1888 lines
67 KiB
Python
1888 lines
67 KiB
Python
from datetime import timedelta
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from decimal import Decimal
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import inspect
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import re
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from dateutil.tz import tzlocal
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import numpy as np
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import pytest
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from pandas._libs import lib
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from pandas.compat import is_platform_windows
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.common import is_categorical_dtype
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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Index,
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MultiIndex,
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Series,
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Timestamp,
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date_range,
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isna,
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notna,
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to_datetime,
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to_timedelta,
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)
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import pandas._testing as tm
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import pandas.core.algorithms as algorithms
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import pandas.core.nanops as nanops
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def assert_stat_op_calc(
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opname,
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alternative,
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frame,
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has_skipna=True,
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check_dtype=True,
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check_dates=False,
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rtol=1e-5,
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atol=1e-8,
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skipna_alternative=None,
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):
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"""
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Check that operator opname works as advertised on frame
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Parameters
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----------
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opname : str
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Name of the operator to test on frame
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alternative : function
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Function that opname is tested against; i.e. "frame.opname()" should
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equal "alternative(frame)".
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frame : DataFrame
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The object that the tests are executed on
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has_skipna : bool, default True
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Whether the method "opname" has the kwarg "skip_na"
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check_dtype : bool, default True
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Whether the dtypes of the result of "frame.opname()" and
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"alternative(frame)" should be checked.
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check_dates : bool, default false
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Whether opname should be tested on a Datetime Series
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rtol : float, default 1e-5
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Relative tolerance.
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atol : float, default 1e-8
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Absolute tolerance.
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skipna_alternative : function, default None
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NaN-safe version of alternative
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"""
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warn = FutureWarning if opname == "mad" else None
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f = getattr(frame, opname)
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if check_dates:
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expected_warning = FutureWarning if opname in ["mean", "median"] else None
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df = DataFrame({"b": date_range("1/1/2001", periods=2)})
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with tm.assert_produces_warning(expected_warning):
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result = getattr(df, opname)()
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assert isinstance(result, Series)
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df["a"] = range(len(df))
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with tm.assert_produces_warning(expected_warning):
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result = getattr(df, opname)()
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assert isinstance(result, Series)
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assert len(result)
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if has_skipna:
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def wrapper(x):
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return alternative(x.values)
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skipna_wrapper = tm._make_skipna_wrapper(alternative, skipna_alternative)
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with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
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result0 = f(axis=0, skipna=False)
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result1 = f(axis=1, skipna=False)
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tm.assert_series_equal(
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result0, frame.apply(wrapper), check_dtype=check_dtype, rtol=rtol, atol=atol
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)
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tm.assert_series_equal(
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result1,
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frame.apply(wrapper, axis=1),
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rtol=rtol,
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atol=atol,
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)
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else:
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skipna_wrapper = alternative
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with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
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result0 = f(axis=0)
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result1 = f(axis=1)
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tm.assert_series_equal(
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result0,
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frame.apply(skipna_wrapper),
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check_dtype=check_dtype,
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rtol=rtol,
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atol=atol,
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)
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if opname in ["sum", "prod"]:
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expected = frame.apply(skipna_wrapper, axis=1)
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tm.assert_series_equal(
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result1, expected, check_dtype=False, rtol=rtol, atol=atol
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)
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# check dtypes
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if check_dtype:
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lcd_dtype = frame.values.dtype
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assert lcd_dtype == result0.dtype
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assert lcd_dtype == result1.dtype
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# bad axis
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with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
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with pytest.raises(ValueError, match="No axis named 2"):
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f(axis=2)
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# all NA case
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if has_skipna:
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all_na = frame * np.NaN
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with tm.assert_produces_warning(
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warn, match="The 'mad' method is deprecated", raise_on_extra_warnings=False
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):
