", "min"])
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def test_nanargminmax(self, opname, index_or_series):
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# GH#7261
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klass = index_or_series
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arg_op = "arg" + opname if klass is Index else "idx" + opname
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obj = klass([pd.NaT, datetime(2011, 11, 1)])
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assert getattr(obj, arg_op)() == 1
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result = getattr(obj, arg_op)(skipna=False)
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if klass is Series:
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assert np.isnan(result)
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else:
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assert result == -1
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obj = klass([pd.NaT, datetime(2011, 11, 1), pd.NaT])
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# check DatetimeIndex non-monotonic path
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assert getattr(obj, arg_op)() == 1
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result = getattr(obj, arg_op)(skipna=False)
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if klass is Series:
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assert np.isnan(result)
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else:
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assert result == -1
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@pytest.mark.parametrize("opname", ["max", "min"])
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@pytest.mark.parametrize("dtype", ["M8[ns]", "datetime64[ns, UTC]"])
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def test_nanops_empty_object(self, opname, index_or_series, dtype):
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klass = index_or_series
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arg_op = "arg" + opname if klass is Index else "idx" + opname
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obj = klass([], dtype=dtype)
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assert getattr(obj, opname)() is pd.NaT
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assert getattr(obj, opname)(skipna=False) is pd.NaT
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with pytest.raises(ValueError, match="empty sequence"):
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getattr(obj, arg_op)()
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with pytest.raises(ValueError, match="empty sequence"):
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getattr(obj, arg_op)(skipna=False)
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def test_argminmax(self):
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obj = Index(np.arange(5, dtype="int64"))
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assert obj.argmin() == 0
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assert obj.argmax() == 4
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obj = Index([np.nan, 1, np.nan, 2])
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assert obj.argmin() == 1
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assert obj.argmax() == 3
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assert obj.argmin(skipna=False) == -1
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assert obj.argmax(skipna=False) == -1
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obj = Index([np.nan])
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assert obj.argmin() == -1
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assert obj.argmax() == -1
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assert obj.argmin(skipna=False) == -1
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assert obj.argmax(skipna=False) == -1
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obj = Index([pd.NaT, datetime(2011, 11, 1), datetime(2011, 11, 2), pd.NaT])
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assert obj.argmin() == 1
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assert obj.argmax() == 2
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assert obj.argmin(skipna=False) == -1
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assert obj.argmax(skipna=False) == -1
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obj = Index([pd.NaT])
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assert obj.argmin() == -1
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assert obj.argmax() == -1
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assert obj.argmin(skipna=False) == -1
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assert obj.argmax(skipna=False) == -1
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@pytest.mark.parametrize("op, expected_col", [["max", "a"], ["min", "b"]])
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def test_same_tz_min_max_axis_1(self, op, expected_col):
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# GH 10390
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df = DataFrame(
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pd.date_range("2016-01-01 00:00:00", periods=3, tz="UTC"), columns=["a"]
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)
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df["b"] = df.a.subtract(pd.Timedelta(seconds=3600))
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result = getattr(df, op)(axis=1)
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expected = df[expected_col].rename(None)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("func", ["maximum", "minimum"])
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def test_numpy_reduction_with_tz_aware_dtype(self, tz_aware_fixture, func):
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# GH 15552
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tz = tz_aware_fixture
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arg = pd.to_datetime(["2019"]).tz_localize(tz)
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expected = Series(arg)
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result = getattr(np, func)(expected, expected)
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tm.assert_series_equal(result, expected)
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class TestIndexReductions:
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# Note: the name TestIndexReductions indicates these tests
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# were moved from a Index-specific test file, _not_ that these tests are
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# intended long-term to be Index-specific
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@pytest.mark.parametrize(
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"start,stop,step",
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[
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(0, 400, 3),
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(500, 0, -6),
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(-(10 ** 6), 10 ** 6, 4),
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(10 ** 6, -(10 ** 6), -4),
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(0, 10, 20),
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],
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)
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def test_max_min_range(self, start, stop, step):
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# GH#17607
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idx = RangeIndex(start, stop, step)
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expected = idx._int64index.max()
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result = idx.max()
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assert result == expected
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# skipna should be irrelevant since RangeIndex should never have NAs
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result2 = idx.max(skipna=False)
