You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1533 lines
50 KiB
1533 lines
50 KiB
from datetime import (
|
|
datetime,
|
|
timedelta,
|
|
)
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import (
|
|
Categorical,
|
|
DataFrame,
|
|
DatetimeIndex,
|
|
Index,
|
|
NaT,
|
|
Period,
|
|
PeriodIndex,
|
|
RangeIndex,
|
|
Series,
|
|
Timedelta,
|
|
TimedeltaIndex,
|
|
Timestamp,
|
|
date_range,
|
|
isna,
|
|
timedelta_range,
|
|
to_timedelta,
|
|
)
|
|
import pandas._testing as tm
|
|
from pandas.core import nanops
|
|
|
|
|
|
def get_objs():
|
|
indexes = [
|
|
tm.makeBoolIndex(10, name="a"),
|
|
tm.makeIntIndex(10, name="a"),
|
|
tm.makeFloatIndex(10, name="a"),
|
|
tm.makeDateIndex(10, name="a"),
|
|
tm.makeDateIndex(10, name="a").tz_localize(tz="US/Eastern"),
|
|
tm.makePeriodIndex(10, name="a"),
|
|
tm.makeStringIndex(10, name="a"),
|
|
tm.makeUnicodeIndex(10, name="a"),
|
|
]
|
|
|
|
arr = np.random.randn(10)
|
|
series = [Series(arr, index=idx, name="a") for idx in indexes]
|
|
|
|
objs = indexes + series
|
|
return objs
|
|
|
|
|
|
objs = get_objs()
|
|
|
|
|
|
class TestReductions:
|
|
@pytest.mark.parametrize("opname", ["max", "min"])
|
|
@pytest.mark.parametrize("obj", objs)
|
|
def test_ops(self, opname, obj):
|
|
result = getattr(obj, opname)()
|
|
if not isinstance(obj, PeriodIndex):
|
|
expected = getattr(obj.values, opname)()
|
|
else:
|
|
expected = Period(ordinal=getattr(obj.asi8, opname)(), freq=obj.freq)
|
|
|
|
if getattr(obj, "tz", None) is not None:
|
|
# We need to de-localize before comparing to the numpy-produced result
|
|
expected = expected.astype("M8[ns]").astype("int64")
|
|
assert result.value == expected
|
|
else:
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize("opname", ["max", "min"])
|
|
@pytest.mark.parametrize(
|
|
"dtype, val",
|
|
[
|
|
("object", 2.0),
|
|
("float64", 2.0),
|
|
("datetime64[ns]", datetime(2011, 11, 1)),
|
|
("Int64", 2),
|
|
("boolean", True),
|
|
],
|
|
)
|
|
def test_nanminmax(self, opname, dtype, val, index_or_series):
|
|
# GH#7261
|
|
klass = index_or_series
|
|
|
|
def check_missing(res):
|
|
if dtype == "datetime64[ns]":
|
|
return res is NaT
|
|
elif dtype in ["Int64", "boolean"]:
|
|
return res is pd.NA
|
|
else:
|
|
return isna(res)
|
|
|
|
obj = klass([None], dtype=dtype)
|
|
assert check_missing(getattr(obj, opname)())
|
|
assert check_missing(getattr(obj, opname)(skipna=False))
|
|
|
|
obj = klass([], dtype=dtype)
|
|
assert check_missing(getattr(obj, opname)())
|
|
assert check_missing(getattr(obj, opname)(skipna=False))
|
|
|
|
if dtype == "object":
|
|
# generic test with object only works for empty / all NaN
|
|
return
|
|
|
|
obj = klass([None, val], dtype=dtype)
|
|
assert getattr(obj, opname)() == val
|
|
assert check_missing(getattr(obj, opname)(skipna=False))
|
|
|
|
obj = klass([None, val, None], dtype=dtype)
|
|
assert getattr(obj, opname)() == val
|
|
assert check_missing(getattr(obj, opname)(skipna=False))
|
|
|
|
@pytest.mark.parametrize("opname", ["max", "min"])
|
|
def test_nanargminmax(self, opname, index_or_series):
|
|
# GH#7261
|
|
klass = index_or_series
|
|
arg_op = "arg" + opname if klass is Index else "idx" + opname
|
|
|
|
obj = klass([NaT, datetime(2011, 11, 1)])
|
|
assert getattr(obj, arg_op)() == 1
|
|
result = getattr(obj, arg_op)(skipna=False)