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r0 = getattr(all_na, opname)(axis=0)
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r1 = getattr(all_na, opname)(axis=1)
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if opname in ["sum", "prod"]:
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unit = 1 if opname == "prod" else 0 # result for empty sum/prod
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expected = Series(unit, index=r0.index, dtype=r0.dtype)
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tm.assert_series_equal(r0, expected)
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expected = Series(unit, index=r1.index, dtype=r1.dtype)
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tm.assert_series_equal(r1, expected)
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class TestDataFrameAnalytics:
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# ---------------------------------------------------------------------
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# Reductions
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@pytest.mark.filterwarnings("ignore:Dropping of nuisance:FutureWarning")
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@pytest.mark.parametrize("axis", [0, 1])
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@pytest.mark.parametrize(
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"opname",
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[
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"count",
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"sum",
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"mean",
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"product",
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"median",
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"min",
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"max",
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"nunique",
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"mad",
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"var",
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"std",
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"sem",
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pytest.param("skew", marks=td.skip_if_no_scipy),
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pytest.param("kurt", marks=td.skip_if_no_scipy),
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],
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)
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def test_stat_op_api_float_string_frame(self, float_string_frame, axis, opname):
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warn = FutureWarning if opname == "mad" else None
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with tm.assert_produces_warning(
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warn, match="The 'mad' method is deprecated", raise_on_extra_warnings=False
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):
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getattr(float_string_frame, opname)(axis=axis)
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if opname not in ("nunique", "mad"):
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getattr(float_string_frame, opname)(axis=axis, numeric_only=True)
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@pytest.mark.filterwarnings("ignore:Dropping of nuisance:FutureWarning")
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@pytest.mark.parametrize("axis", [0, 1])
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@pytest.mark.parametrize(
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"opname",
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[
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"count",
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"sum",
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"mean",
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"product",
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"median",
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"min",
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"max",
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"var",
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"std",
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"sem",
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pytest.param("skew", marks=td.skip_if_no_scipy),
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pytest.param("kurt", marks=td.skip_if_no_scipy),
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],
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)
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def test_stat_op_api_float_frame(self, float_frame, axis, opname):
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getattr(float_frame, opname)(axis=axis, numeric_only=False)
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def test_stat_op_calc(self, float_frame_with_na, mixed_float_frame):
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def count(s):
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return notna(s).sum()
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def nunique(s):
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return len(algorithms.unique1d(s.dropna()))
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def mad(x):
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return np.abs(x - x.mean()).mean()
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def var(x):
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return np.var(x, ddof=1)
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def std(x):
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return np.std(x, ddof=1)
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def sem(x):
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return np.std(x, ddof=1) / np.sqrt(len(x))
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assert_stat_op_calc(
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"nunique",
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nunique,
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float_frame_with_na,
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has_skipna=False,
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check_dtype=False,
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check_dates=True,
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)
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# GH#32571 check_less_precise is needed on apparently-random
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# py37-npdev builds and OSX-PY36-min_version builds
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# mixed types (with upcasting happening)
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assert_stat_op_calc(
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"sum",
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np.sum,
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mixed_float_frame.astype("float32"),
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check_dtype=False,
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rtol=1e-3,
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)
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assert_stat_op_calc(
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"sum", np.sum, float_frame_with_na, skipna_alternative=np.nansum
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)
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assert_stat_op_calc("mean", np.mean, float_frame_with_na, check_dates=True)
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assert_stat_op_calc(
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"product", np.prod, float_frame_with_na, skipna_alternative=np.nanprod
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)
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assert_stat_op_calc("mad", mad, float_frame_with_na)
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assert_stat_op_calc("var", var, float_frame_with_na)
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assert_stat_op_calc("std", std, float_frame_with_na)
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assert_stat_op_calc("sem", sem, float_frame_with_na)
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assert_stat_op_calc(
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"count",
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count,
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float_frame_with_na,
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has_skipna=False,
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check_dtype=False,
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check_dates=True,
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)
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@td.skip_if_no_scipy
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def test_stat_op_calc_skew_kurtosis(self, float_frame_with_na):
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def skewness(x):
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from scipy.stats import skew
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if len(x) < 3:
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return np.nan
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return skew(x, bias=False)
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def kurt(x):
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from scipy.stats import kurtosis
