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keshe/.venv/Lib/site-packages/pandas/tests/frame/test_reductions.py

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6 months ago
from datetime import timedelta
from decimal import Decimal
import re
from dateutil.tz import tzlocal
import numpy as np
import pytest
from pandas._config import using_pyarrow_string_dtype
from pandas.compat import (
IS64,
is_platform_windows,
)
from pandas.compat.numpy import np_version_gt2
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Categorical,
CategoricalDtype,
DataFrame,
DatetimeIndex,
Index,
PeriodIndex,
RangeIndex,
Series,
Timestamp,
date_range,
isna,
notna,
to_datetime,
to_timedelta,
)
import pandas._testing as tm
from pandas.core import (
algorithms,
nanops,
)
is_windows_np2_or_is32 = (is_platform_windows() and not np_version_gt2) or not IS64
is_windows_or_is32 = is_platform_windows() or not IS64
def make_skipna_wrapper(alternative, skipna_alternative=None):
"""
Create a function for calling on an array.
Parameters
----------
alternative : function
The function to be called on the array with no NaNs.
Only used when 'skipna_alternative' is None.
skipna_alternative : function
The function to be called on the original array
Returns
-------
function
"""
if skipna_alternative:
def skipna_wrapper(x):
return skipna_alternative(x.values)
else:
def skipna_wrapper(x):
nona = x.dropna()
if len(nona) == 0:
return np.nan
return alternative(nona)
return skipna_wrapper
def assert_stat_op_calc(
opname,
alternative,
frame,
has_skipna=True,
check_dtype=True,
check_dates=False,
rtol=1e-5,
atol=1e-8,
skipna_alternative=None,
):
"""
Check that operator opname works as advertised on frame
Parameters
----------
opname : str
Name of the operator to test on frame
alternative : function
Function that opname is tested against; i.e. "frame.opname()" should
equal "alternative(frame)".
frame : DataFrame
The object that the tests are executed on
has_skipna : bool, default True
Whether the method "opname" has the kwarg "skip_na"
check_dtype : bool, default True
Whether the dtypes of the result of "frame.opname()" and
"alternative(frame)" should be checked.
check_dates : bool, default false
Whether opname should be tested on a Datetime Series
rtol : float, default 1e-5
Relative tolerance.
atol : float, default 1e-8
Absolute tolerance.
skipna_alternative : function, default None
NaN-safe version of alternative
"""
f = getattr(frame, opname)
if check_dates:
df = DataFrame({"b": date_range("1/1/2001", periods=2)})
with tm.assert_produces_warning(None):
result = getattr(df, opname)()
assert isinstance(result, Series)
df["a"] = range(len(df))
with tm.assert_produces_warning(None):
result = getattr(df, opname)()
assert isinstance(result, Series)
assert len(result)
if has_skipna:
def wrapper(x):
return alternative(x.values)
skipna_wrapper = make_skipna_wrapper(alternative, skipna_alternative)
result0 = f(axis=0, skipna=False)
result1 = f(axis=1, skipna=False)
tm.assert_series_equal(
result0, frame.apply(wrapper), check_dtype=check_dtype, rtol=rtol, atol=atol
)
tm.assert_series_equal(
result1,
frame.apply(wrapper, axis=1),
rtol=rtol,
atol=atol,
)
else:
skipna_wrapper = alternative
result0 = f(axis=0)
result1 = f(axis=1)
tm.assert_series_equal(
result0,
frame.apply(skipna_wrapper),
check_dtype=check_dtype,
rtol=rtol,
atol=atol,
)
if opname in ["sum", "prod"]:
expected = frame.apply(skipna_wrapper, axis=1)
tm.assert_series_equal(
result1, expected, check_dtype=False, rtol=rtol, atol=atol
)
# check dtypes
if check_dtype:
lcd_dtype = frame.values.dtype
assert lcd_dtype == result0.dtype
assert lcd_dtype == result1.dtype
# bad axis
with pytest.raises(ValueError, match="No axis named 2"):
f(axis=2)
# all NA case
if has_skipna:
all_na = frame * np.nan
r0 = getattr(all_na, opname)(axis=0)
r1 = getattr(all_na, opname)(axis=1)
if opname in ["sum", "prod"]:
unit = 1 if opname == "prod" else 0 # result for empty sum/prod
expected = Series(unit, index=r0.index, dtype=r0.dtype)
tm.assert_series_equal(r0, expected)
expected = Series(unit, index=r1.index, dtype=r1.dtype)
tm.assert_series_equal(r1, expected)
@pytest.fixture
def bool_frame_with_na():
"""
Fixture for DataFrame of booleans with index of unique strings
Columns are ['A', 'B', 'C', 'D']; some entries are missing
"""
df = DataFrame(
np.concatenate(
[np.ones((15, 4), dtype=bool), np.zeros((15, 4), dtype=bool)], axis=0
),
index=Index([f"foo_{i}" for i in range(30)], dtype=object),
columns=Index(list("ABCD"), dtype=object),
dtype=object,
)
# set some NAs
df.iloc[5:10] = np.nan
df.iloc[15:20, -2:] = np.nan
return df
@pytest.fixture
def float_frame_with_na():
"""
Fixture for DataFrame of floats with index of unique strings
Columns are ['A', 'B', 'C', 'D']; some entries are missing
"""
df = DataFrame(
np.random.default_rng(2).standard_normal((30, 4)),
index=Index([f"foo_{i}" for i in range(30)], dtype=object),
columns=Index(list("ABCD"), dtype=object),
)
# set some NAs
df.iloc[5:10] = np.nan
df.iloc[15:20, -2:] = np.nan
return df
class TestDataFrameAnalytics:
# ---------------------------------------------------------------------
# Reductions
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize(
"opname",
[
"count",
"sum",
"mean",
"product",
"median",
"min",
"max",
"nunique",
"var",
"std",
"sem",
pytest.param("skew", marks=td.skip_if_no("scipy")),
pytest.param("kurt", marks=td.skip_if_no("scipy")),
],
)
def test_stat_op_api_float_string_frame(
self, float_string_frame, axis, opname, using_infer_string
):
if (
(opname in ("sum", "min", "max") and axis == 0)
or opname
in (
"count",
"nunique",
)
) and not (using_infer_string and opname == "sum"):
getattr(float_string_frame, opname)(axis=axis)
else:
if opname in ["var", "std", "sem", "skew", "kurt"]:
msg = "could not convert string to float: 'bar'"
elif opname == "product":
if axis == 1:
msg = "can't multiply sequence by non-int of type 'float'"
else:
msg = "can't multiply sequence by non-int of type 'str'"
elif opname == "sum":
msg = r"unsupported operand type\(s\) for \+: 'float' and 'str'"
elif opname == "mean":
if axis == 0:
# different message on different builds
msg = "|".join(
[
r"Could not convert \['.*'\] to numeric",
"Could not convert string '(bar){30}' to numeric",
]
)
else:
msg = r"unsupported operand type\(s\) for \+: 'float' and 'str'"
elif opname in ["min", "max"]:
msg = "'[><]=' not supported between instances of 'float' and 'str'"
elif opname == "median":
msg = re.compile(
r"Cannot convert \[.*\] to numeric|does not support", flags=re.S
)
if not isinstance(msg, re.Pattern):
msg = msg + "|does not support"
with pytest.raises(TypeError, match=msg):
getattr(float_string_frame, opname)(axis=axis)
if opname != "nunique":
getattr(float_string_frame, opname)(axis=axis, numeric_only=True)
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize(
"opname",
[
"count",
"sum",
"mean",
"product",
"median",
"min",
"max",
"var",
"std",
"sem",
pytest.param("skew", marks=td.skip_if_no("scipy")),
pytest.param("kurt", marks=td.skip_if_no("scipy")),
],
)
def test_stat_op_api_float_frame(self, float_frame, axis, opname):
getattr(float_frame, opname)(axis=axis, numeric_only=False)
def test_stat_op_calc(self, float_frame_with_na, mixed_float_frame):
def count(s):
return notna(s).sum()
def nunique(s):
return len(algorithms.unique1d(s.dropna()))
def var(x):
return np.var(x, ddof=1)
def std(x):
return np.std(x, ddof=1)
def sem(x):
return np.std(x, ddof=1) / np.sqrt(len(x))
assert_stat_op_calc(