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assert result2 == expected
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expected = idx._int64index.min()
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result = idx.min()
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assert result == expected
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# skipna should be irrelevant since RangeIndex should never have NAs
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result2 = idx.min(skipna=False)
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assert result2 == expected
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# empty
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idx = RangeIndex(start, stop, -step)
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assert isna(idx.max())
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assert isna(idx.min())
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def test_minmax_timedelta64(self):
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# monotonic
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idx1 = TimedeltaIndex(["1 days", "2 days", "3 days"])
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assert idx1.is_monotonic
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# non-monotonic
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idx2 = TimedeltaIndex(["1 days", np.nan, "3 days", "NaT"])
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assert not idx2.is_monotonic
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for idx in [idx1, idx2]:
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assert idx.min() == Timedelta("1 days")
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assert idx.max() == Timedelta("3 days")
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assert idx.argmin() == 0
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assert idx.argmax() == 2
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@pytest.mark.parametrize("op", ["min", "max"])
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def test_minmax_timedelta_empty_or_na(self, op):
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# Return NaT
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obj = TimedeltaIndex([])
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assert getattr(obj, op)() is pd.NaT
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obj = TimedeltaIndex([pd.NaT])
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assert getattr(obj, op)() is pd.NaT
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obj = TimedeltaIndex([pd.NaT, pd.NaT, pd.NaT])
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assert getattr(obj, op)() is pd.NaT
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def test_numpy_minmax_timedelta64(self):
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td = timedelta_range("16815 days", "16820 days", freq="D")
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assert np.min(td) == Timedelta("16815 days")
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assert np.max(td) == Timedelta("16820 days")
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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np.min(td, out=0)
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with pytest.raises(ValueError, match=errmsg):
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np.max(td, out=0)
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assert np.argmin(td) == 0
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assert np.argmax(td) == 5
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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np.argmin(td, out=0)
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with pytest.raises(ValueError, match=errmsg):
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np.argmax(td, out=0)
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def test_timedelta_ops(self):
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# GH#4984
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# make sure ops return Timedelta
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s = Series(
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[Timestamp("20130101") + timedelta(seconds=i * i) for i in range(10)]
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)
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td = s.diff()
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result = td.mean()
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expected = to_timedelta(timedelta(seconds=9))
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assert result == expected
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result = td.to_frame().mean()
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assert result[0] == expected
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result = td.quantile(0.1)
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expected = Timedelta(np.timedelta64(2600, "ms"))
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assert result == expected
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result = td.median()
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expected = to_timedelta("00:00:09")
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assert result == expected
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result = td.to_frame().median()
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assert result[0] == expected
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# GH#6462
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# consistency in returned values for sum
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result = td.sum()
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expected = to_timedelta("00:01:21")
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assert result == expected
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result = td.to_frame().sum()
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assert result[0] == expected
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# std
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result = td.std()
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expected = to_timedelta(Series(td.dropna().values).std())
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assert result == expected
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result = td.to_frame().std()
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assert result[0] == expected
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# GH#10040
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# make sure NaT is properly handled by median()
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s = Series([Timestamp("2015-02-03"), Timestamp("2015-02-07")])
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assert s.diff().median() == timedelta(days=4)
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s = Series(
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[Timestamp("2015-02-03"), Timestamp("2015-02-07"), Timestamp("2015-02-15")]
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)
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assert s.diff().median() == timedelta(days=6)
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@pytest.mark.parametrize("opname", ["skew", "kurt", "sem", "prod", "var"])
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def test_invalid_td64_reductions(self, opname):
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s = Series(
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[Timestamp("20130101") + timedelta(seconds=i * i) for i in range(10)]
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)
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td = s.diff()
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msg = "|".join(
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[
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f"reduction operation '{opname}' not allowed for this dtype",
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rf"cannot perform {opname} with type timedelta64\[ns\]",
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]
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)