|
|
if klass is Series:
|
|
assert np.isnan(result)
|
|
else:
|
|
assert result == -1
|
|
|
|
obj = klass([NaT, datetime(2011, 11, 1), NaT])
|
|
# check DatetimeIndex non-monotonic path
|
|
assert getattr(obj, arg_op)() == 1
|
|
result = getattr(obj, arg_op)(skipna=False)
|
|
if klass is Series:
|
|
assert np.isnan(result)
|
|
else:
|
|
assert result == -1
|
|
|
|
@pytest.mark.parametrize("opname", ["max", "min"])
|
|
@pytest.mark.parametrize("dtype", ["M8[ns]", "datetime64[ns, UTC]"])
|
|
def test_nanops_empty_object(self, opname, index_or_series, dtype):
|
|
klass = index_or_series
|
|
arg_op = "arg" + opname if klass is Index else "idx" + opname
|
|
|
|
obj = klass([], dtype=dtype)
|
|
|
|
assert getattr(obj, opname)() is NaT
|
|
assert getattr(obj, opname)(skipna=False) is NaT
|
|
|
|
with pytest.raises(ValueError, match="empty sequence"):
|
|
getattr(obj, arg_op)()
|
|
with pytest.raises(ValueError, match="empty sequence"):
|
|
getattr(obj, arg_op)(skipna=False)
|
|
|
|
def test_argminmax(self):
|
|
obj = Index(np.arange(5, dtype="int64"))
|
|
assert obj.argmin() == 0
|
|
assert obj.argmax() == 4
|
|
|
|
obj = Index([np.nan, 1, np.nan, 2])
|
|
assert obj.argmin() == 1
|
|
assert obj.argmax() == 3
|
|
assert obj.argmin(skipna=False) == -1
|
|
assert obj.argmax(skipna=False) == -1
|
|
|
|
obj = Index([np.nan])
|
|
assert obj.argmin() == -1
|
|
assert obj.argmax() == -1
|
|
assert obj.argmin(skipna=False) == -1
|
|
assert obj.argmax(skipna=False) == -1
|
|
|
|
obj = Index([NaT, datetime(2011, 11, 1), datetime(2011, 11, 2), NaT])
|
|
assert obj.argmin() == 1
|
|
assert obj.argmax() == 2
|
|
assert obj.argmin(skipna=False) == -1
|
|
assert obj.argmax(skipna=False) == -1
|
|
|
|
obj = Index([NaT])
|
|
assert obj.argmin() == -1
|
|
assert obj.argmax() == -1
|
|
assert obj.argmin(skipna=False) == -1
|
|
assert obj.argmax(skipna=False) == -1
|
|
|
|
@pytest.mark.parametrize("op, expected_col", [["max", "a"], ["min", "b"]])
|
|
def test_same_tz_min_max_axis_1(self, op, expected_col):
|
|
# GH 10390
|
|
df = DataFrame(
|
|
date_range("2016-01-01 00:00:00", periods=3, tz="UTC"), columns=["a"]
|
|
)
|
|
df["b"] = df.a.subtract(Timedelta(seconds=3600))
|
|
result = getattr(df, op)(axis=1)
|
|
expected = df[expected_col].rename(None)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("func", ["maximum", "minimum"])
|
|
def test_numpy_reduction_with_tz_aware_dtype(self, tz_aware_fixture, func):
|
|
# GH 15552
|
|
tz = tz_aware_fixture
|
|
arg = pd.to_datetime(["2019"]).tz_localize(tz)
|
|
expected = Series(arg)
|
|
result = getattr(np, func)(expected, expected)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
class TestIndexReductions:
|
|
# Note: the name TestIndexReductions indicates these tests
|
|
# were moved from a Index-specific test file, _not_ that these tests are
|
|
# intended long-term to be Index-specific
|
|
|
|
@pytest.mark.parametrize(
|
|
"start,stop,step",
|
|
[
|
|
(0, 400, 3),
|
|
(500, 0, -6),
|
|
(-(10**6), 10**6, 4),
|
|
(10**6, -(10**6), -4),
|
|
(0, 10, 20),
|
|
],
|
|
)
|
|
def test_max_min_range(self, start, stop, step):
|
|
# GH#17607
|
|
idx = RangeIndex(start, stop, step)
|
|
expected = idx._values.max()
|
|
result = idx.max()
|
|
assert result == expected
|
|
|
|
# skipna should be irrelevant since RangeIndex should never have NAs
|