"nunique",
nunique,
float_frame_with_na,
has_skipna=False,
check_dtype=False,
check_dates=True,
)
# GH#32571: rol needed for flaky CI builds
# mixed types (with upcasting happening)
assert_stat_op_calc(
"sum",
np.sum,
mixed_float_frame.astype("float32"),
check_dtype=False,
rtol=1e-3,
)
assert_stat_op_calc(
"sum", np.sum, float_frame_with_na, skipna_alternative=np.nansum
)
assert_stat_op_calc("mean", np.mean, float_frame_with_na, check_dates=True)
assert_stat_op_calc(
"product", np.prod, float_frame_with_na, skipna_alternative=np.nanprod
)
assert_stat_op_calc("var", var, float_frame_with_na)
assert_stat_op_calc("std", std, float_frame_with_na)
assert_stat_op_calc("sem", sem, float_frame_with_na)
assert_stat_op_calc(
"count",
count,
float_frame_with_na,
has_skipna=False,
check_dtype=False,
check_dates=True,
)
def test_stat_op_calc_skew_kurtosis(self, float_frame_with_na):
sp_stats = pytest.importorskip("scipy.stats")
def skewness(x):
if len(x) < 3:
return np.nan
return sp_stats.skew(x, bias=False)
def kurt(x):
if len(x) < 4:
return np.nan
return sp_stats.kurtosis(x, bias=False)
assert_stat_op_calc("skew", skewness, float_frame_with_na)
assert_stat_op_calc("kurt", kurt, float_frame_with_na)
def test_median(self, float_frame_with_na, int_frame):
def wrapper(x):
if isna(x).any():
return np.nan
return np.median(x)
assert_stat_op_calc("median", wrapper, float_frame_with_na, check_dates=True)
assert_stat_op_calc(
"median", wrapper, int_frame, check_dtype=False, check_dates=True
)
@pytest.mark.parametrize(
"method", ["sum", "mean", "prod", "var", "std", "skew", "min", "max"]
)
@pytest.mark.parametrize(
"df",
[
DataFrame(
{
"a": [
-0.00049987540199591344,
-0.0016467257772919831,
0.00067695870775883013,
],
"b": [-0, -0, 0.0],
"c": [
0.00031111847529610595,
0.0014902627951905339,
-0.00094099200035979691,
],
},
index=["foo", "bar", "baz"],
dtype="O",
),
DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object),
],
)
@pytest.mark.filterwarnings("ignore:Mismatched null-like values:FutureWarning")
def test_stat_operators_attempt_obj_array(self, method, df, axis):
# GH#676
assert df.values.dtype == np.object_
result = getattr(df, method)(axis=axis)
expected = getattr(df.astype("f8"), method)(axis=axis).astype(object)
if axis in [1, "columns"] and method in ["min", "max"]:
expected[expected.isna()] = None
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"])
def test_mixed_ops(self, op):
# GH#16116
df = DataFrame(
{
"int": [1, 2, 3, 4],
"float": [1.0, 2.0, 3.0, 4.0],
"str": ["a", "b", "c", "d"],
}
)
msg = "|".join(
[
"Could not convert",
"could not convert",
"can't multiply sequence by non-int",
"does not support",
]
)
with pytest.raises(TypeError, match=msg):
getattr(df, op)()
with pd.option_context("use_bottleneck", False):
msg = "|".join(
[
"Could not convert",
"could not convert",
"can't multiply sequence by non-int",
"does not support",
]
)
with pytest.raises(TypeError, match=msg):
getattr(df, op)()
@pytest.mark.xfail(
using_pyarrow_string_dtype(), reason="sum doesn't work for arrow strings"
)
def test_reduce_mixed_frame(self):
# GH 6806
df = DataFrame(
{
"bool_data": [True, True, False, False, False],
"int_data": [10, 20, 30, 40, 50],
"string_data": ["a", "b", "c", "d", "e"],
}
)
df.reindex(columns=["bool_data", "int_data", "string_data"])
test = df.sum(axis=0)
tm.assert_numpy_array_equal(
test.values, np.array([2, 150, "abcde"], dtype=object)
)
alt = df.T.sum(axis=1)
tm.assert_series_equal(test, alt)
def test_nunique(self):
df = DataFrame({"A": [1, 1, 1], "B": [1, 2, 3], "C": [1, np.nan, 3]})
tm.assert_series_equal(df.nunique(), Series({"A": 1, "B": 3, "C": 2}))
tm.assert_series_equal(
df.nunique(dropna=False), Series({"A": 1, "B": 3, "C": 3})
)
tm.assert_series_equal(df.nunique(axis=1), Series({0: 1, 1: 2, 2: 2}))
tm.assert_series_equal(
df.nunique(axis=1, dropna=False), Series({0: 1, 1: 3, 2: 2})
)
@pytest.mark.parametrize("tz", [None, "UTC"])
def test_mean_mixed_datetime_numeric(self, tz):
# https://github.com/pandas-dev/pandas/issues/24752
df = DataFrame({"A": [1, 1], "B": [Timestamp("2000", tz=tz)] * 2})
result = df.mean()
expected = Series([1.0, Timestamp("2000", tz=tz)], index=["A", "B"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("tz", [None, "UTC"])
def test_mean_includes_datetimes(self, tz):
# https://github.com/pandas-dev/pandas/issues/24752
# Behavior in 0.24.0rc1 was buggy.
# As of 2.0 with numeric_only=None we do *not* drop datetime columns
df = DataFrame({"A": [Timestamp("2000", tz=tz)] * 2})
result = df.mean()
expected = Series([Timestamp("2000", tz=tz)], index=["A"])
tm.assert_series_equal(result, expected)
def test_mean_mixed_string_decimal(self):
# GH 11670
# possible bug when calculating mean of DataFrame?
d = [
{"A": 2, "B": None, "C": Decimal("628.00")},
{"A": 1, "B": None, "C": Decimal("383.00")},
{"A": 3, "B": None, "C": Decimal("651.00")},
{"A": 2, "B": None, "C": Decimal("575.00")},
{"A": 4, "B": None, "C": Decimal("1114.00")},
{"A": 1, "B": "TEST", "C": Decimal("241.00")},
{"A": 2, "B": None, "C": Decimal("572.00")},
{"A": 4, "B": None, "C": Decimal("609.00")},
{"A": 3, "B": None, "C": Decimal("820.00")},
{"A": 5, "B": None, "C": Decimal("1223.00")},
]
df = DataFrame(d)
with pytest.raises(
TypeError, match="unsupported operand type|does not support"
):
df.mean()
result = df[["A", "C"]].mean()
expected = Series([2.7, 681.6], index=["A", "C"], dtype=object)
tm.assert_series_equal(result, expected)
def test_var_std(self, datetime_frame):
result = datetime_frame.std(ddof=4)
expected = datetime_frame.apply(lambda x: x.std(ddof=4))
tm.assert_almost_equal(result, expected)
result = datetime_frame.var(ddof=4)
expected = datetime_frame.apply(lambda x: x.var(ddof=4))
tm.assert_almost_equal(result, expected)
arr = np.repeat(np.random.default_rng(2).random((1, 1000)), 1000, 0)
result = nanops.nanvar(arr, axis=0)
assert not (result < 0).any()
with pd.option_context("use_bottleneck", False):
result = nanops.nanvar(arr, axis=0)
assert not (result < 0).any()
@pytest.mark.parametrize("meth", ["sem", "var", "std"])
def test_numeric_only_flag(self, meth):
# GH 9201
df1 = DataFrame(
np.random.default_rng(2).standard_normal((5, 3)),
columns=["foo", "bar", "baz"],
)
# Cast to object to avoid implicit cast when setting entry to "100" below
df1 = df1.astype({"foo": object})
# set one entry to a number in str format
df1.loc[0, "foo"] = "100"
df2 = DataFrame(
np.random.default_rng(2).standard_normal((5, 3)),
columns=["foo", "bar", "baz"],
)
# Cast to object to avoid implicit cast when setting entry to "a" below
df2 = df2.astype({"foo": object})
# set one entry to a non-number str
df2.loc[0, "foo"] = "a"
result = getattr(df1, meth)(axis=1, numeric_only=True)
expected = getattr(df1[["bar", "baz"]], meth)(axis=1)
tm.assert_series_equal(expected, result)
result = getattr(df2, meth)(axis=1, numeric_only=True)
expected = getattr(df2[["bar", "baz"]], meth)(axis=1)
tm.assert_series_equal(expected, result)
# df1 has all numbers, df2 has a letter inside
msg = r"unsupported operand type\(s\) for -: 'float' and 'str'"
with pytest.raises(TypeError, match=msg):
getattr(df1, meth)(axis=1, numeric_only=False)
msg = "could not convert string to float: 'a'"
with pytest.raises(TypeError, match=msg):
getattr(df2, meth)(axis=1, numeric_only=False)
def test_sem(self, datetime_frame):
result = datetime_frame.sem(ddof=4)
expected = datetime_frame.apply(lambda x: x.std(ddof=4) / np.sqrt(len(x)))
tm.assert_almost_equal(result, expected)