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with pytest.raises(TypeError, match=msg):
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getattr(td, opname)()
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with pytest.raises(TypeError, match=msg):
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getattr(td.to_frame(), opname)(numeric_only=False)
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def test_minmax_tz(self, tz_naive_fixture):
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tz = tz_naive_fixture
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# monotonic
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idx1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], tz=tz)
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assert idx1.is_monotonic
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# non-monotonic
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idx2 = pd.DatetimeIndex(
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["2011-01-01", pd.NaT, "2011-01-03", "2011-01-02", pd.NaT], tz=tz
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)
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assert not idx2.is_monotonic
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for idx in [idx1, idx2]:
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assert idx.min() == Timestamp("2011-01-01", tz=tz)
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assert idx.max() == Timestamp("2011-01-03", tz=tz)
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assert idx.argmin() == 0
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assert idx.argmax() == 2
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@pytest.mark.parametrize("op", ["min", "max"])
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def test_minmax_nat_datetime64(self, op):
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# Return NaT
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obj = DatetimeIndex([])
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assert pd.isna(getattr(obj, op)())
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obj = DatetimeIndex([pd.NaT])
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assert pd.isna(getattr(obj, op)())
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obj = DatetimeIndex([pd.NaT, pd.NaT, pd.NaT])
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assert pd.isna(getattr(obj, op)())
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def test_numpy_minmax_integer(self):
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# GH#26125
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idx = Index([1, 2, 3])
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expected = idx.values.max()
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result = np.max(idx)
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assert result == expected
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expected = idx.values.min()
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result = np.min(idx)
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assert result == expected
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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np.min(idx, out=0)
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with pytest.raises(ValueError, match=errmsg):
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np.max(idx, out=0)
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expected = idx.values.argmax()
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result = np.argmax(idx)
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assert result == expected
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expected = idx.values.argmin()
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result = np.argmin(idx)
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assert result == expected
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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np.argmin(idx, out=0)
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with pytest.raises(ValueError, match=errmsg):
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np.argmax(idx, out=0)
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def test_numpy_minmax_range(self):
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# GH#26125
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idx = RangeIndex(0, 10, 3)
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expected = idx._int64index.max()
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result = np.max(idx)
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assert result == expected
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expected = idx._int64index.min()
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result = np.min(idx)
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assert result == expected
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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np.min(idx, out=0)
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with pytest.raises(ValueError, match=errmsg):
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np.max(idx, out=0)
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# No need to test again argmax/argmin compat since the implementation
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# is the same as basic integer index
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def test_numpy_minmax_datetime64(self):
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dr = pd.date_range(start="2016-01-15", end="2016-01-20")
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assert np.min(dr) == Timestamp("2016-01-15 00:00:00", freq="D")
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assert np.max(dr) == Timestamp("2016-01-20 00:00:00", freq="D")
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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np.min(dr, out=0)
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with pytest.raises(ValueError, match=errmsg):
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np.max(dr, out=0)
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assert np.argmin(dr) == 0
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assert np.argmax(dr) == 5
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errmsg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=errmsg):
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np.argmin(dr, out=0)
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with pytest.raises(ValueError, match=errmsg):
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np.argmax(dr, out=0)
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def test_minmax_period(self):
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# monotonic
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idx1 = pd.PeriodIndex([NaT, "2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
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assert not idx1.is_monotonic
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assert idx1[1:].is_monotonic
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# non-monotonic
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idx2 = pd.PeriodIndex(
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["2011-01-01", NaT, "2011-01-03", "2011-01-02", NaT], freq="D"
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)
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assert not idx2.is_monotonic
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for idx in [idx1, idx2]:
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assert idx.min() == pd.Period("2011-01-01", freq="D")
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assert idx.max() == pd.Period("2011-01-03", freq="D")
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assert idx1.argmin() == 1
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assert idx2.argmin() == 0
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assert idx1.argmax() == 3
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assert idx2.argmax() == 2
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for op in ["min", "max"]:
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# Return NaT
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obj = PeriodIndex([], freq="M")
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|
result = getattr(obj, op)()
|
|
assert result is NaT
|
|
|
|
obj = PeriodIndex([NaT], freq="M")
|
|
result = getattr(obj, op)()
|
|
assert result is NaT
|
|
|
|
obj = PeriodIndex([NaT, NaT, NaT], freq="M")
|
|
result = getattr(obj, op)()
|
|
assert result is NaT
|
|
|
|
def test_numpy_minmax_period(self):
|
|
pr = pd.period_range(start="2016-01-15", end="2016-01-20")
|
|
|
|
assert np.min(pr) == Period("2016-01-15", freq="D")
|
|
assert np.max(pr) == Period("2016-01-20", freq="D")
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.min(pr, out=0)
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.max(pr, out=0)
|
|
|
|
assert np.argmin(pr) == 0
|
|
assert np.argmax(pr) == 5
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmin(pr, out=0)
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmax(pr, out=0)
|
|
|
|
def test_min_max_categorical(self):
|
|
|
|
ci = pd.CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=False)
|
|
with pytest.raises(TypeError):
|
|
ci.min()
|
|
with pytest.raises(TypeError):
|
|
ci.max()
|
|
|
|
ci = pd.CategoricalIndex(list("aabbca"), categories=list("cab"), ordered=True)
|
|
assert ci.min() == "c"
|
|
assert ci.max() == "b"
|
|
|
|
|
|
class TestSeriesReductions:
|
|
# Note: the name TestSeriesReductions indicates these tests
|
|
# were moved from a series-specific test file, _not_ that these tests are
|
|
# intended long-term to be series-specific
|
|
|
|
def test_sum_inf(self):
|
|
s = Series(np.random.randn(10))
|
|
s2 = s.copy()
|
|
|
|
s[5:8] = np.inf
|
|
s2[5:8] = np.nan
|
|
|
|
assert np.isinf(s.sum())
|
|
|
|
arr = np.random.randn(100, 100).astype("f4")
|
|
arr[:, 2] = np.inf
|
|
|
|
with pd.option_context("mode.use_inf_as_na", True):
|
|
tm.assert_almost_equal(s.sum(), s2.sum())
|
|
|
|
res = nanops.nansum(arr, axis=1)
|
|
assert np.isinf(res).all()
|
|
|
|
@pytest.mark.parametrize("dtype", ["float64", "Int64", "boolean", "object"])
|
|
@pytest.mark.parametrize("use_bottleneck", [True, False])
|
|
@pytest.mark.parametrize("method, unit", [("sum", 0.0), ("prod", 1.0)])
|
|
def test_empty(self, method, unit, use_bottleneck, dtype):
|
|
with pd.option_context("use_bottleneck", use_bottleneck):
|
|
# GH#9422 / GH#18921
|
|
# Entirely empty
|
|
s = Series([], dtype=dtype)
|
|
# NA by default
|
|
result = getattr(s, method)()
|
|
assert result == unit
|
|
|
|
# Explicit
|
|
result = getattr(s, method)(min_count=0)
|
|
assert result == unit
|
|
|
|
result = getattr(s, method)(min_count=1)
|
|
assert pd.isna(result)
|
|
|
|
# Skipna, default
|
|
result = getattr(s, method)(skipna=True)
|
|
result == unit
|
|
|
|
# Skipna, explicit
|
|
result = getattr(s, method)(skipna=True, min_count=0)
|
|
assert result == unit
|
|
|
|
result = getattr(s, method)(skipna=True, min_count=1)
|
|
assert pd.isna(result)
|
|
|
|
result = getattr(s, method)(skipna=False, min_count=0)
|
|
assert result == unit
|
|
|
|
result = getattr(s, method)(skipna=False, min_count=1)
|
|
assert pd.isna(result)
|
|
|
|
# All-NA
|
|
s = Series([np.nan], dtype=dtype)
|
|
# NA by default
|
|
result = getattr(s, method)()
|
|
assert result == unit
|
|
|
|
# Explicit
|
|
result = getattr(s, method)(min_count=0)
|
|
assert result == unit
|
|
|
|
result = getattr(s, method)(min_count=1)
|
|
assert pd.isna(result)
|
|
|
|
# Skipna, default
|
|
result = getattr(s, method)(skipna=True)
|
|
result == unit
|
|
|
|
# skipna, explicit
|
|
result = getattr(s, method)(skipna=True, min_count=0)
|
|
assert result == unit
|
|
|
|
result = getattr(s, method)(skipna=True, min_count=1)
|
|
assert pd.isna(result)
|
|
|
|
# Mix of valid, empty
|
|
s = Series([np.nan, 1], dtype=dtype)
|
|
# Default
|
|
result = getattr(s, method)()
|
|
assert result == 1.0
|
|
|
|
# Explicit
|
|
result = getattr(s, method)(min_count=0)
|
|
assert result == 1.0
|
|
|
|
result = getattr(s, method)(min_count=1)
|
|
assert result == 1.0
|
|
|
|
# Skipna
|
|
result = getattr(s, method)(skipna=True)
|
|
assert result == 1.0
|
|
|
|
result = getattr(s, method)(skipna=True, min_count=0)
|
|
assert result == 1.0
|
|
|
|
# GH#844 (changed in GH#9422)
|
|
df = DataFrame(np.empty((10, 0)), dtype=dtype)
|
|
assert (getattr(df, method)(1) == unit).all()
|
|
|
|
s = pd.Series([1], dtype=dtype)
|
|
result = getattr(s, method)(min_count=2)
|
|
assert pd.isna(result)
|
|
|
|
result = getattr(s, method)(skipna=False, min_count=2)
|
|
assert pd.isna(result)
|
|
|
|
s = pd.Series([np.nan], dtype=dtype)
|
|
result = getattr(s, method)(min_count=2)
|
|
assert pd.isna(result)
|
|
|
|
s = pd.Series([np.nan, 1], dtype=dtype)
|
|
result = getattr(s, method)(min_count=2)
|
|
assert pd.isna(result)
|
|
|
|
@pytest.mark.parametrize("method, unit", [("sum", 0.0), ("prod", 1.0)])
|
|
def test_empty_multi(self, method, unit):
|
|
s = pd.Series(
|
|
[1, np.nan, np.nan, np.nan],
|
|
index=pd.MultiIndex.from_product([("a", "b"), (0, 1)]),
|
|
)
|
|
# 1 / 0 by default
|
|
result = getattr(s, method)(level=0)
|
|
expected = pd.Series([1, unit], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# min_count=0
|
|
result = getattr(s, method)(level=0, min_count=0)
|
|
expected = pd.Series([1, unit], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# min_count=1
|
|
result = getattr(s, method)(level=0, min_count=1)
|
|
expected = pd.Series([1, np.nan], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("method", ["mean", "median", "std", "var"])
|
|
def test_ops_consistency_on_empty(self, method):
|
|
|
|
# GH#7869
|
|
# consistency on empty
|
|
|
|
# float
|
|
result = getattr(Series(dtype=float), method)()
|
|
assert pd.isna(result)
|
|
|
|
# timedelta64[ns]
|
|
tdser = Series([], dtype="m8[ns]")
|
|
if method == "var":
|
|
msg = "|".join(
|
|
[
|
|
"operation 'var' not allowed",
|
|
r"cannot perform var with type timedelta64\[ns\]",
|
|
]
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
getattr(tdser, method)()
|
|
else:
|
|
result = getattr(tdser, method)()
|
|
assert result is pd.NaT
|
|
|
|
def test_nansum_buglet(self):
|
|
ser = Series([1.0, np.nan], index=[0, 1])
|
|
result = np.nansum(ser)
|
|
tm.assert_almost_equal(result, 1)
|
|
|
|
@pytest.mark.parametrize("use_bottleneck", [True, False])
|
|
def test_sum_overflow(self, use_bottleneck):
|
|
|
|
with pd.option_context("use_bottleneck", use_bottleneck):
|
|
# GH#6915
|
|
# overflowing on the smaller int dtypes
|
|
for dtype in ["int32", "int64"]:
|
|
v = np.arange(5000000, dtype=dtype)
|
|
s = Series(v)
|
|
|
|
result = s.sum(skipna=False)
|
|
assert int(result) == v.sum(dtype="int64")
|
|
result = s.min(skipna=False)
|
|
assert int(result) == 0
|
|
result = s.max(skipna=False)
|
|
assert int(result) == v[-1]
|
|
|
|
for dtype in ["float32", "float64"]:
|
|
v = np.arange(5000000, dtype=dtype)
|
|
s = Series(v)
|
|
|
|
result = s.sum(skipna=False)
|
|
assert result == v.sum(dtype=dtype)
|
|
result = s.min(skipna=False)
|
|
assert np.allclose(float(result), 0.0)
|
|
result = s.max(skipna=False)
|
|
assert np.allclose(float(result), v[-1])
|
|
|
|
def test_empty_timeseries_reductions_return_nat(self):
|
|
# covers GH#11245
|
|
for dtype in ("m8[ns]", "m8[ns]", "M8[ns]", "M8[ns, UTC]"):
|
|
assert Series([], dtype=dtype).min() is pd.NaT
|
|
assert Series([], dtype=dtype).max() is pd.NaT
|
|
assert Series([], dtype=dtype).min(skipna=False) is pd.NaT
|
|
assert Series([], dtype=dtype).max(skipna=False) is pd.NaT
|
|
|
|
def test_numpy_argmin(self):
|
|
# See GH#16830
|
|
data = np.arange(1, 11)
|
|
|
|
s = Series(data, index=data)
|
|
result = np.argmin(s)
|
|
|
|
expected = np.argmin(data)
|
|
assert result == expected
|
|
|
|
result = s.argmin()
|
|
|
|
assert result == expected
|
|
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.argmin(s, out=data)
|
|
|
|
def test_numpy_argmax(self):
|
|
# See GH#16830
|
|
data = np.arange(1, 11)
|
|
|
|
s = Series(data, index=data)
|
|
result = np.argmax(s)
|
|
expected = np.argmax(data)
|
|
assert result == expected
|
|
|
|
result = s.argmax()
|
|
|
|
assert result == expected
|
|
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.argmax(s, out=data)
|
|
|
|
def test_idxmin(self):