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if len(x) < 4:
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return np.nan
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return kurtosis(x, bias=False)
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assert_stat_op_calc("skew", skewness, float_frame_with_na)
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assert_stat_op_calc("kurt", kurt, float_frame_with_na)
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# TODO: Ensure warning isn't emitted in the first place
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# ignore mean of empty slice and all-NaN
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@pytest.mark.filterwarnings("ignore::RuntimeWarning")
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def test_median(self, float_frame_with_na, int_frame):
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def wrapper(x):
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if isna(x).any():
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return np.nan
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return np.median(x)
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assert_stat_op_calc("median", wrapper, float_frame_with_na, check_dates=True)
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assert_stat_op_calc(
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"median", wrapper, int_frame, check_dtype=False, check_dates=True
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)
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@pytest.mark.parametrize(
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"method", ["sum", "mean", "prod", "var", "std", "skew", "min", "max"]
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)
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@pytest.mark.parametrize(
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"df",
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[
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DataFrame(
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{
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"a": [
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-0.00049987540199591344,
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-0.0016467257772919831,
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0.00067695870775883013,
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],
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"b": [-0, -0, 0.0],
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"c": [
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0.00031111847529610595,
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0.0014902627951905339,
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-0.00094099200035979691,
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],
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},
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index=["foo", "bar", "baz"],
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dtype="O",
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),
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DataFrame({0: [np.nan, 2], 1: [np.nan, 3], 2: [np.nan, 4]}, dtype=object),
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],
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)
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def test_stat_operators_attempt_obj_array(self, method, df):
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# GH#676
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assert df.values.dtype == np.object_
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result = getattr(df, method)(1)
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expected = getattr(df.astype("f8"), method)(1)
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if method in ["sum", "prod"]:
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"])
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def test_mixed_ops(self, op):
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# GH#16116
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df = DataFrame(
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{
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"int": [1, 2, 3, 4],
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"float": [1.0, 2.0, 3.0, 4.0],
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"str": ["a", "b", "c", "d"],
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}
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)
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with tm.assert_produces_warning(
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FutureWarning, match="Select only valid columns"
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):
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result = getattr(df, op)()
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assert len(result) == 2
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with pd.option_context("use_bottleneck", False):
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with tm.assert_produces_warning(
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FutureWarning, match="Select only valid columns"
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):
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result = getattr(df, op)()
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assert len(result) == 2
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def test_reduce_mixed_frame(self):
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# GH 6806
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df = DataFrame(
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{
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"bool_data": [True, True, False, False, False],
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"int_data": [10, 20, 30, 40, 50],
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"string_data": ["a", "b", "c", "d", "e"],
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}
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)
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df.reindex(columns=["bool_data", "int_data", "string_data"])
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test = df.sum(axis=0)
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tm.assert_numpy_array_equal(
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test.values, np.array([2, 150, "abcde"], dtype=object)
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)
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alt = df.T.sum(axis=1)
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tm.assert_series_equal(test, alt)
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def test_nunique(self):
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df = DataFrame({"A": [1, 1, 1], "B": [1, 2, 3], "C": [1, np.nan, 3]})
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tm.assert_series_equal(df.nunique(), Series({"A": 1, "B": 3, "C": 2}))
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tm.assert_series_equal(
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df.nunique(dropna=False), Series({"A": 1, "B": 3, "C": 3})
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)
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tm.assert_series_equal(df.nunique(axis=1), Series({0: 1, 1: 2, 2: 2}))
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tm.assert_series_equal(
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df.nunique(axis=1, dropna=False), Series({0: 1, 1: 3, 2: 2})
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)
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@pytest.mark.parametrize("tz", [None, "UTC"])
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def test_mean_mixed_datetime_numeric(self, tz):
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# https://github.com/pandas-dev/pandas/issues/24752
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df = DataFrame({"A": [1, 1], "B": [Timestamp("2000", tz=tz)] * 2})
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with tm.assert_produces_warning(FutureWarning):
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result = df.mean()
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expected = Series([1.0], index=["A"])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("tz", [None, "UTC"])
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def test_mean_excludes_datetimes(self, tz):
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# https://github.com/pandas-dev/pandas/issues/24752
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# Our long-term desired behavior is unclear, but the behavior in
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# 0.24.0rc1 was buggy.
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df = DataFrame({"A": [Timestamp("2000", tz=tz)] * 2})
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with tm.assert_produces_warning(FutureWarning):
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result = df.mean()
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expected = Series(dtype=np.float64)
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tm.assert_series_equal(result, expected)
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def test_mean_mixed_string_decimal(self):
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# GH 11670
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# possible bug when calculating mean of DataFrame?