arr = np.repeat(np.random.default_rng(2).random((1, 1000)), 1000, 0)
result = nanops.nansem(arr, axis=0)
assert not (result < 0).any()
with pd.option_context("use_bottleneck", False):
result = nanops.nansem(arr, axis=0)
assert not (result < 0).any()
@pytest.mark.parametrize(
"dropna, expected",
[
(
True,
{
"A": [12],
"B": [10.0],
"C": [1.0],
"D": ["a"],
"E": Categorical(["a"], categories=["a"]),
"F": DatetimeIndex(["2000-01-02"], dtype="M8[ns]"),
"G": to_timedelta(["1 days"]),
},
),
(
False,
{
"A": [12],
"B": [10.0],
"C": [np.nan],
"D": np.array([np.nan], dtype=object),
"E": Categorical([np.nan], categories=["a"]),
"F": DatetimeIndex([pd.NaT], dtype="M8[ns]"),
"G": to_timedelta([pd.NaT]),
},
),
(
True,
{
"H": [8, 9, np.nan, np.nan],
"I": [8, 9, np.nan, np.nan],
"J": [1, np.nan, np.nan, np.nan],
"K": Categorical(["a", np.nan, np.nan, np.nan], categories=["a"]),
"L": DatetimeIndex(
["2000-01-02", "NaT", "NaT", "NaT"], dtype="M8[ns]"
),
"M": to_timedelta(["1 days", "nan", "nan", "nan"]),
"N": [0, 1, 2, 3],
},
),
(
False,
{
"H": [8, 9, np.nan, np.nan],
"I": [8, 9, np.nan, np.nan],
"J": [1, np.nan, np.nan, np.nan],
"K": Categorical([np.nan, "a", np.nan, np.nan], categories=["a"]),
"L": DatetimeIndex(
["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"
),
"M": to_timedelta(["nan", "1 days", "nan", "nan"]),
"N": [0, 1, 2, 3],
},
),
],
)
def test_mode_dropna(self, dropna, expected):
df = DataFrame(
{
"A": [12, 12, 19, 11],
"B": [10, 10, np.nan, 3],
"C": [1, np.nan, np.nan, np.nan],
"D": Series([np.nan, np.nan, "a", np.nan], dtype=object),
"E": Categorical([np.nan, np.nan, "a", np.nan]),
"F": DatetimeIndex(["NaT", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"),
"G": to_timedelta(["1 days", "nan", "nan", "nan"]),
"H": [8, 8, 9, 9],
"I": [9, 9, 8, 8],
"J": [1, 1, np.nan, np.nan],
"K": Categorical(["a", np.nan, "a", np.nan]),
"L": DatetimeIndex(
["2000-01-02", "2000-01-02", "NaT", "NaT"], dtype="M8[ns]"
),
"M": to_timedelta(["1 days", "nan", "1 days", "nan"]),
"N": np.arange(4, dtype="int64"),
}
)
result = df[sorted(expected.keys())].mode(dropna=dropna)
expected = DataFrame(expected)
tm.assert_frame_equal(result, expected)
def test_mode_sortwarning(self, using_infer_string):
# Check for the warning that is raised when the mode
# results cannot be sorted
df = DataFrame({"A": [np.nan, np.nan, "a", "a"]})
expected = DataFrame({"A": ["a", np.nan]})
warning = None if using_infer_string else UserWarning
with tm.assert_produces_warning(warning):
result = df.mode(dropna=False)
result = result.sort_values(by="A").reset_index(drop=True)
tm.assert_frame_equal(result, expected)
def test_mode_empty_df(self):
df = DataFrame([], columns=["a", "b"])
result = df.mode()
expected = DataFrame([], columns=["a", "b"], index=Index([], dtype=np.int64))
tm.assert_frame_equal(result, expected)
def test_operators_timedelta64(self):
df = DataFrame(
{
"A": date_range("2012-1-1", periods=3, freq="D"),
"B": date_range("2012-1-2", periods=3, freq="D"),
"C": Timestamp("20120101") - timedelta(minutes=5, seconds=5),
}
)
diffs = DataFrame({"A": df["A"] - df["C"], "B": df["A"] - df["B"]})
# min
result = diffs.min()
assert result.iloc[0] == diffs.loc[0, "A"]
assert result.iloc[1] == diffs.loc[0, "B"]
result = diffs.min(axis=1)
assert (result == diffs.loc[0, "B"]).all()
# max
result = diffs.max()
assert result.iloc[0] == diffs.loc[2, "A"]
assert result.iloc[1] == diffs.loc[2, "B"]
result = diffs.max(axis=1)
assert (result == diffs["A"]).all()
# abs
result = diffs.abs()
result2 = abs(diffs)
expected = DataFrame({"A": df["A"] - df["C"], "B": df["B"] - df["A"]})
tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result2, expected)
# mixed frame
mixed = diffs.copy()
mixed["C"] = "foo"
mixed["D"] = 1
mixed["E"] = 1.0
mixed["F"] = Timestamp("20130101")
# results in an object array
result = mixed.min()
expected = Series(
[
pd.Timedelta(timedelta(seconds=5 * 60 + 5)),
pd.Timedelta(timedelta(days=-1)),
"foo",
1,
1.0,
Timestamp("20130101"),
],
index=mixed.columns,
)
tm.assert_series_equal(result, expected)
# excludes non-numeric
result = mixed.min(axis=1, numeric_only=True)
expected = Series([1, 1, 1.0], index=[0, 1, 2])
tm.assert_series_equal(result, expected)
# works when only those columns are selected
result = mixed[["A", "B"]].min(1)
expected = Series([timedelta(days=-1)] * 3)
tm.assert_series_equal(result, expected)
result = mixed[["A", "B"]].min()
expected = Series(
[timedelta(seconds=5 * 60 + 5), timedelta(days=-1)], index=["A", "B"]
)
tm.assert_series_equal(result, expected)
# GH 3106
df = DataFrame(
{
"time": date_range("20130102", periods=5),
"time2": date_range("20130105", periods=5),
}
)
df["off1"] = df["time2"] - df["time"]
assert df["off1"].dtype == "timedelta64[ns]"
df["off2"] = df["time"] - df["time2"]
df._consolidate_inplace()
assert df["off1"].dtype == "timedelta64[ns]"
assert df["off2"].dtype == "timedelta64[ns]"
def test_std_timedelta64_skipna_false(self):
# GH#37392
tdi = pd.timedelta_range("1 Day", periods=10)
df = DataFrame({"A": tdi, "B": tdi}, copy=True)
df.iloc[-2, -1] = pd.NaT
result = df.std(skipna=False)
expected = Series(
[df["A"].std(), pd.NaT], index=["A", "B"], dtype="timedelta64[ns]"
)
tm.assert_series_equal(result, expected)
result = df.std(axis=1, skipna=False)
expected = Series([pd.Timedelta(0)] * 8 + [pd.NaT, pd.Timedelta(0)])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"values", [["2022-01-01", "2022-01-02", pd.NaT, "2022-01-03"], 4 * [pd.NaT]]
)
def test_std_datetime64_with_nat(
self, values, skipna, using_array_manager, request, unit
):
# GH#51335
if using_array_manager and (
not skipna or all(value is pd.NaT for value in values)
):
mark = pytest.mark.xfail(
reason="GH#51446: Incorrect type inference on NaT in reduction result"
)
request.applymarker(mark)
dti = to_datetime(values).as_unit(unit)
df = DataFrame({"a": dti})
result = df.std(skipna=skipna)
if not skipna or all(value is pd.NaT for value in values):
expected = Series({"a": pd.NaT}, dtype=f"timedelta64[{unit}]")
else:
# 86400000000000ns == 1 day
expected = Series({"a": 86400000000000}, dtype=f"timedelta64[{unit}]")
tm.assert_series_equal(result, expected)
def test_sum_corner(self):
empty_frame = DataFrame()
axis0 = empty_frame.sum(0)
axis1 = empty_frame.sum(1)
assert isinstance(axis0, Series)
assert isinstance(axis1, Series)
assert len(axis0) == 0
assert len(axis1) == 0
@pytest.mark.parametrize(
"index",
[
RangeIndex(0),
DatetimeIndex([]),
Index([], dtype=np.int64),
Index([], dtype=np.float64),
DatetimeIndex([], freq="ME"),
PeriodIndex([], freq="D"),
],
)
def test_axis_1_empty(self, all_reductions, index):
df = DataFrame(columns=["a"], index=index)
result = getattr(df, all_reductions)(axis=1)
if all_reductions in ("any", "all"):
expected_dtype = "bool"
elif all_reductions == "count":
expected_dtype = "int64"
else:
expected_dtype = "object"
expected = Series([], index=index, dtype=expected_dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)])
@pytest.mark.parametrize("numeric_only", [None, True, False])
def test_sum_prod_nanops(self, method, unit, numeric_only):
idx = ["a", "b", "c"]
df = DataFrame({"a": [unit, unit], "b": [unit, np.nan], "c": [np.nan, np.nan]})
# The default
result = getattr(df, method)(numeric_only=numeric_only)
expected = Series([unit, unit, unit], index=idx, dtype="float64")
tm.assert_series_equal(result, expected)
# min_count=1
result = getattr(df, method)(numeric_only=numeric_only, min_count=1)
expected = Series([unit, unit, np.nan], index=idx)
tm.assert_series_equal(result, expected)