|
result2 = idx.max(skipna=False)
|
|
assert result2 == expected
|
|
|
|
expected = idx._values.min()
|
|
result = idx.min()
|
|
assert result == expected
|
|
|
|
# skipna should be irrelevant since RangeIndex should never have NAs
|
|
result2 = idx.min(skipna=False)
|
|
assert result2 == expected
|
|
|
|
# empty
|
|
idx = RangeIndex(start, stop, -step)
|
|
assert isna(idx.max())
|
|
assert isna(idx.min())
|
|
|
|
def test_minmax_timedelta64(self):
|
|
|
|
# monotonic
|
|
idx1 = TimedeltaIndex(["1 days", "2 days", "3 days"])
|
|
assert idx1.is_monotonic
|
|
|
|
# non-monotonic
|
|
idx2 = TimedeltaIndex(["1 days", np.nan, "3 days", "NaT"])
|
|
assert not idx2.is_monotonic
|
|
|
|
for idx in [idx1, idx2]:
|
|
assert idx.min() == Timedelta("1 days")
|
|
assert idx.max() == Timedelta("3 days")
|
|
assert idx.argmin() == 0
|
|
assert idx.argmax() == 2
|
|
|
|
@pytest.mark.parametrize("op", ["min", "max"])
|
|
def test_minmax_timedelta_empty_or_na(self, op):
|
|
# Return NaT
|
|
obj = TimedeltaIndex([])
|
|
assert getattr(obj, op)() is NaT
|
|
|
|
obj = TimedeltaIndex([NaT])
|
|
assert getattr(obj, op)() is NaT
|
|
|
|
obj = TimedeltaIndex([NaT, NaT, NaT])
|
|
assert getattr(obj, op)() is NaT
|
|
|
|
def test_numpy_minmax_timedelta64(self):
|
|
td = timedelta_range("16815 days", "16820 days", freq="D")
|
|
|
|
assert np.min(td) == Timedelta("16815 days")
|
|
assert np.max(td) == Timedelta("16820 days")
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.min(td, out=0)
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.max(td, out=0)
|
|
|
|
assert np.argmin(td) == 0
|
|
assert np.argmax(td) == 5
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmin(td, out=0)
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmax(td, out=0)
|
|
|
|
def test_timedelta_ops(self):
|
|
# GH#4984
|
|
# make sure ops return Timedelta
|
|
s = Series(
|
|
[Timestamp("20130101") + timedelta(seconds=i * i) for i in range(10)]
|
|
)
|
|
td = s.diff()
|
|
|
|
result = td.mean()
|
|
expected = to_timedelta(timedelta(seconds=9))
|
|
assert result == expected
|
|
|
|
result = td.to_frame().mean()
|
|
assert result[0] == expected
|
|
|
|
result = td.quantile(0.1)
|
|
expected = Timedelta(np.timedelta64(2600, "ms"))
|
|
assert result == expected
|
|
|
|
result = td.median()
|
|
expected = to_timedelta("00:00:09")
|
|
assert result == expected
|
|
|
|
result = td.to_frame().median()
|
|
assert result[0] == expected
|
|
|
|
# GH#6462
|
|
# consistency in returned values for sum
|
|
result = td.sum()
|
|
expected = to_timedelta("00:01:21")
|
|
assert result == expected
|
|
|
|
result = td.to_frame().sum()
|
|
assert result[0] == expected
|
|
|
|
# std
|
|
result = td.std()
|
|
expected = to_timedelta(Series(td.dropna().values).std())
|
|
assert result == expected
|
|
|
|
result = td.to_frame().std()
|
|
assert result[0] == expected
|
|
|
|
# GH#10040
|
|
# make sure NaT is properly handled by median()
|
|
s = Series([Timestamp("2015-02-03"), Timestamp("2015-02-07")])
|
|
assert s.diff().median() == timedelta(days=4)
|
|
|
|
s = Series(
|
|
[Timestamp("2015-02-03"), Timestamp("2015-02-07"), Timestamp("2015-02-15")]
|
|
)
|
|
assert s.diff().median() == timedelta(days=6)
|
|
|
|
@pytest.mark.parametrize("opname", ["skew", "kurt", "sem", "prod", "var"])
|
|