|
|
# test idxmin
|
|
# _check_stat_op approach can not be used here because of isna check.
|
|
string_series = tm.makeStringSeries().rename("series")
|
|
|
|
# add some NaNs
|
|
string_series[5:15] = np.NaN
|
|
|
|
# skipna or no
|
|
assert string_series[string_series.idxmin()] == string_series.min()
|
|
assert pd.isna(string_series.idxmin(skipna=False))
|
|
|
|
# no NaNs
|
|
nona = string_series.dropna()
|
|
assert nona[nona.idxmin()] == nona.min()
|
|
assert nona.index.values.tolist().index(nona.idxmin()) == nona.values.argmin()
|
|
|
|
# all NaNs
|
|
allna = string_series * np.nan
|
|
assert pd.isna(allna.idxmin())
|
|
|
|
# datetime64[ns]
|
|
s = Series(pd.date_range("20130102", periods=6))
|
|
result = s.idxmin()
|
|
assert result == 0
|
|
|
|
s[0] = np.nan
|
|
result = s.idxmin()
|
|
assert result == 1
|
|
|
|
def test_idxmax(self):
|
|
# test idxmax
|
|
# _check_stat_op approach can not be used here because of isna check.
|
|
string_series = tm.makeStringSeries().rename("series")
|
|
|
|
# add some NaNs
|
|
string_series[5:15] = np.NaN
|
|
|
|
# skipna or no
|
|
assert string_series[string_series.idxmax()] == string_series.max()
|
|
assert pd.isna(string_series.idxmax(skipna=False))
|
|
|
|
# no NaNs
|
|
nona = string_series.dropna()
|
|
assert nona[nona.idxmax()] == nona.max()
|
|
assert nona.index.values.tolist().index(nona.idxmax()) == nona.values.argmax()
|
|
|
|
# all NaNs
|
|
allna = string_series * np.nan
|
|
assert pd.isna(allna.idxmax())
|
|
|
|
from pandas import date_range
|
|
|
|
s = Series(date_range("20130102", periods=6))
|
|
result = s.idxmax()
|
|
assert result == 5
|
|
|
|
s[5] = np.nan
|
|
result = s.idxmax()
|
|
assert result == 4
|
|
|
|
# Float64Index
|
|
# GH#5914
|
|
s = pd.Series([1, 2, 3], [1.1, 2.1, 3.1])
|
|
result = s.idxmax()
|
|
assert result == 3.1
|
|
result = s.idxmin()
|
|
assert result == 1.1
|
|
|
|
s = pd.Series(s.index, s.index)
|
|
result = s.idxmax()
|
|
assert result == 3.1
|
|
result = s.idxmin()
|
|
assert result == 1.1
|
|
|
|
def test_all_any(self):
|
|
ts = tm.makeTimeSeries()
|
|
bool_series = ts > 0
|
|
assert not bool_series.all()
|
|
assert bool_series.any()
|
|
|
|
# Alternative types, with implicit 'object' dtype.
|
|
s = Series(["abc", True])
|
|
assert "abc" == s.any() # 'abc' || True => 'abc'
|
|
|
|
def test_all_any_params(self):
|
|
# Check skipna, with implicit 'object' dtype.
|
|
s1 = Series([np.nan, True])
|
|
s2 = Series([np.nan, False])
|
|
assert s1.all(skipna=False) # nan && True => True
|
|
assert s1.all(skipna=True)
|
|
assert np.isnan(s2.any(skipna=False)) # nan || False => nan
|
|
assert not s2.any(skipna=True)
|
|
|
|
# Check level.
|
|
s = pd.Series([False, False, True, True, False, True], index=[0, 0, 1, 1, 2, 2])
|
|
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
|
|
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
|
|
|
|
# bool_only is not implemented with level option.
|
|
with pytest.raises(NotImplementedError):
|
|
s.any(bool_only=True, level=0)
|
|
with pytest.raises(NotImplementedError):
|
|
s.all(bool_only=True, level=0)