# min_count=0
result = getattr(df, method)(numeric_only=numeric_only, min_count=0)
expected = Series([unit, unit, unit], index=idx, dtype="float64")
tm.assert_series_equal(result, expected)
result = getattr(df.iloc[1:], method)(numeric_only=numeric_only, min_count=1)
expected = Series([unit, np.nan, np.nan], index=idx)
tm.assert_series_equal(result, expected)
# min_count > 1
df = DataFrame({"A": [unit] * 10, "B": [unit] * 5 + [np.nan] * 5})
result = getattr(df, method)(numeric_only=numeric_only, min_count=5)
expected = Series(result, index=["A", "B"])
tm.assert_series_equal(result, expected)
result = getattr(df, method)(numeric_only=numeric_only, min_count=6)
expected = Series(result, index=["A", "B"])
tm.assert_series_equal(result, expected)
def test_sum_nanops_timedelta(self):
# prod isn't defined on timedeltas
idx = ["a", "b", "c"]
df = DataFrame({"a": [0, 0], "b": [0, np.nan], "c": [np.nan, np.nan]})
df2 = df.apply(to_timedelta)
# 0 by default
result = df2.sum()
expected = Series([0, 0, 0], dtype="m8[ns]", index=idx)
tm.assert_series_equal(result, expected)
# min_count=0
result = df2.sum(min_count=0)
tm.assert_series_equal(result, expected)
# min_count=1
result = df2.sum(min_count=1)
expected = Series([0, 0, np.nan], dtype="m8[ns]", index=idx)
tm.assert_series_equal(result, expected)
def test_sum_nanops_min_count(self):
# https://github.com/pandas-dev/pandas/issues/39738
df = DataFrame({"x": [1, 2, 3], "y": [4, 5, 6]})
result = df.sum(min_count=10)
expected = Series([np.nan, np.nan], index=["x", "y"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("float_type", ["float16", "float32", "float64"])
@pytest.mark.parametrize(
"kwargs, expected_result",
[
({"axis": 1, "min_count": 2}, [3.2, 5.3, np.nan]),
({"axis": 1, "min_count": 3}, [np.nan, np.nan, np.nan]),
({"axis": 1, "skipna": False}, [3.2, 5.3, np.nan]),
],
)
def test_sum_nanops_dtype_min_count(self, float_type, kwargs, expected_result):
# GH#46947
df = DataFrame({"a": [1.0, 2.3, 4.4], "b": [2.2, 3, np.nan]}, dtype=float_type)
result = df.sum(**kwargs)
expected = Series(expected_result).astype(float_type)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("float_type", ["float16", "float32", "float64"])
@pytest.mark.parametrize(
"kwargs, expected_result",
[
({"axis": 1, "min_count": 2}, [2.0, 4.0, np.nan]),
({"axis": 1, "min_count": 3}, [np.nan, np.nan, np.nan]),
({"axis": 1, "skipna": False}, [2.0, 4.0, np.nan]),
],
)
def test_prod_nanops_dtype_min_count(self, float_type, kwargs, expected_result):
# GH#46947
df = DataFrame(
{"a": [1.0, 2.0, 4.4], "b": [2.0, 2.0, np.nan]}, dtype=float_type
)
result = df.prod(**kwargs)
expected = Series(expected_result).astype(float_type)
tm.assert_series_equal(result, expected)
def test_sum_object(self, float_frame):
values = float_frame.values.astype(int)
frame = DataFrame(values, index=float_frame.index, columns=float_frame.columns)
deltas = frame * timedelta(1)
deltas.sum()
def test_sum_bool(self, float_frame):
# ensure this works, bug report
bools = np.isnan(float_frame)
bools.sum(1)
bools.sum(0)
def test_sum_mixed_datetime(self):
# GH#30886
df = DataFrame({"A": date_range("2000", periods=4), "B": [1, 2, 3, 4]}).reindex(
[2, 3, 4]
)
with pytest.raises(TypeError, match="does not support reduction 'sum'"):
df.sum()
def test_mean_corner(self, float_frame, float_string_frame):
# unit test when have object data
msg = "Could not convert|does not support"
with pytest.raises(TypeError, match=msg):
float_string_frame.mean(axis=0)
# xs sum mixed type, just want to know it works...
with pytest.raises(TypeError, match="unsupported operand type"):
float_string_frame.mean(axis=1)
# take mean of boolean column
float_frame["bool"] = float_frame["A"] > 0
means = float_frame.mean(0)
assert means["bool"] == float_frame["bool"].values.mean()
def test_mean_datetimelike(self):
# GH#24757 check that datetimelike are excluded by default, handled
# correctly with numeric_only=True
# As of 2.0, datetimelike are *not* excluded with numeric_only=None
df = DataFrame(
{
"A": np.arange(3),
"B": date_range("2016-01-01", periods=3),
"C": pd.timedelta_range("1D", periods=3),
"D": pd.period_range("2016", periods=3, freq="Y"),
}
)
result = df.mean(numeric_only=True)
expected = Series({"A": 1.0})
tm.assert_series_equal(result, expected)
with pytest.raises(TypeError, match="mean is not implemented for PeriodArray"):
df.mean()
def test_mean_datetimelike_numeric_only_false(self):
df = DataFrame(
{
"A": np.arange(3),
"B": date_range("2016-01-01", periods=3),
"C": pd.timedelta_range("1D", periods=3),
}
)
# datetime(tz) and timedelta work
result = df.mean(numeric_only=False)
expected = Series({"A": 1, "B": df.loc[1, "B"], "C": df.loc[1, "C"]})
tm.assert_series_equal(result, expected)
# mean of period is not allowed
df["D"] = pd.period_range("2016", periods=3, freq="Y")
with pytest.raises(TypeError, match="mean is not implemented for Period"):
df.mean(numeric_only=False)
def test_mean_extensionarray_numeric_only_true(self):
# https://github.com/pandas-dev/pandas/issues/33256
arr = np.random.default_rng(2).integers(1000, size=(10, 5))
df = DataFrame(arr, dtype="Int64")
result = df.mean(numeric_only=True)
expected = DataFrame(arr).mean().astype("Float64")
tm.assert_series_equal(result, expected)
def test_stats_mixed_type(self, float_string_frame):
with pytest.raises(TypeError, match="could not convert"):
float_string_frame.std(1)
with pytest.raises(TypeError, match="could not convert"):
float_string_frame.var(1)
with pytest.raises(TypeError, match="unsupported operand type"):
float_string_frame.mean(1)
with pytest.raises(TypeError, match="could not convert"):
float_string_frame.skew(1)
def test_sum_bools(self):
df = DataFrame(index=range(1), columns=range(10))
bools = isna(df)
assert bools.sum(axis=1)[0] == 10
# ----------------------------------------------------------------------
# Index of max / min
@pytest.mark.parametrize("skipna", [True, False])
@pytest.mark.parametrize("axis", [0, 1])
def test_idxmin(self, float_frame, int_frame, skipna, axis):
frame = float_frame
frame.iloc[5:10] = np.nan
frame.iloc[15:20, -2:] = np.nan
for df in [frame, int_frame]:
warn = None
if skipna is False or axis == 1:
warn = None if df is int_frame else FutureWarning
msg = "The behavior of DataFrame.idxmin with all-NA values"
with tm.assert_produces_warning(warn, match=msg):
result = df.idxmin(axis=axis, skipna=skipna)
msg2 = "The behavior of Series.idxmin"
with tm.assert_produces_warning(warn, match=msg2):
expected = df.apply(Series.idxmin, axis=axis, skipna=skipna)
expected = expected.astype(df.index.dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
def test_idxmin_empty(self, index, skipna, axis):
# GH53265
if axis == 0:
frame = DataFrame(index=index)
else:
frame = DataFrame(columns=index)
result = frame.idxmin(axis=axis, skipna=skipna)
expected = Series(dtype=index.dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("numeric_only", [True, False])
def test_idxmin_numeric_only(self, numeric_only):
df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1], "c": list("xyx")})
result = df.idxmin(numeric_only=numeric_only)
if numeric_only:
expected = Series([2, 1], index=["a", "b"])
else:
expected = Series([2, 1, 0], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
def test_idxmin_axis_2(self, float_frame):
frame = float_frame
msg = "No axis named 2 for object type DataFrame"
with pytest.raises(ValueError, match=msg):
frame.idxmin(axis=2)
@pytest.mark.parametrize("skipna", [True, False])