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d = [
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{"A": 2, "B": None, "C": Decimal("628.00")},
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{"A": 1, "B": None, "C": Decimal("383.00")},
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{"A": 3, "B": None, "C": Decimal("651.00")},
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{"A": 2, "B": None, "C": Decimal("575.00")},
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{"A": 4, "B": None, "C": Decimal("1114.00")},
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{"A": 1, "B": "TEST", "C": Decimal("241.00")},
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{"A": 2, "B": None, "C": Decimal("572.00")},
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{"A": 4, "B": None, "C": Decimal("609.00")},
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{"A": 3, "B": None, "C": Decimal("820.00")},
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{"A": 5, "B": None, "C": Decimal("1223.00")},
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]
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df = DataFrame(d)
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with tm.assert_produces_warning(
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FutureWarning, match="Select only valid columns"
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):
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result = df.mean()
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expected = Series([2.7, 681.6], index=["A", "C"])
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tm.assert_series_equal(result, expected)
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|
|
def test_var_std(self, datetime_frame):
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result = datetime_frame.std(ddof=4)
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expected = datetime_frame.apply(lambda x: x.std(ddof=4))
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tm.assert_almost_equal(result, expected)
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|
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result = datetime_frame.var(ddof=4)
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expected = datetime_frame.apply(lambda x: x.var(ddof=4))
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tm.assert_almost_equal(result, expected)
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arr = np.repeat(np.random.random((1, 1000)), 1000, 0)
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result = nanops.nanvar(arr, axis=0)
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assert not (result < 0).any()
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with pd.option_context("use_bottleneck", False):
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result = nanops.nanvar(arr, axis=0)
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assert not (result < 0).any()
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|
|
|
@pytest.mark.parametrize("meth", ["sem", "var", "std"])
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def test_numeric_only_flag(self, meth):
|
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# GH 9201
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df1 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
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# set one entry to a number in str format
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df1.loc[0, "foo"] = "100"
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df2 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
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# set one entry to a non-number str
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df2.loc[0, "foo"] = "a"
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result = getattr(df1, meth)(axis=1, numeric_only=True)
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expected = getattr(df1[["bar", "baz"]], meth)(axis=1)
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tm.assert_series_equal(expected, result)
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result = getattr(df2, meth)(axis=1, numeric_only=True)
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expected = getattr(df2[["bar", "baz"]], meth)(axis=1)
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tm.assert_series_equal(expected, result)
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# df1 has all numbers, df2 has a letter inside
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msg = r"unsupported operand type\(s\) for -: 'float' and 'str'"
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with pytest.raises(TypeError, match=msg):
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getattr(df1, meth)(axis=1, numeric_only=False)
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msg = "could not convert string to float: 'a'"
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with pytest.raises(TypeError, match=msg):
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getattr(df2, meth)(axis=1, numeric_only=False)
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|
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def test_sem(self, datetime_frame):
|
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result = datetime_frame.sem(ddof=4)
|
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expected = datetime_frame.apply(lambda x: x.std(ddof=4) / np.sqrt(len(x)))
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|
tm.assert_almost_equal(result, expected)
|
|
|
|
arr = np.repeat(np.random.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()
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_kurt(self):
|
|
index = MultiIndex(
|
|
levels=[["bar"], ["one", "two", "three"], [0, 1]],
|
|
codes=[[0, 0, 0, 0, 0, 0], [0, 1, 2, 0, 1, 2], [0, 1, 0, 1, 0, 1]],
|
|
)
|
|
df = DataFrame(np.random.randn(6, 3), index=index)
|
|
|
|
kurt = df.kurt()
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
kurt2 = df.kurt(level=0).xs("bar")
|
|
tm.assert_series_equal(kurt, kurt2, check_names=False)
|
|
assert kurt.name is None
|
|
assert kurt2.name == "bar"
|
|
|
|
@pytest.mark.parametrize(
|
|
"dropna, expected",
|
|
[
|
|
(
|
|
True,
|
|
{
|
|
"A": [12],
|
|
"B": [10.0],
|
|
"C": [1.0],
|
|
"D": ["a"],
|
|
"E": Categorical(["a"], categories=["a"]),
|
|
"F": to_datetime(["2000-1-2"]),
|
|
"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": [pd.NaT],
|
|
"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": to_datetime(["2000-1-2", "NaT", "NaT", "NaT"]),
|
|
"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": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]),
|
|
"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": [np.nan, np.nan, "a", np.nan],
|
|
"E": Categorical([np.nan, np.nan, "a", np.nan]),
|
|
"F": to_datetime(["NaT", "2000-1-2", "NaT", "NaT"]),
|
|
"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": to_datetime(["2000-1-2", "2000-1-2", "NaT", "NaT"]),
|
|
"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):
|
|
# 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]})
|
|
|
|
with tm.assert_produces_warning(UserWarning):
|
|
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=int))
|
|
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[0] == diffs.loc[0, "A"]
|
|
assert result[1] == diffs.loc[0, "B"]
|
|
|
|
result = diffs.min(axis=1)
|
|
assert (result == diffs.loc[0, "B"]).all()
|
|
|
|
# max
|
|
result = diffs.max()
|
|
assert result[0] == diffs.loc[2, "A"]
|
|
assert result[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 numeric
|
|
with tm.assert_produces_warning(FutureWarning, match="Select only valid"):
|
|
result = mixed.min(axis=1)
|
|
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)
|
|
|
|
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("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 tm.assert_produces_warning(FutureWarning, match="Select only valid"):
|
|
result = df.sum()
|
|
|
|
expected = Series({"B": 7.0})
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_mean_corner(self, float_frame, float_string_frame):
|
|
# unit test when have object data
|
|
with tm.assert_produces_warning(FutureWarning, match="Select only valid"):
|
|
the_mean = float_string_frame.mean(axis=0)
|
|
the_sum = float_string_frame.sum(axis=0, numeric_only=True)
|
|
tm.assert_index_equal(the_sum.index, the_mean.index)
|
|
assert len(the_mean.index) < len(float_string_frame.columns)