|
|
|
|
# bool_only is not implemented alone.
|
|
with pytest.raises(NotImplementedError):
|
|
s.any(bool_only=True)
|
|
with pytest.raises(NotImplementedError):
|
|
s.all(bool_only=True)
|
|
|
|
def test_all_any_boolean(self):
|
|
# Check skipna, with boolean type
|
|
s1 = Series([pd.NA, True], dtype="boolean")
|
|
s2 = Series([pd.NA, False], dtype="boolean")
|
|
assert s1.all(skipna=False) is pd.NA # NA && True => NA
|
|
assert s1.all(skipna=True)
|
|
assert s2.any(skipna=False) is pd.NA # NA || False => NA
|
|
assert not s2.any(skipna=True)
|
|
|
|
# GH-33253: all True / all False values buggy with skipna=False
|
|
s3 = Series([True, True], dtype="boolean")
|
|
s4 = Series([False, False], dtype="boolean")
|
|
assert s3.all(skipna=False)
|
|
assert not s4.any(skipna=False)
|
|
|
|
# Check level TODO(GH-33449) result should also be boolean
|
|
s = pd.Series(
|
|
[False, False, True, True, False, True],
|
|
index=[0, 0, 1, 1, 2, 2],
|
|
dtype="boolean",
|
|
)
|
|
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
|
|
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
|
|
|
|
def test_timedelta64_analytics(self):
|
|
|
|
# index min/max
|
|
dti = pd.date_range("2012-1-1", periods=3, freq="D")
|
|
td = Series(dti) - pd.Timestamp("20120101")
|
|
|
|
result = td.idxmin()
|
|
assert result == 0
|
|
|
|
result = td.idxmax()
|
|
assert result == 2
|
|
|
|
# GH#2982
|
|
# with NaT
|
|
td[0] = np.nan
|
|
|
|
result = td.idxmin()
|
|
assert result == 1
|
|
|
|
result = td.idxmax()
|
|
assert result == 2
|
|
|
|
# abs
|
|
s1 = Series(pd.date_range("20120101", periods=3))
|
|
s2 = Series(pd.date_range("20120102", periods=3))
|
|
expected = Series(s2 - s1)
|
|
|
|
result = np.abs(s1 - s2)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = (s1 - s2).abs()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# max/min
|
|
result = td.max()
|
|
expected = pd.Timedelta("2 days")
|
|
assert result == expected
|
|
|
|
result = td.min()
|
|
expected = pd.Timedelta("1 days")
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize(
|
|
"test_input,error_type",
|
|
[
|
|
(pd.Series([], dtype="float64"), ValueError),
|
|
# For strings, or any Series with dtype 'O'
|
|
(pd.Series(["foo", "bar", "baz"]), TypeError),
|
|
(pd.Series([(1,), (2,)]), TypeError),
|
|
# For mixed data types
|
|
(pd.Series(["foo", "foo", "bar", "bar", None, np.nan, "baz"]), TypeError),
|
|
],
|
|
)
|
|
def test_assert_idxminmax_raises(self, test_input, error_type):
|
|
"""
|
|
Cases where ``Series.argmax`` and related should raise an exception
|
|
"""
|
|
with pytest.raises(error_type):
|
|
test_input.idxmin()
|
|
with pytest.raises(error_type):
|
|
test_input.idxmin(skipna=False)
|
|
with pytest.raises(error_type):
|
|
test_input.idxmax()
|
|
with pytest.raises(error_type):
|
|
test_input.idxmax(skipna=False)
|
|
|
|
def test_idxminmax_with_inf(self):
|
|
# For numeric data with NA and Inf (GH #13595)
|
|
s = pd.Series([0, -np.inf, np.inf, np.nan])
|
|
|
|
assert s.idxmin() == 1
|
|
assert np.isnan(s.idxmin(skipna=False))
|
|
|
|
assert s.idxmax() == 2
|
|
assert np.isnan(s.idxmax(skipna=False))
|
|
|
|
# Using old-style behavior that treats floating point nan, -inf, and
|
|
# +inf as missing
|
|
with pd.option_context("mode.use_inf_as_na", True):
|
|
assert s.idxmin() == 0
|
|
assert np.isnan(s.idxmin(skipna=False))
|
|
assert s.idxmax() == 0
|
|
np.isnan(s.idxmax(skipna=False))
|
|
|
|
|
|
class TestDatetime64SeriesReductions:
|
|
# Note: the name TestDatetime64SeriesReductions indicates these tests
|
|
# were moved from a series-specific test file, _not_ that these tests are
|
|
# intended long-term to be series-specific
|
|
|
|
@pytest.mark.parametrize(
|
|
"nat_ser",
|
|
[
|
|
Series([pd.NaT, pd.NaT]),
|
|
Series([pd.NaT, pd.Timedelta("nat")]),
|
|
Series([pd.Timedelta("nat"), pd.Timedelta("nat")]),
|
|
],
|
|
)
|
|
def test_minmax_nat_series(self, nat_ser):
|
|
# GH#23282
|
|
assert nat_ser.min() is pd.NaT
|
|
assert nat_ser.max() is pd.NaT
|
|
assert nat_ser.min(skipna=False) is pd.NaT
|
|
assert nat_ser.max(skipna=False) is pd.NaT
|
|
|
|
@pytest.mark.parametrize(
|
|
"nat_df",
|
|
[
|
|
pd.DataFrame([pd.NaT, pd.NaT]),
|
|
pd.DataFrame([pd.NaT, pd.Timedelta("nat")]),
|
|
pd.DataFrame([pd.Timedelta("nat"), pd.Timedelta("nat")]),
|
|
],
|
|
)
|
|
def test_minmax_nat_dataframe(self, nat_df):
|
|
# GH#23282
|
|
assert nat_df.min()[0] is pd.NaT
|
|
assert nat_df.max()[0] is pd.NaT
|
|
assert nat_df.min(skipna=False)[0] is pd.NaT
|
|