@pytest.mark.parametrize("axis", [0, 1])
def test_idxmax(self, float_frame, int_frame, skipna, axis):
frame = float_frame
frame.iloc[5:10] = np.nan
frame.iloc[15:20, -2:] = np.nan
for df in [frame, int_frame]:
warn = None
if skipna is False or axis == 1:
warn = None if df is int_frame else FutureWarning
msg = "The behavior of DataFrame.idxmax with all-NA values"
with tm.assert_produces_warning(warn, match=msg):
result = df.idxmax(axis=axis, skipna=skipna)
msg2 = "The behavior of Series.idxmax"
with tm.assert_produces_warning(warn, match=msg2):
expected = df.apply(Series.idxmax, axis=axis, skipna=skipna)
expected = expected.astype(df.index.dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
def test_idxmax_empty(self, index, skipna, axis):
# GH53265
if axis == 0:
frame = DataFrame(index=index)
else:
frame = DataFrame(columns=index)
result = frame.idxmax(axis=axis, skipna=skipna)
expected = Series(dtype=index.dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("numeric_only", [True, False])
def test_idxmax_numeric_only(self, numeric_only):
df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1], "c": list("xyx")})
result = df.idxmax(numeric_only=numeric_only)
if numeric_only:
expected = Series([1, 0], index=["a", "b"])
else:
expected = Series([1, 0, 1], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
def test_idxmax_arrow_types(self):
# GH#55368
pytest.importorskip("pyarrow")
df = DataFrame({"a": [2, 3, 1], "b": [2, 1, 1]}, dtype="int64[pyarrow]")
result = df.idxmax()
expected = Series([1, 0], index=["a", "b"])
tm.assert_series_equal(result, expected)
result = df.idxmin()
expected = Series([2, 1], index=["a", "b"])
tm.assert_series_equal(result, expected)
df = DataFrame({"a": ["b", "c", "a"]}, dtype="string[pyarrow]")
result = df.idxmax(numeric_only=False)
expected = Series([1], index=["a"])
tm.assert_series_equal(result, expected)
result = df.idxmin(numeric_only=False)
expected = Series([2], index=["a"])
tm.assert_series_equal(result, expected)
def test_idxmax_axis_2(self, float_frame):
frame = float_frame
msg = "No axis named 2 for object type DataFrame"
with pytest.raises(ValueError, match=msg):
frame.idxmax(axis=2)
def test_idxmax_mixed_dtype(self):
# don't cast to object, which would raise in nanops
dti = date_range("2016-01-01", periods=3)
# Copying dti is needed for ArrayManager otherwise when we set
# df.loc[0, 3] = pd.NaT below it edits dti
df = DataFrame({1: [0, 2, 1], 2: range(3)[::-1], 3: dti.copy(deep=True)})
result = df.idxmax()
expected = Series([1, 0, 2], index=[1, 2, 3])
tm.assert_series_equal(result, expected)
result = df.idxmin()
expected = Series([0, 2, 0], index=[1, 2, 3])
tm.assert_series_equal(result, expected)
# with NaTs
df.loc[0, 3] = pd.NaT
result = df.idxmax()
expected = Series([1, 0, 2], index=[1, 2, 3])
tm.assert_series_equal(result, expected)
result = df.idxmin()
expected = Series([0, 2, 1], index=[1, 2, 3])
tm.assert_series_equal(result, expected)
# with multi-column dt64 block
df[4] = dti[::-1]
df._consolidate_inplace()
result = df.idxmax()
expected = Series([1, 0, 2, 0], index=[1, 2, 3, 4])
tm.assert_series_equal(result, expected)
result = df.idxmin()
expected = Series([0, 2, 1, 2], index=[1, 2, 3, 4])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"op, expected_value",
[("idxmax", [0, 4]), ("idxmin", [0, 5])],
)
def test_idxmax_idxmin_convert_dtypes(self, op, expected_value):
# GH 40346
df = DataFrame(
{
"ID": [100, 100, 100, 200, 200, 200],
"value": [0, 0, 0, 1, 2, 0],
},
dtype="Int64",
)
df = df.groupby("ID")
result = getattr(df, op)()
expected = DataFrame(
{"value": expected_value},
index=Index([100, 200], name="ID", dtype="Int64"),
)
tm.assert_frame_equal(result, expected)
def test_idxmax_dt64_multicolumn_axis1(self):
dti = date_range("2016-01-01", periods=3)
df = DataFrame({3: dti, 4: dti[::-1]}, copy=True)
df.iloc[0, 0] = pd.NaT
df._consolidate_inplace()
result = df.idxmax(axis=1)
expected = Series([4, 3, 3])
tm.assert_series_equal(result, expected)
result = df.idxmin(axis=1)
expected = Series([4, 3, 4])
tm.assert_series_equal(result, expected)
# ----------------------------------------------------------------------
# Logical reductions
@pytest.mark.parametrize("opname", ["any", "all"])
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize("bool_only", [False, True])
def test_any_all_mixed_float(self, opname, axis, bool_only, float_string_frame):
# make sure op works on mixed-type frame
mixed = float_string_frame
mixed["_bool_"] = np.random.default_rng(2).standard_normal(len(mixed)) > 0.5
getattr(mixed, opname)(axis=axis, bool_only=bool_only)
@pytest.mark.parametrize("opname", ["any", "all"])
@pytest.mark.parametrize("axis", [0, 1])
def test_any_all_bool_with_na(self, opname, axis, bool_frame_with_na):
getattr(bool_frame_with_na, opname)(axis=axis, bool_only=False)
@pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:FutureWarning")
@pytest.mark.parametrize("opname", ["any", "all"])
def test_any_all_bool_frame(self, opname, bool_frame_with_na):
# GH#12863: numpy gives back non-boolean data for object type
# so fill NaNs to compare with pandas behavior
frame = bool_frame_with_na.fillna(True)
alternative = getattr(np, opname)
f = getattr(frame, opname)
def skipna_wrapper(x):
nona = x.dropna().values
return alternative(nona)
def wrapper(x):
return alternative(x.values)
result0 = f(axis=0, skipna=False)
result1 = f(axis=1, skipna=False)
tm.assert_series_equal(result0, frame.apply(wrapper))
tm.assert_series_equal(result1, frame.apply(wrapper, axis=1))
result0 = f(axis=0)
result1 = f(axis=1)
tm.assert_series_equal(result0, frame.apply(skipna_wrapper))
tm.assert_series_equal(
result1, frame.apply(skipna_wrapper, axis=1), check_dtype=False
)
# bad axis
with pytest.raises(ValueError, match="No axis named 2"):
f(axis=2)
# all NA case
all_na = frame * np.nan
r0 = getattr(all_na, opname)(axis=0)
r1 = getattr(all_na, opname)(axis=1)
if opname == "any":
assert not r0.any()
assert not r1.any()
else:
assert r0.all()
assert r1.all()
def test_any_all_extra(self):
df = DataFrame(
{
"A": [True, False, False],
"B": [True, True, False],
"C": [True, True, True],
},
index=["a", "b", "c"],
)
result = df[["A", "B"]].any(axis=1)
expected = Series([True, True, False], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
result = df[["A", "B"]].any(axis=1, bool_only=True)
tm.assert_series_equal(result, expected)
result = df.all(1)
expected = Series([True, False, False], index=["a", "b", "c"])
tm.assert_series_equal(result, expected)
result = df.all(1, bool_only=True)
tm.assert_series_equal(result, expected)
# Axis is None
result = df.all(axis=None).item()
assert result is False
result = df.any(axis=None).item()
assert result is True
result = df[["C"]].all(axis=None).item()
assert result is True
@pytest.mark.parametrize("axis", [0, 1])
@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
@pytest.mark.parametrize("skipna", [True, False])
def test_any_all_object_dtype(
self, axis, bool_agg_func, skipna, using_infer_string
):
# GH#35450
df = DataFrame(
data=[
[1, np.nan, np.nan, True],
[np.nan, 2, np.nan, True],
[np.nan, np.nan, np.nan, True],
[np.nan, np.nan, "5", np.nan],
]
)
if using_infer_string:
# na in object is True while in string pyarrow numpy it's false
val = not axis == 0 and not skipna and bool_agg_func == "all"
else:
val = True
result = getattr(df, bool_agg_func)(axis=axis, skipna=skipna)
expected = Series([True, True, val, True])
tm.assert_series_equal(result, expected)