def test_invalid_td64_reductions(self, opname):
|
|
s = Series(
|
|
[Timestamp("20130101") + timedelta(seconds=i * i) for i in range(10)]
|
|
)
|
|
td = s.diff()
|
|
|
|
msg = "|".join(
|
|
[
|
|
f"reduction operation '{opname}' not allowed for this dtype",
|
|
rf"cannot perform {opname} with type timedelta64\[ns\]",
|
|
f"does not support reduction '{opname}'",
|
|
]
|
|
)
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
getattr(td, opname)()
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
getattr(td.to_frame(), opname)(numeric_only=False)
|
|
|
|
def test_minmax_tz(self, tz_naive_fixture):
|
|
tz = tz_naive_fixture
|
|
# monotonic
|
|
idx1 = DatetimeIndex(["2011-01-01", "2011-01-02", "2011-01-03"], tz=tz)
|
|
assert idx1.is_monotonic
|
|
|
|
# non-monotonic
|
|
idx2 = DatetimeIndex(
|
|
["2011-01-01", NaT, "2011-01-03", "2011-01-02", NaT], tz=tz
|
|
)
|
|
assert not idx2.is_monotonic
|
|
|
|
for idx in [idx1, idx2]:
|
|
assert idx.min() == Timestamp("2011-01-01", tz=tz)
|
|
assert idx.max() == Timestamp("2011-01-03", tz=tz)
|
|
assert idx.argmin() == 0
|
|
assert idx.argmax() == 2
|
|
|
|
@pytest.mark.parametrize("op", ["min", "max"])
|
|
def test_minmax_nat_datetime64(self, op):
|
|
# Return NaT
|
|
obj = DatetimeIndex([])
|
|
assert isna(getattr(obj, op)())
|
|
|
|
obj = DatetimeIndex([NaT])
|
|
assert isna(getattr(obj, op)())
|
|
|
|
obj = DatetimeIndex([NaT, NaT, NaT])
|
|
assert isna(getattr(obj, op)())
|
|
|
|
def test_numpy_minmax_integer(self):
|
|
# GH#26125
|
|
idx = Index([1, 2, 3])
|
|
|
|
expected = idx.values.max()
|
|
result = np.max(idx)
|
|
assert result == expected
|
|
|
|
expected = idx.values.min()
|
|
result = np.min(idx)
|
|
assert result == expected
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.min(idx, out=0)
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.max(idx, out=0)
|
|
|
|
expected = idx.values.argmax()
|
|
result = np.argmax(idx)
|
|
assert result == expected
|
|
|
|
expected = idx.values.argmin()
|
|
result = np.argmin(idx)
|
|
assert result == expected
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmin(idx, out=0)
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmax(idx, out=0)
|
|
|
|
def test_numpy_minmax_range(self):
|
|
# GH#26125
|
|
idx = RangeIndex(0, 10, 3)
|
|
|
|
result = np.max(idx)
|
|
assert result == 9
|
|
|
|
result = np.min(idx)
|
|
assert result == 0
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.min(idx, out=0)
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.max(idx, out=0)
|
|
|
|
# No need to test again argmax/argmin compat since the implementation
|
|
# is the same as basic integer index
|
|
|
|
def test_numpy_minmax_datetime64(self):
|
|
dr = date_range(start="2016-01-15", end="2016-01-20")
|
|
|
|
assert np.min(dr) == Timestamp("2016-01-15 00:00:00")
|
|
assert np.max(dr) == Timestamp("2016-01-20 00:00:00")
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.min(dr, out=0)
|
|
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.max(dr, out=0)
|
|
|
|
assert np.argmin(dr) == 0
|
|
assert np.argmax(dr) == 5
|
|
|
|
errmsg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmin(dr, out=0)
|
|
|
|
with pytest.raises(ValueError, match=errmsg):
|
|
np.argmax(dr, out=0)
|
|
|
|
def test_minmax_period(self):
|
|
|
|
# monotonic
|
|