assert nat_df.max(skipna=False)[0] is pd.NaT
|
|
|
|
def test_min_max(self):
|
|
rng = pd.date_range("1/1/2000", "12/31/2000")
|
|
rng2 = rng.take(np.random.permutation(len(rng)))
|
|
|
|
the_min = rng2.min()
|
|
the_max = rng2.max()
|
|
assert isinstance(the_min, pd.Timestamp)
|
|
assert isinstance(the_max, pd.Timestamp)
|
|
assert the_min == rng[0]
|
|
assert the_max == rng[-1]
|
|
|
|
assert rng.min() == rng[0]
|
|
assert rng.max() == rng[-1]
|
|
|
|
def test_min_max_series(self):
|
|
rng = pd.date_range("1/1/2000", periods=10, freq="4h")
|
|
lvls = ["A", "A", "A", "B", "B", "B", "C", "C", "C", "C"]
|
|
df = DataFrame({"TS": rng, "V": np.random.randn(len(rng)), "L": lvls})
|
|
|
|
result = df.TS.max()
|
|
exp = pd.Timestamp(df.TS.iat[-1])
|
|
assert isinstance(result, pd.Timestamp)
|
|
assert result == exp
|
|
|
|
result = df.TS.min()
|
|
exp = pd.Timestamp(df.TS.iat[0])
|
|
assert isinstance(result, pd.Timestamp)
|
|
assert result == exp
|
|
|
|
|
|
class TestCategoricalSeriesReductions:
|
|
# Note: the name TestCategoricalSeriesReductions indicates these tests
|
|
# were moved from a series-specific test file, _not_ that these tests are
|
|
# intended long-term to be series-specific
|
|
|
|
@pytest.mark.parametrize("function", ["min", "max"])
|
|
def test_min_max_unordered_raises(self, function):
|
|
# unordered cats have no min/max
|
|
cat = Series(Categorical(["a", "b", "c", "d"], ordered=False))
|
|
msg = f"Categorical is not ordered for operation {function}"
|
|
with pytest.raises(TypeError, match=msg):
|
|
getattr(cat, function)()
|
|
|
|
@pytest.mark.parametrize(
|
|
"values, categories",
|
|
[
|
|
(list("abc"), list("abc")),
|
|
(list("abc"), list("cba")),
|
|
(list("abc") + [np.nan], list("cba")),
|
|
([1, 2, 3], [3, 2, 1]),
|
|
([1, 2, 3, np.nan], [3, 2, 1]),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("function", ["min", "max"])
|
|
def test_min_max_ordered(self, values, categories, function):
|
|
# GH 25303
|
|
cat = Series(Categorical(values, categories=categories, ordered=True))
|
|
result = getattr(cat, function)(skipna=True)
|
|
expected = categories[0] if function == "min" else categories[2]
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize("function", ["min", "max"])
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_min_max_ordered_with_nan_only(self, function, skipna):
|
|
# https://github.com/pandas-dev/pandas/issues/33450
|
|
cat = Series(Categorical([np.nan], categories=[1, 2], ordered=True))
|
|
result = getattr(cat, function)(skipna=skipna)
|
|
assert result is np.nan
|
|
|
|
@pytest.mark.parametrize("function", ["min", "max"])
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_min_max_skipna(self, function, skipna):
|
|
cat = Series(
|
|
Categorical(["a", "b", np.nan, "a"], categories=["b", "a"], ordered=True)
|
|
)
|
|
result = getattr(cat, function)(skipna=skipna)
|
|
|
|
if skipna is True:
|
|
expected = "b" if function == "min" else "a"
|
|
assert result == expected
|
|
else:
|
|
assert result is np.nan
|
|
|
|
|
|
class TestSeriesMode:
|
|
# Note: the name TestSeriesMode indicates these tests
|
|
# were moved from a series-specific test file, _not_ that these tests are
|
|
# intended long-term to be series-specific
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected",
|
|
[(True, Series([], dtype=np.float64)), (False, Series([], dtype=np.float64))],
|
|
)
|
|
def test_mode_empty(self, dropna, expected):
|
|
s = Series([], dtype=np.float64)
|
|
result = s.mode(dropna)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, data, expected",
|
|
[
|
|
(True, [1, 1, 1, 2], [1]),
|
|
(True, [1, 1, 1, 2, 3, 3, 3], [1, 3]),
|
|
(False, [1, 1, 1, 2], [1]),
|
|
(False, [1, 1, 1, 2, 3, 3, 3], [1, 3]),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"dt", list(np.typecodes["AllInteger"] + np.typecodes["Float"])
|
|
)
|
|
def test_mode_numerical(self, dropna, data, expected, dt):
|
|
s = Series(data, dtype=dt)
|
|
result = s.mode(dropna)
|
|
expected = Series(expected, dtype=dt)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("dropna, expected", [(True, [1.0]), (False, [1, np.nan])])
|
|
def test_mode_numerical_nan(self, dropna, expected):
|
|
s = Series([1, 1, 2, np.nan, np.nan])
|
|
result = s.mode(dropna)
|
|
expected = Series(expected)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected1, expected2, expected3",
|
|
[(True, ["b"], ["bar"], ["nan"]), (False, ["b"], [np.nan], ["nan"])],
|
|
)
|
|
def test_mode_str_obj(self, dropna, expected1, expected2, expected3):