|
|
|
|
# xs sum mixed type, just want to know it works...
|
|
with tm.assert_produces_warning(FutureWarning, match="Select only valid"):
|
|
the_mean = float_string_frame.mean(axis=1)
|
|
the_sum = float_string_frame.sum(axis=1, numeric_only=True)
|
|
tm.assert_index_equal(the_sum.index, the_mean.index)
|
|
|
|
# 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
|
|
|
|
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="A"),
|
|
}
|
|
)
|
|
result = df.mean(numeric_only=True)
|
|
expected = Series({"A": 1.0})
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
# in the future datetime columns will be included
|
|
result = df.mean()
|
|
expected = Series({"A": 1.0, "C": df.loc[1, "C"]})
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
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="A")
|
|
|
|
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.randint(1000, size=(10, 5))
|
|
df = DataFrame(arr, dtype="Int64")
|
|
result = df.mean(numeric_only=True)
|
|
expected = DataFrame(arr).mean()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_stats_mixed_type(self, float_string_frame):
|
|
# don't blow up
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
float_string_frame.std(1)
|
|
float_string_frame.var(1)
|
|
float_string_frame.mean(1)
|
|
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]:
|
|
result = df.idxmin(axis=axis, skipna=skipna)
|
|
expected = df.apply(Series.idxmin, axis=axis, skipna=skipna)
|
|
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")})
|
|
if numeric_only:
|
|
result = df.idxmin(numeric_only=numeric_only)
|
|
expected = Series([2, 1], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
else:
|
|
with pytest.raises(TypeError, match="not allowed for this dtype"):
|
|
df.idxmin(numeric_only=numeric_only)
|
|
|
|
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]:
|
|
result = df.idxmax(axis=axis, skipna=skipna)
|
|
expected = df.apply(Series.idxmax, axis=axis, skipna=skipna)
|
|
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")})
|
|
if numeric_only:
|
|
result = df.idxmax(numeric_only=numeric_only)
|
|
expected = Series([1, 0], index=["a", "b"])
|
|
tm.assert_series_equal(result, expected)
|
|
else:
|
|
with pytest.raises(TypeError, match="not allowed for this dtype"):
|
|
df.idxmin(numeric_only=numeric_only)
|
|
|
|
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.randn(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.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):
|
|
# 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],
|
|
]
|
|
)
|
|
result = getattr(df, bool_agg_func)(axis=axis, skipna=skipna)
|
|
expected = Series([True, True, True, True])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
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]}
|
|
)
|
|
|
|
result = df.all(bool_only=True)
|
|
expected = Series(dtype=np.bool_)
|
|
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)
|
|
|
|
warn = None
|
|
if any(is_categorical_dtype(x) for x in data.dtypes):
|
|
warn = FutureWarning
|
|
|
|
with tm.assert_produces_warning(
|
|
warn, match="Select only valid columns", check_stacklevel=False
|
|
):
|
|
result = func(data)
|
|
assert isinstance(result, np.bool_)
|
|
assert result.item() is expected
|
|
|
|
# method version
|
|
with tm.assert_produces_warning(
|
|
warn, match="Select only valid columns", check_stacklevel=False
|
|
):
|
|
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):
|
|
msg = "object-dtype columns with all-bool values"
|
|
|
|
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
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
res = df._get_bool_data()
|
|
expected = df[["B", "C"]]
|
|
tm.assert_frame_equal(res, expected)
|
|
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
res = df.all(bool_only=True, axis=0)
|
|
expected = Series([False, True], index=["B", "C"])
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
# operating on a subset of columns should not produce a _larger_ Series
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
res = df[["B", "C"]].all(bool_only=True, axis=0)
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
assert not df.all(bool_only=True, axis=None)
|
|
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
res = df.any(bool_only=True, axis=0)
|
|
expected = Series([True, True], index=["B", "C"])
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
# operating on a subset of columns should not produce a _larger_ Series
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
res = df[["B", "C"]].any(bool_only=True, axis=0)
|
|
tm.assert_series_equal(res, expected)
|
|
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
assert df.any(bool_only=True, axis=None)
|
|
|
|
@pytest.mark.parametrize("method", ["any", "all"])
|
|
def test_any_all_level_axis_none_raises(self, method):
|
|
df = DataFrame(
|
|
{"A": 1},
|
|
index=MultiIndex.from_product(
|
|
[["A", "B"], ["a", "b"]], names=["out", "in"]
|
|
),
|
|
)
|
|
xpr = "Must specify 'axis' when aggregating by level."