idx1 = PeriodIndex([NaT, "2011-01-01", "2011-01-02", "2011-01-03"], freq="D")
|
|
assert not idx1.is_monotonic
|
|
assert idx1[1:].is_monotonic
|
|
|
|
# non-monotonic
|
|
idx2 = PeriodIndex(
|
|
["2011-01-01", NaT, "2011-01-03", "2011-01-02", NaT], freq="D"
|
|
)
|
|
assert not idx2.is_monotonic
|
|
|
|
for idx in [idx1, idx2]:
|
|
assert idx.min() == Period("2011-01-01", freq="D")
|
|
assert idx.max() == Period("2011-01-03", freq="D")
|
|
assert idx1.argmin() == 1
|
|
assert idx2.argmin() == 0
|
|
assert idx1.argmax() == 3
|
|
assert idx2.argmax() == 2
|
|
|
|
for op in ["min", "max"]:
|
|
# Return NaT
|
|
obj = PeriodIndex([], freq="M")
|
|
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)
|
|
msg = (
|
|
r"Categorical is not ordered for operation min\n"
|
|
r"you can use .as_ordered\(\) to change the Categorical to an ordered one\n"
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
ci.min()
|
|
msg = (
|
|
r"Categorical is not ordered for operation max\n"
|
|
r"you can use .as_ordered\(\) to change the Categorical to an ordered one\n"
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
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", "Float32", "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 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 isna(result)
|
|
|
|
result = getattr(s, method)(skipna=False, min_count=0)
|
|
assert result == unit
|
|
|
|
result = getattr(s, method)(skipna=False, min_count=1)
|
|
assert 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 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 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 = Series([1], dtype=dtype)
|
|
result = getattr(s, method)(min_count=2)
|
|
assert isna(result)
|
|
|
|
result = getattr(s, method)(skipna=False, min_count=2)
|
|
assert isna(result)
|
|
|
|
s = Series([np.nan], dtype=dtype)
|
|
result = getattr(s, method)(min_count=2)
|
|
assert isna(result)
|
|
|
|
s = Series([np.nan, 1], dtype=dtype)
|
|
result = getattr(s, method)(min_count=2)
|
|
assert isna(result)
|
|
|
|
@pytest.mark.parametrize("method, unit", [("sum", 0.0), ("prod", 1.0)])
|
|
def test_empty_multi(self, method, unit):
|
|
s = Series(
|
|
[1, np.nan, np.nan, np.nan],
|
|
index=pd.MultiIndex.from_product([("a", "b"), (0, 1)]),
|
|
)
|
|
# 1 / 0 by default
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = getattr(s, method)(level=0)
|
|
expected = Series([1, unit], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# min_count=0
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = getattr(s, method)(level=0, min_count=0)
|
|
expected = Series([1, unit], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# min_count=1
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = getattr(s, method)(level=0, min_count=1)
|
|
expected = Series([1, np.nan], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("method", ["mean"])
|
|
@pytest.mark.parametrize("dtype", ["Float64", "Int64", "boolean"])
|
|
def test_ops_consistency_on_empty_nullable(self, method, dtype):
|
|
|
|
# GH#34814
|
|
# consistency for nullable dtypes on empty or ALL-NA mean
|
|
|
|
# empty series
|
|
eser = Series([], dtype=dtype)
|
|
result = getattr(eser, method)()
|
|
assert result is pd.NA
|
|
|
|
# ALL-NA series
|
|
nser = Series([np.nan], dtype=dtype)
|
|
result = getattr(nser, method)()
|
|