|
|
# Test string and object types.
|
|
data = ["a"] * 2 + ["b"] * 3
|
|
|
|
s = Series(data, dtype="c")
|
|
result = s.mode(dropna)
|
|
expected1 = Series(expected1, dtype="c")
|
|
tm.assert_series_equal(result, expected1)
|
|
|
|
data = ["foo", "bar", "bar", np.nan, np.nan, np.nan]
|
|
|
|
s = Series(data, dtype=object)
|
|
result = s.mode(dropna)
|
|
expected2 = Series(expected2, dtype=object)
|
|
tm.assert_series_equal(result, expected2)
|
|
|
|
data = ["foo", "bar", "bar", np.nan, np.nan, np.nan]
|
|
|
|
s = Series(data, dtype=object).astype(str)
|
|
result = s.mode(dropna)
|
|
expected3 = Series(expected3, dtype=str)
|
|
tm.assert_series_equal(result, expected3)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected1, expected2",
|
|
[(True, ["foo"], ["foo"]), (False, ["foo"], [np.nan])],
|
|
)
|
|
def test_mode_mixeddtype(self, dropna, expected1, expected2):
|
|
s = Series([1, "foo", "foo"])
|
|
result = s.mode(dropna)
|
|
expected = Series(expected1)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
s = Series([1, "foo", "foo", np.nan, np.nan, np.nan])
|
|
result = s.mode(dropna)
|
|
expected = Series(expected2, dtype=object)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected1, expected2",
|
|
[
|
|
(
|
|
True,
|
|
["1900-05-03", "2011-01-03", "2013-01-02"],
|
|
["2011-01-03", "2013-01-02"],
|
|
),
|
|
(False, [np.nan], [np.nan, "2011-01-03", "2013-01-02"]),
|
|
],
|
|
)
|
|
def test_mode_datetime(self, dropna, expected1, expected2):
|
|
s = Series(
|
|
["2011-01-03", "2013-01-02", "1900-05-03", "nan", "nan"], dtype="M8[ns]"
|
|
)
|
|
result = s.mode(dropna)
|
|
expected1 = Series(expected1, dtype="M8[ns]")
|
|
tm.assert_series_equal(result, expected1)
|
|
|
|
s = Series(
|
|
[
|
|
"2011-01-03",
|
|
"2013-01-02",
|
|
"1900-05-03",
|
|
"2011-01-03",
|
|
"2013-01-02",
|
|
"nan",
|
|
"nan",
|
|
],
|
|
dtype="M8[ns]",
|
|
)
|
|
result = s.mode(dropna)
|
|
expected2 = Series(expected2, dtype="M8[ns]")
|
|
tm.assert_series_equal(result, expected2)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected1, expected2",
|
|
[
|
|
(True, ["-1 days", "0 days", "1 days"], ["2 min", "1 day"]),
|
|
(False, [np.nan], [np.nan, "2 min", "1 day"]),
|
|
],
|
|
)
|
|
def test_mode_timedelta(self, dropna, expected1, expected2):
|
|
# gh-5986: Test timedelta types.
|
|
|
|
s = Series(
|
|
["1 days", "-1 days", "0 days", "nan", "nan"], dtype="timedelta64[ns]"
|
|
)
|
|
result = s.mode(dropna)
|
|
expected1 = Series(expected1, dtype="timedelta64[ns]")
|
|
tm.assert_series_equal(result, expected1)
|
|
|
|
s = Series(
|
|
[
|
|
"1 day",
|
|
"1 day",
|
|
"-1 day",
|
|
"-1 day 2 min",
|
|
"2 min",
|
|
"2 min",
|
|
"nan",
|
|
"nan",
|
|
],
|
|
dtype="timedelta64[ns]",
|
|
)
|
|
result = s.mode(dropna)
|
|
expected2 = Series(expected2, dtype="timedelta64[ns]")
|
|
tm.assert_series_equal(result, expected2)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected1, expected2, expected3",
|
|
[
|
|
(
|
|
True,
|
|
Categorical([1, 2], categories=[1, 2]),
|
|
Categorical(["a"], categories=[1, "a"]),
|
|
Categorical([3, 1], categories=[3, 2, 1], ordered=True),
|
|
),
|
|
(
|
|
False,
|
|
Categorical([np.nan], categories=[1, 2]),
|
|
Categorical([np.nan, "a"], categories=[1, "a"]),
|
|
Categorical([np.nan, 3, 1], categories=[3, 2, 1], ordered=True),
|
|
),
|
|
],
|
|
)
|
|
def test_mode_category(self, dropna, expected1, expected2, expected3):
|
|
s = Series(Categorical([1, 2, np.nan, np.nan]))
|
|
result = s.mode(dropna)
|
|
expected1 = Series(expected1, dtype="category")
|
|
tm.assert_series_equal(result, expected1)
|
|
|
|
s = Series(Categorical([1, "a", "a", np.nan, np.nan]))
|
|
result = s.mode(dropna)
|
|
expected2 = Series(expected2, dtype="category")
|
|
tm.assert_series_equal(result, expected2)
|
|
|
|
s = Series(
|
|
Categorical(
|
|
[1, 1, 2, 3, 3, np.nan, np.nan], categories=[3, 2, 1], ordered=True
|
|
)
|
|
)
|
|
result = s.mode(dropna)
|
|
expected3 = Series(expected3, dtype="category")
|
|
tm.assert_series_equal(result, expected3)
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected1, expected2",
|
|
[(True, [2 ** 63], [1, 2 ** 63]), (False, [2 ** 63], [1, 2 ** 63])],
|
|
)
|
|
def test_mode_intoverflow(self, dropna, expected1, expected2):
|
|
# Test for uint64 overflow.
|
|
s = Series([1, 2 ** 63, 2 ** 63], dtype=np.uint64)
|
|
result = s.mode(dropna)
|
|
expected1 = Series(expected1, dtype=np.uint64)
|
|
tm.assert_series_equal(result, expected1)
|
|
|
|
s = Series([1, 2 ** 63], dtype=np.uint64)
|
|
result = s.mode(dropna)
|
|
expected2 = Series(expected2, dtype=np.uint64)
|
|
tm.assert_series_equal(result, expected2)
|
|
|
|
def test_mode_sortwarning(self):
|
|
# Check for the warning that is raised when the mode
|
|
# results cannot be sorted
|
|
|
|
expected = Series(["foo", np.nan])
|
|
s = Series([1, "foo", "foo", np.nan, np.nan])
|
|
|
|
with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
|
|
result = s.mode(dropna=False)
|
|
result = result.sort_values().reset_index(drop=True)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|