# GH#50947 deprecates this but it is not emitting a warning in some builds.
@pytest.mark.filterwarnings(
"ignore:'any' with datetime64 dtypes is deprecated.*:FutureWarning"
)
def test_any_datetime(self):
# GH 23070
float_data = [1, np.nan, 3, np.nan]
datetime_data = [
Timestamp("1960-02-15"),
Timestamp("1960-02-16"),
pd.NaT,
pd.NaT,
]
df = DataFrame({"A": float_data, "B": datetime_data})
result = df.any(axis=1)
expected = Series([True, True, True, False])
tm.assert_series_equal(result, expected)
def test_any_all_bool_only(self):
# GH 25101
df = DataFrame(
{"col1": [1, 2, 3], "col2": [4, 5, 6], "col3": [None, None, None]},
columns=Index(["col1", "col2", "col3"], dtype=object),
)
result = df.all(bool_only=True)
expected = Series(dtype=np.bool_, index=[])
tm.assert_series_equal(result, expected)
df = DataFrame(
{
"col1": [1, 2, 3],
"col2": [4, 5, 6],
"col3": [None, None, None],
"col4": [False, False, True],
}
)
result = df.all(bool_only=True)
expected = Series({"col4": False})
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"func, data, expected",
[
(np.any, {}, False),
(np.all, {}, True),
(np.any, {"A": []}, False),
(np.all, {"A": []}, True),
(np.any, {"A": [False, False]}, False),
(np.all, {"A": [False, False]}, False),
(np.any, {"A": [True, False]}, True),
(np.all, {"A": [True, False]}, False),
(np.any, {"A": [True, True]}, True),
(np.all, {"A": [True, True]}, True),
(np.any, {"A": [False], "B": [False]}, False),
(np.all, {"A": [False], "B": [False]}, False),
(np.any, {"A": [False, False], "B": [False, True]}, True),
(np.all, {"A": [False, False], "B": [False, True]}, False),
# other types
(np.all, {"A": Series([0.0, 1.0], dtype="float")}, False),
(np.any, {"A": Series([0.0, 1.0], dtype="float")}, True),
(np.all, {"A": Series([0, 1], dtype=int)}, False),
(np.any, {"A": Series([0, 1], dtype=int)}, True),
pytest.param(np.all, {"A": Series([0, 1], dtype="M8[ns]")}, False),
pytest.param(np.all, {"A": Series([0, 1], dtype="M8[ns, UTC]")}, False),
pytest.param(np.any, {"A": Series([0, 1], dtype="M8[ns]")}, True),
pytest.param(np.any, {"A": Series([0, 1], dtype="M8[ns, UTC]")}, True),
pytest.param(np.all, {"A": Series([1, 2], dtype="M8[ns]")}, True),
pytest.param(np.all, {"A": Series([1, 2], dtype="M8[ns, UTC]")}, True),
pytest.param(np.any, {"A": Series([1, 2], dtype="M8[ns]")}, True),
pytest.param(np.any, {"A": Series([1, 2], dtype="M8[ns, UTC]")}, True),
pytest.param(np.all, {"A": Series([0, 1], dtype="m8[ns]")}, False),
pytest.param(np.any, {"A": Series([0, 1], dtype="m8[ns]")}, True),
pytest.param(np.all, {"A": Series([1, 2], dtype="m8[ns]")}, True),
pytest.param(np.any, {"A": Series([1, 2], dtype="m8[ns]")}, True),
# np.all on Categorical raises, so the reduction drops the
# column, so all is being done on an empty Series, so is True
(np.all, {"A": Series([0, 1], dtype="category")}, True),
(np.any, {"A": Series([0, 1], dtype="category")}, False),
(np.all, {"A": Series([1, 2], dtype="category")}, True),
(np.any, {"A": Series([1, 2], dtype="category")}, False),
# Mix GH#21484
pytest.param(
np.all,
{
"A": Series([10, 20], dtype="M8[ns]"),
"B": Series([10, 20], dtype="m8[ns]"),
},
True,
),
],
)
def test_any_all_np_func(self, func, data, expected):
# GH 19976
data = DataFrame(data)
if any(isinstance(x, CategoricalDtype) for x in data.dtypes):
with pytest.raises(
TypeError, match="dtype category does not support reduction"
):
func(data)
# method version
with pytest.raises(
TypeError, match="dtype category does not support reduction"
):
getattr(DataFrame(data), func.__name__)(axis=None)
else:
msg = "'(any|all)' with datetime64 dtypes is deprecated"
if data.dtypes.apply(lambda x: x.kind == "M").any():
warn = FutureWarning
else:
warn = None
with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False):
# GH#34479
result = func(data)
assert isinstance(result, np.bool_)
assert result.item() is expected
# method version
with tm.assert_produces_warning(warn, match=msg):
# GH#34479
result = getattr(DataFrame(data), func.__name__)(axis=None)
assert isinstance(result, np.bool_)
assert result.item() is expected
def test_any_all_object(self):
# GH 19976
result = np.all(DataFrame(columns=["a", "b"])).item()
assert result is True
result = np.any(DataFrame(columns=["a", "b"])).item()
assert result is False
def test_any_all_object_bool_only(self):
df = DataFrame({"A": ["foo", 2], "B": [True, False]}).astype(object)
df._consolidate_inplace()
df["C"] = Series([True, True])
# Categorical of bools is _not_ considered booly
df["D"] = df["C"].astype("category")
# The underlying bug is in DataFrame._get_bool_data, so we check
# that while we're here
res = df._get_bool_data()
expected = df[["C"]]
tm.assert_frame_equal(res, expected)
res = df.all(bool_only=True, axis=0)
expected = Series([True], index=["C"])
tm.assert_series_equal(res, expected)
# operating on a subset of columns should not produce a _larger_ Series
res = df[["B", "C"]].all(bool_only=True, axis=0)
tm.assert_series_equal(res, expected)
assert df.all(bool_only=True, axis=None)
res = df.any(bool_only=True, axis=0)
expected = Series([True], index=["C"])
tm.assert_series_equal(res, expected)
# operating on a subset of columns should not produce a _larger_ Series
res = df[["C"]].any(bool_only=True, axis=0)
tm.assert_series_equal(res, expected)
assert df.any(bool_only=True, axis=None)
# ---------------------------------------------------------------------
# Unsorted
def test_series_broadcasting(self):
# smoke test for numpy warnings
# GH 16378, GH 16306
df = DataFrame([1.0, 1.0, 1.0])
df_nan = DataFrame({"A": [np.nan, 2.0, np.nan]})
s = Series([1, 1, 1])
s_nan = Series([np.nan, np.nan, 1])
with tm.assert_produces_warning(None):
df_nan.clip(lower=s, axis=0)
for op in ["lt", "le", "gt", "ge", "eq", "ne"]:
getattr(df, op)(s_nan, axis=0)
class TestDataFrameReductions:
def test_min_max_dt64_with_NaT(self):
# Both NaT and Timestamp are in DataFrame.
df = DataFrame({"foo": [pd.NaT, pd.NaT, Timestamp("2012-05-01")]})
res = df.min()
exp = Series([Timestamp("2012-05-01")], index=["foo"])
tm.assert_series_equal(res, exp)
res = df.max()
exp = Series([Timestamp("2012-05-01")], index=["foo"])
tm.assert_series_equal(res, exp)
# GH12941, only NaTs are in DataFrame.
df = DataFrame({"foo": [pd.NaT, pd.NaT]})
res = df.min()
exp = Series([pd.NaT], index=["foo"])
tm.assert_series_equal(res, exp)
res = df.max()
exp = Series([pd.NaT], index=["foo"])
tm.assert_series_equal(res, exp)
def test_min_max_dt64_with_NaT_skipna_false(self, request, tz_naive_fixture):
# GH#36907
tz = tz_naive_fixture
if isinstance(tz, tzlocal) and is_platform_windows():
pytest.skip(
"GH#37659 OSError raised within tzlocal bc Windows "
"chokes in times before 1970-01-01"
)
df = DataFrame(
{
"a": [
Timestamp("2020-01-01 08:00:00", tz=tz),
Timestamp("1920-02-01 09:00:00", tz=tz),
],
"b": [Timestamp("2020-02-01 08:00:00", tz=tz), pd.NaT],
}
)
res = df.min(axis=1, skipna=False)
expected = Series([df.loc[0, "a"], pd.NaT])
assert expected.dtype == df["a"].dtype
tm.assert_series_equal(res, expected)
res = df.max(axis=1, skipna=False)
expected = Series([df.loc[0, "b"], pd.NaT])
assert expected.dtype == df["a"].dtype
tm.assert_series_equal(res, expected)
def test_min_max_dt64_api_consistency_with_NaT(self):
# Calling the following sum functions returned an error for dataframes but
# returned NaT for series. These tests check that the API is consistent in
# min/max calls on empty Series/DataFrames. See GH:33704 for more
# information
df = DataFrame({"x": to_datetime([])})
expected_dt_series = Series(to_datetime([]))
# check axis 0
assert (df.min(axis=0).x is pd.NaT) == (expected_dt_series.min() is pd.NaT)
assert (df.max(axis=0).x is pd.NaT) == (expected_dt_series.max() is pd.NaT)
# check axis 1
tm.assert_series_equal(df.min(axis=1), expected_dt_series)
tm.assert_series_equal(df.max(axis=1), expected_dt_series)
def test_min_max_dt64_api_consistency_empty_df(self):