assert result is pd.NA
|
|
|
|
@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 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\[ns\]",
|
|
"does not support reduction 'var'",
|
|
]
|
|
)
|
|
with pytest.raises(TypeError, match=msg):
|
|
getattr(tdser, method)()
|
|
else:
|
|
result = getattr(tdser, method)()
|
|
assert result is 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 NaT
|
|
assert Series([], dtype=dtype).max() is NaT
|
|
assert Series([], dtype=dtype).min(skipna=False) is NaT
|
|
assert Series([], dtype=dtype).max(skipna=False) is 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 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 isna(allna.idxmin())
|
|
|
|
# datetime64[ns]
|
|
s = Series(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 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 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 = 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 = 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 s.any()
|
|
|
|
@pytest.mark.parametrize("klass", [Index, Series])
|
|
def test_numpy_all_any(self, klass):
|
|
# GH#40180
|
|
idx = klass([0, 1, 2])
|
|
assert not np.all(idx)
|
|
assert np.any(idx)
|
|
idx = Index([1, 2, 3])
|
|
assert np.all(idx)
|
|
|
|
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 s2.any(skipna=False)
|
|
assert not s2.any(skipna=True)
|
|
|
|
# Check level.
|
|
s = Series([False, False, True, True, False, True], index=[0, 0, 1, 1, 2, 2])
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
tm.assert_series_equal(s.all(level=0), Series([False, True, False]))
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
tm.assert_series_equal(s.any(level=0), Series([False, True, True]))
|
|
|
|
msg = "Option bool_only is not implemented with option level"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
s.any(bool_only=True, level=0)
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
s.all(bool_only=True, level=0)
|
|
|
|
# GH#38810 bool_only is not implemented alone.
|
|
msg = "Series.any does not implement bool_only"
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
s.any(bool_only=True)
|
|
msg = "Series.all does not implement bool_only."
|
|
with pytest.raises(NotImplementedError, match=msg):
|
|
s.all(bool_only=True)
|
|
|
|
@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
def test_any_all_object_dtype(self, bool_agg_func, skipna):
|
|
# GH#12863
|
|
ser = Series(["a", "b", "c", "d", "e"], dtype=object)
|
|
result = getattr(ser, bool_agg_func)(skipna=skipna)
|
|
expected = True
|
|
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
|
|
@pytest.mark.parametrize(
|
|
"data", [[False, None], [None, False], [False, np.nan], [np.nan, False]]
|
|
)
|
|
def test_any_all_object_dtype_missing(self, data, bool_agg_func):
|
|
# GH#27709
|
|
ser = Series(data)
|
|
result = getattr(ser, bool_agg_func)(skipna=False)
|
|
|
|
# None is treated is False, but np.nan is treated as True
|
|
expected = bool_agg_func == "any" and None not in data
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize("dtype", ["boolean", "Int64", "UInt64", "Float64"])
|
|
@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
|
|
@pytest.mark.parametrize("skipna", [True, False])
|
|
@pytest.mark.parametrize(
|
|
# expected_data indexed as [[skipna=False/any, skipna=False/all],
|
|
# [skipna=True/any, skipna=True/all]]
|
|
"data,expected_data",
|
|
[
|
|
([0, 0, 0], [[False, False], [False, False]]),
|
|
([1, 1, 1], [[True, True], [True, True]]),
|
|
([pd.NA, pd.NA, pd.NA], [[pd.NA, pd.NA], [False, True]]),
|
|
([0, pd.NA, 0], [[pd.NA, False], [False, False]]),
|
|
([1, pd.NA, 1], [[True, pd.NA], [True, True]]),
|
|
([1, pd.NA, 0], [[True, False], [True, False]]),
|
|
],
|
|
)
|
|
def test_any_all_nullable_kleene_logic(
|
|
self, bool_agg_func, skipna, data, dtype, expected_data
|
|
):
|
|
# GH-37506, GH-41967
|
|
ser = Series(data, dtype=dtype)
|
|
expected = expected_data[skipna][bool_agg_func == "all"]
|
|
|
|
result = getattr(ser, bool_agg_func)(skipna=skipna)
|
|
assert (result is pd.NA and expected is pd.NA) or result == expected
|
|
|
|
@pytest.mark.parametrize(
|
|
"bool_agg_func,expected",
|
|
[("all", [False, True, False]), ("any", [False, True, True])],
|
|
)
|
|
def test_any_all_boolean_level(self, bool_agg_func, expected):
|
|
# GH#33449
|
|
ser = Series(
|
|
[False, False, True, True, False, True],
|
|
index=[0, 0, 1, 1, 2, 2],
|
|
dtype="boolean",
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = getattr(ser, bool_agg_func)(level=0)
|
|
expected = Series(expected, dtype="boolean")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_any_axis1_bool_only(self):
|
|
# GH#32432
|
|
df = DataFrame({"A": [True, False], "B": [1, 2]})
|
|
result = df.any(axis=1, bool_only=True)
|
|
expected = Series([True, False])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_any_all_datetimelike(self):
|
|
# GH#38723 these may not be the desired long-term behavior (GH#34479)
|
|
# but in the interim should be internally consistent
|
|
dta = date_range("1995-01-02", periods=3)._data
|
|
ser = Series(dta)
|
|
df = DataFrame(ser)
|
|
|
|
assert dta.all()
|
|
assert dta.any()
|
|
|
|
assert ser.all()
|
|
assert ser.any()
|
|
|
|
assert df.any().all()
|
|
assert df.all().all()
|
|
|
|
dta = dta.tz_localize("UTC")
|
|
ser = Series(dta)
|
|
df = DataFrame(ser)
|
|
|
|
assert dta.all()
|
|
assert dta.any()
|
|
|
|
assert ser.all()
|
|
assert ser.any()
|
|
|
|
assert df.any().all()
|
|
assert df.all().all()
|
|
|
|
tda = dta - dta[0]
|
|
ser = Series(tda)
|
|
df = DataFrame(ser)
|
|
|
|
assert tda.any()
|
|
assert not tda.all()
|
|
|
|
assert ser.any()
|
|
assert not ser.all()
|
|
|
|
assert df.any().all()
|
|
assert not df.all().any()
|
|
|
|
def test_timedelta64_analytics(self):
|
|
|
|
# index min/max
|
|
dti = date_range("2012-1-1", periods=3, freq="D")
|
|
td = Series(dti) - 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(date_range("20120101", periods=3))
|
|
s2 = Series(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 = Timedelta("2 days")
|
|
assert result == expected
|
|
|
|
result = td.min()
|
|
expected = Timedelta("1 days")
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize(
|
|
"test_input,error_type",
|
|
[
|
|
(Series([], dtype="float64"), ValueError),
|
|
# For strings, or any Series with dtype 'O'
|
|
(Series(["foo", "bar", "baz"]), TypeError),
|
|
(Series([(1,), (2,)]), TypeError),
|
|
# For mixed data types
|
|
(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
|
|
"""
|
|
msg = (
|
|
"reduction operation 'argmin' not allowed for this dtype|"
|
|
"attempt to get argmin of an empty sequence"