# check DataFrame/Series api consistency when calling min/max on an empty
# DataFrame/Series.
df = DataFrame({"x": []})
expected_float_series = Series([], dtype=float)
# check axis 0
assert np.isnan(df.min(axis=0).x) == np.isnan(expected_float_series.min())
assert np.isnan(df.max(axis=0).x) == np.isnan(expected_float_series.max())
# check axis 1
tm.assert_series_equal(df.min(axis=1), expected_float_series)
tm.assert_series_equal(df.min(axis=1), expected_float_series)
@pytest.mark.parametrize(
"initial",
["2018-10-08 13:36:45+00:00", "2018-10-08 13:36:45+03:00"], # Non-UTC timezone
)
@pytest.mark.parametrize("method", ["min", "max"])
def test_preserve_timezone(self, initial: str, method):
# GH 28552
initial_dt = to_datetime(initial)
expected = Series([initial_dt])
df = DataFrame([expected])
result = getattr(df, method)(axis=1)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("method", ["min", "max"])
def test_minmax_tzaware_skipna_axis_1(self, method, skipna):
# GH#51242
val = to_datetime("1900-01-01", utc=True)
df = DataFrame(
{"a": Series([pd.NaT, pd.NaT, val]), "b": Series([pd.NaT, val, val])}
)
op = getattr(df, method)
result = op(axis=1, skipna=skipna)
if skipna:
expected = Series([pd.NaT, val, val])
else:
expected = Series([pd.NaT, pd.NaT, val])
tm.assert_series_equal(result, expected)
def test_frame_any_with_timedelta(self):
# GH#17667
df = DataFrame(
{
"a": Series([0, 0]),
"t": Series([to_timedelta(0, "s"), to_timedelta(1, "ms")]),
}
)
result = df.any(axis=0)
expected = Series(data=[False, True], index=["a", "t"])
tm.assert_series_equal(result, expected)
result = df.any(axis=1)
expected = Series(data=[False, True])
tm.assert_series_equal(result, expected)
def test_reductions_skipna_none_raises(
self, request, frame_or_series, all_reductions
):
if all_reductions == "count":
request.applymarker(
pytest.mark.xfail(reason="Count does not accept skipna")
)
obj = frame_or_series([1, 2, 3])
msg = 'For argument "skipna" expected type bool, received type NoneType.'
with pytest.raises(ValueError, match=msg):
getattr(obj, all_reductions)(skipna=None)
@td.skip_array_manager_invalid_test
def test_reduction_timestamp_smallest_unit(self):
# GH#52524
df = DataFrame(
{
"a": Series([Timestamp("2019-12-31")], dtype="datetime64[s]"),
"b": Series(
[Timestamp("2019-12-31 00:00:00.123")], dtype="datetime64[ms]"
),
}
)
result = df.max()
expected = Series(
[Timestamp("2019-12-31"), Timestamp("2019-12-31 00:00:00.123")],
dtype="datetime64[ms]",
index=["a", "b"],
)
tm.assert_series_equal(result, expected)
@td.skip_array_manager_not_yet_implemented
def test_reduction_timedelta_smallest_unit(self):
# GH#52524
df = DataFrame(
{
"a": Series([pd.Timedelta("1 days")], dtype="timedelta64[s]"),
"b": Series([pd.Timedelta("1 days")], dtype="timedelta64[ms]"),
}
)
result = df.max()
expected = Series(
[pd.Timedelta("1 days"), pd.Timedelta("1 days")],
dtype="timedelta64[ms]",
index=["a", "b"],
)
tm.assert_series_equal(result, expected)
class TestNuisanceColumns:
@pytest.mark.parametrize("method", ["any", "all"])
def test_any_all_categorical_dtype_nuisance_column(self, method):
# GH#36076 DataFrame should match Series behavior
ser = Series([0, 1], dtype="category", name="A")
df = ser.to_frame()
# Double-check the Series behavior is to raise
with pytest.raises(TypeError, match="does not support reduction"):
getattr(ser, method)()
with pytest.raises(TypeError, match="does not support reduction"):
getattr(np, method)(ser)
with pytest.raises(TypeError, match="does not support reduction"):
getattr(df, method)(bool_only=False)
with pytest.raises(TypeError, match="does not support reduction"):
getattr(df, method)(bool_only=None)
with pytest.raises(TypeError, match="does not support reduction"):
getattr(np, method)(df, axis=0)
def test_median_categorical_dtype_nuisance_column(self):
# GH#21020 DataFrame.median should match Series.median
df = DataFrame({"A": Categorical([1, 2, 2, 2, 3])})
ser = df["A"]
# Double-check the Series behavior is to raise
with pytest.raises(TypeError, match="does not support reduction"):
ser.median()
with pytest.raises(TypeError, match="does not support reduction"):
df.median(numeric_only=False)
with pytest.raises(TypeError, match="does not support reduction"):
df.median()
# same thing, but with an additional non-categorical column
df["B"] = df["A"].astype(int)
with pytest.raises(TypeError, match="does not support reduction"):
df.median(numeric_only=False)
with pytest.raises(TypeError, match="does not support reduction"):
df.median()
# TODO: np.median(df, axis=0) gives np.array([2.0, 2.0]) instead
# of expected.values
@pytest.mark.parametrize("method", ["min", "max"])
def test_min_max_categorical_dtype_non_ordered_nuisance_column(self, method):
# GH#28949 DataFrame.min should behave like Series.min
cat = Categorical(["a", "b", "c", "b"], ordered=False)
ser = Series(cat)
df = ser.to_frame("A")
# Double-check the Series behavior
with pytest.raises(TypeError, match="is not ordered for operation"):
getattr(ser, method)()
with pytest.raises(TypeError, match="is not ordered for operation"):
getattr(np, method)(ser)
with pytest.raises(TypeError, match="is not ordered for operation"):
getattr(df, method)(numeric_only=False)
with pytest.raises(TypeError, match="is not ordered for operation"):
getattr(df, method)()
with pytest.raises(TypeError, match="is not ordered for operation"):
getattr(np, method)(df, axis=0)
# same thing, but with an additional non-categorical column
df["B"] = df["A"].astype(object)
with pytest.raises(TypeError, match="is not ordered for operation"):
getattr(df, method)()
with pytest.raises(TypeError, match="is not ordered for operation"):
getattr(np, method)(df, axis=0)
class TestEmptyDataFrameReductions:
@pytest.mark.parametrize(
"opname, dtype, exp_value, exp_dtype",
[
("sum", np.int8, 0, np.int64),
("prod", np.int8, 1, np.int_),
("sum", np.int64, 0, np.int64),
("prod", np.int64, 1, np.int64),
("sum", np.uint8, 0, np.uint64),
("prod", np.uint8, 1, np.uint),
("sum", np.uint64, 0, np.uint64),
("prod", np.uint64, 1, np.uint64),
("sum", np.float32, 0, np.float32),
("prod", np.float32, 1, np.float32),
("sum", np.float64, 0, np.float64),
],
)
def test_df_empty_min_count_0(self, opname, dtype, exp_value, exp_dtype):
df = DataFrame({0: [], 1: []}, dtype=dtype)
result = getattr(df, opname)(min_count=0)
expected = Series([exp_value, exp_value], dtype=exp_dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"opname, dtype, exp_dtype",
[
("sum", np.int8, np.float64),
("prod", np.int8, np.float64),
("sum", np.int64, np.float64),
("prod", np.int64, np.float64),
("sum", np.uint8, np.float64),
("prod", np.uint8, np.float64),
("sum", np.uint64, np.float64),
("prod", np.uint64, np.float64),
("sum", np.float32, np.float32),
("prod", np.float32, np.float32),
("sum", np.float64, np.float64),
],
)
def test_df_empty_min_count_1(self, opname, dtype, exp_dtype):
df = DataFrame({0: [], 1: []}, dtype=dtype)
result = getattr(df, opname)(min_count=1)
expected = Series([np.nan, np.nan], dtype=exp_dtype)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"opname, dtype, exp_value, exp_dtype",
[
("sum", "Int8", 0, ("Int32" if is_windows_np2_or_is32 else "Int64")),
("prod", "Int8", 1, ("Int32" if is_windows_np2_or_is32 else "Int64")),
("prod", "Int8", 1, ("Int32" if is_windows_np2_or_is32 else "Int64")),
("sum", "Int64", 0, "Int64"),
("prod", "Int64", 1, "Int64"),
("sum", "UInt8", 0, ("UInt32" if is_windows_np2_or_is32 else "UInt64")),
("prod", "UInt8", 1, ("UInt32" if is_windows_np2_or_is32 else "UInt64")),
("sum", "UInt64", 0, "UInt64"),
("prod", "UInt64", 1, "UInt64"),
("sum", "Float32", 0, "Float32"),
("prod", "Float32", 1, "Float32"),
("sum", "Float64", 0, "Float64"),
],
)
def test_df_empty_nullable_min_count_0(self, opname, dtype, exp_value, exp_dtype):
df = DataFrame({0: [], 1: []}, dtype=dtype)
result = getattr(df, opname)(min_count=0)
expected = Series([exp_value, exp_value], dtype=exp_dtype)
tm.assert_series_equal(result, expected)