|
|
)
|
|
with pytest.raises(error_type, match=msg):
|
|
test_input.idxmin()
|
|
with pytest.raises(error_type, match=msg):
|
|
test_input.idxmin(skipna=False)
|
|
msg = (
|
|
"reduction operation 'argmax' not allowed for this dtype|"
|
|
"attempt to get argmax of an empty sequence"
|
|
)
|
|
with pytest.raises(error_type, match=msg):
|
|
test_input.idxmax()
|
|
with pytest.raises(error_type, match=msg):
|
|
test_input.idxmax(skipna=False)
|
|
|
|
def test_idxminmax_with_inf(self):
|
|
# For numeric data with NA and Inf (GH #13595)
|
|
s = 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([NaT, NaT]),
|
|
Series([NaT, Timedelta("nat")]),
|
|
Series([Timedelta("nat"), Timedelta("nat")]),
|
|
],
|
|
)
|
|
def test_minmax_nat_series(self, nat_ser):
|
|
# GH#23282
|
|
assert nat_ser.min() is NaT
|
|
assert nat_ser.max() is NaT
|
|
assert nat_ser.min(skipna=False) is NaT
|
|
assert nat_ser.max(skipna=False) is NaT
|
|
|
|
@pytest.mark.parametrize(
|
|
"nat_df",
|
|
[
|
|
DataFrame([NaT, NaT]),
|
|
DataFrame([NaT, Timedelta("nat")]),
|
|
DataFrame([Timedelta("nat"), Timedelta("nat")]),
|
|
],
|
|
)
|
|
def test_minmax_nat_dataframe(self, nat_df):
|
|
# GH#23282
|
|
assert nat_df.min()[0] is NaT
|
|
assert nat_df.max()[0] is NaT
|
|
assert nat_df.min(skipna=False)[0] is NaT
|
|
assert nat_df.max(skipna=False)[0] is NaT
|
|
|
|
def test_min_max(self):
|
|
rng = 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, Timestamp)
|
|
assert isinstance(the_max, 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 = 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 = Timestamp(df.TS.iat[-1])
|
|
assert isinstance(result, Timestamp)
|
|
assert result == exp
|
|
|
|
result = df.TS.min()
|
|
exp = Timestamp(df.TS.iat[0])
|
|
assert isinstance(result, 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):
|
|
result = s.mode(dropna=False)
|
|
result = result.sort_values().reset_index(drop=True)
|
|
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_mode_boolean_with_na(self):
|
|
# GH#42107
|
|
ser = Series([True, False, True, pd.NA], dtype="boolean")
|
|
result = ser.mode()
|
|
expected = Series({0: True}, dtype="boolean")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"array,expected,dtype",
|
|
[
|
|
(
|
|
[0, 1j, 1, 1, 1 + 1j, 1 + 2j],
|
|
Series([1], dtype=np.complex128),
|
|
np.complex128,
|
|
),
|
|
(
|
|
[0, 1j, 1, 1, 1 + 1j, 1 + 2j],
|
|
Series([1], dtype=np.complex64),
|
|
np.complex64,
|
|
),
|
|
(
|
|
[1 + 1j, 2j, 1 + 1j],
|
|
Series([1 + 1j], dtype=np.complex128),
|
|
np.complex128,
|
|
),
|
|
],
|
|
)
|
|
def test_single_mode_value_complex(self, array, expected, dtype):
|
|
result = Series(array, dtype=dtype).mode()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"array,expected,dtype",
|
|
[
|
|
(
|
|
# no modes
|
|
[0, 1j, 1, 1 + 1j, 1 + 2j],
|
|
Series([0j, 1j, 1 + 0j, 1 + 1j, 1 + 2j], dtype=np.complex128),
|
|
np.complex128,
|
|
),
|
|
(
|
|
[1 + 1j, 2j, 1 + 1j, 2j, 3],
|
|
Series([2j, 1 + 1j], dtype=np.complex64),
|
|
np.complex64,
|
|
),
|
|
],
|
|
)
|
|
def test_multimode_complex(self, array, expected, dtype):
|
|
# GH 17927
|
|
# mode tries to sort multimodal series.
|
|
# Complex numbers are sorted by their magnitude
|
|
result = Series(array, dtype=dtype).mode()
|
|
tm.assert_series_equal(result, expected)
|
|
|