# TODO: why does min_count=1 impact the resulting Windows dtype
# differently than min_count=0?
@pytest.mark.parametrize(
"opname, dtype, exp_dtype",
[
("sum", "Int8", ("Int32" if is_windows_or_is32 else "Int64")),
("prod", "Int8", ("Int32" if is_windows_or_is32 else "Int64")),
("sum", "Int64", "Int64"),
("prod", "Int64", "Int64"),
("sum", "UInt8", ("UInt32" if is_windows_or_is32 else "UInt64")),
("prod", "UInt8", ("UInt32" if is_windows_or_is32 else "UInt64")),
("sum", "UInt64", "UInt64"),
("prod", "UInt64", "UInt64"),
("sum", "Float32", "Float32"),
("prod", "Float32", "Float32"),
("sum", "Float64", "Float64"),
],
)
def test_df_empty_nullable_min_count_1(self, opname, dtype, exp_dtype):
df = DataFrame({0: [], 1: []}, dtype=dtype)
result = getattr(df, opname)(min_count=1)
expected = Series([pd.NA, pd.NA], dtype=exp_dtype)
tm.assert_series_equal(result, expected)
def test_sum_timedelta64_skipna_false(using_array_manager, request):
# GH#17235
if using_array_manager:
mark = pytest.mark.xfail(
reason="Incorrect type inference on NaT in reduction result"
)
request.applymarker(mark)
arr = np.arange(8).astype(np.int64).view("m8[s]").reshape(4, 2)
arr[-1, -1] = "Nat"
df = DataFrame(arr)
assert (df.dtypes == arr.dtype).all()
result = df.sum(skipna=False)
expected = Series([pd.Timedelta(seconds=12), pd.NaT], dtype="m8[s]")
tm.assert_series_equal(result, expected)
result = df.sum(axis=0, skipna=False)
tm.assert_series_equal(result, expected)
result = df.sum(axis=1, skipna=False)
expected = Series(
[
pd.Timedelta(seconds=1),
pd.Timedelta(seconds=5),
pd.Timedelta(seconds=9),
pd.NaT,
],
dtype="m8[s]",
)
tm.assert_series_equal(result, expected)
@pytest.mark.xfail(
using_pyarrow_string_dtype(), reason="sum doesn't work with arrow strings"
)
def test_mixed_frame_with_integer_sum():
# https://github.com/pandas-dev/pandas/issues/34520
df = DataFrame([["a", 1]], columns=list("ab"))
df = df.astype({"b": "Int64"})
result = df.sum()
expected = Series(["a", 1], index=["a", "b"])
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("numeric_only", [True, False, None])
@pytest.mark.parametrize("method", ["min", "max"])
def test_minmax_extensionarray(method, numeric_only):
# https://github.com/pandas-dev/pandas/issues/32651
int64_info = np.iinfo("int64")
ser = Series([int64_info.max, None, int64_info.min], dtype=pd.Int64Dtype())
df = DataFrame({"Int64": ser})
result = getattr(df, method)(numeric_only=numeric_only)
expected = Series(
[getattr(int64_info, method)],
dtype="Int64",
index=Index(["Int64"]),
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("ts_value", [Timestamp("2000-01-01"), pd.NaT])
def test_frame_mixed_numeric_object_with_timestamp(ts_value):
# GH 13912
df = DataFrame({"a": [1], "b": [1.1], "c": ["foo"], "d": [ts_value]})
with pytest.raises(TypeError, match="does not support reduction"):
df.sum()
def test_prod_sum_min_count_mixed_object():
# https://github.com/pandas-dev/pandas/issues/41074
df = DataFrame([1, "a", True])
result = df.prod(axis=0, min_count=1, numeric_only=False)
expected = Series(["a"], dtype=object)
tm.assert_series_equal(result, expected)
msg = re.escape("unsupported operand type(s) for +: 'int' and 'str'")
with pytest.raises(TypeError, match=msg):
df.sum(axis=0, min_count=1, numeric_only=False)
@pytest.mark.parametrize("method", ["min", "max", "mean", "median", "skew", "kurt"])
@pytest.mark.parametrize("numeric_only", [True, False])
@pytest.mark.parametrize("dtype", ["float64", "Float64"])
def test_reduction_axis_none_returns_scalar(method, numeric_only, dtype):
# GH#21597 As of 2.0, axis=None reduces over all axes.
df = DataFrame(np.random.default_rng(2).standard_normal((4, 4)), dtype=dtype)
result = getattr(df, method)(axis=None, numeric_only=numeric_only)
np_arr = df.to_numpy(dtype=np.float64)
if method in {"skew", "kurt"}:
comp_mod = pytest.importorskip("scipy.stats")
if method == "kurt":
method = "kurtosis"
expected = getattr(comp_mod, method)(np_arr, bias=False, axis=None)
tm.assert_almost_equal(result, expected)
else:
expected = getattr(np, method)(np_arr, axis=None)
assert result == expected
@pytest.mark.parametrize(
"kernel",
[
"corr",
"corrwith",
"cov",
"idxmax",
"idxmin",
"kurt",
"max",
"mean",
"median",
"min",
"prod",
"quantile",
"sem",
"skew",
"std",
"sum",
"var",
],
)
def test_fails_on_non_numeric(kernel):
# GH#46852
df = DataFrame({"a": [1, 2, 3], "b": object})
args = (df,) if kernel == "corrwith" else ()
msg = "|".join(
[
"not allowed for this dtype",
"argument must be a string or a number",
"not supported between instances of",
"unsupported operand type",
"argument must be a string or a real number",
]
)
if kernel == "median":
# slightly different message on different builds
msg1 = (
r"Cannot convert \[\[<class 'object'> <class 'object'> "
r"<class 'object'>\]\] to numeric"
)
msg2 = (
r"Cannot convert \[<class 'object'> <class 'object'> "
r"<class 'object'>\] to numeric"
)
msg = "|".join([msg1, msg2])
with pytest.raises(TypeError, match=msg):
getattr(df, kernel)(*args)
@pytest.mark.parametrize(
"method",
[
"all",
"any",
"count",
"idxmax",
"idxmin",
"kurt",
"kurtosis",
"max",
"mean",
"median",
"min",
"nunique",
"prod",
"product",
"sem",
"skew",
"std",
"sum",
"var",
],
)
@pytest.mark.parametrize("min_count", [0, 2])
def test_numeric_ea_axis_1(method, skipna, min_count, any_numeric_ea_dtype):
# GH 54341
df = DataFrame(
{
"a": Series([0, 1, 2, 3], dtype=any_numeric_ea_dtype),
"b": Series([0, 1, pd.NA, 3], dtype=any_numeric_ea_dtype),
},
)
expected_df = DataFrame(
{
"a": [0.0, 1.0, 2.0, 3.0],
"b": [0.0, 1.0, np.nan, 3.0],
},
)
if method in ("count", "nunique"):
expected_dtype = "int64"
elif method in ("all", "any"):
expected_dtype = "boolean"
elif method in (
"kurt",
"kurtosis",
"mean",
"median",
"sem",
"skew",
"std",
"var",
) and not any_numeric_ea_dtype.startswith("Float"):
expected_dtype = "Float64"
else:
expected_dtype = any_numeric_ea_dtype
kwargs = {}
if method not in ("count", "nunique", "quantile"):
kwargs["skipna"] = skipna
if method in ("prod", "product", "sum"):
kwargs["min_count"] = min_count
warn = None
msg = None
if not skipna and method in ("idxmax", "idxmin"):
warn = FutureWarning
msg = f"The behavior of DataFrame.{method} with all-NA values"
with tm.assert_produces_warning(warn, match=msg):
result = getattr(df, method)(axis=1, **kwargs)
with tm.assert_produces_warning(warn, match=msg):
expected = getattr(expected_df, method)(axis=1, **kwargs)
if method not in ("idxmax", "idxmin"):
expected = expected.astype(expected_dtype)
tm.assert_series_equal(result, expected)