|
|
with pytest.raises(ValueError, match=xpr):
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
getattr(df, method)(axis=None, level="out")
|
|
|
|
# ---------------------------------------------------------------------
|
|
# 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)
|
|
|
|
def test_frame_any_all_with_level(self):
|
|
df = DataFrame(
|
|
{"data": [False, False, True, False, True, False, True]},
|
|
index=[
|
|
["one", "one", "two", "one", "two", "two", "two"],
|
|
[0, 1, 0, 2, 1, 2, 3],
|
|
],
|
|
)
|
|
|
|
with tm.assert_produces_warning(FutureWarning, match="Using the level"):
|
|
result = df.any(level=0)
|
|
ex = DataFrame({"data": [False, True]}, index=["one", "two"])
|
|
tm.assert_frame_equal(result, ex)
|
|
|
|
with tm.assert_produces_warning(FutureWarning, match="Using the level"):
|
|
result = df.all(level=0)
|
|
ex = DataFrame({"data": [False, False]}, index=["one", "two"])
|
|
tm.assert_frame_equal(result, ex)
|
|
|
|
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_deprecation_skipna_none(self, frame_or_series):
|
|
# GH#44580
|
|
obj = frame_or_series([1, 2, 3])
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="skipna", raise_on_extra_warnings=False
|
|
):
|
|
obj.mad(skipna=None)
|
|
|
|
def test_reductions_deprecation_level_argument(
|
|
self, frame_or_series, reduction_functions
|
|
):
|
|
# GH#39983
|
|
obj = frame_or_series(
|
|
[1, 2, 3], index=MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]])
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning, match="level"):
|
|
getattr(obj, reduction_functions)(level=0)
|
|
|
|
def test_reductions_skipna_none_raises(
|
|
self, request, frame_or_series, reduction_functions
|
|
):
|
|
if reduction_functions == "count":
|
|
request.node.add_marker(
|
|
pytest.mark.xfail(reason="Count does not accept skipna")
|
|
)
|
|
elif reduction_functions == "mad":
|
|
pytest.skip("Mad is deprecated: GH#11787")
|
|
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, reduction_functions)(skipna=None)
|
|
|
|
|
|
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 bool_only=None, operating on this column raises and is ignored,
|
|
# so we expect an empty result.
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
result = getattr(df, method)(bool_only=None)
|
|
expected = Series([], index=Index([]), dtype=bool)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns", check_stacklevel=False
|
|
):
|
|
result = getattr(np, method)(df, axis=0)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
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 tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
result = df.median()
|
|
expected = Series([], index=Index([]), dtype=np.float64)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# 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 tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
result = df.median()
|
|
expected = Series([2.0], index=["B"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# 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 tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
result = getattr(df, method)()
|
|
expected = Series([], index=Index([]), dtype=np.float64)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns", check_stacklevel=False
|
|
):
|
|
result = getattr(np, method)(df)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# same thing, but with an additional non-categorical column
|
|
df["B"] = df["A"].astype(object)
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
result = getattr(df, method)()
|
|
if method == "min":
|
|
expected = Series(["a"], index=["B"])
|
|
else:
|
|
expected = Series(["c"], index=["B"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns", check_stacklevel=False
|
|
):
|
|
result = getattr(np, method)(df)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_reduction_object_block_splits_nuisance_columns(self):
|
|
# GH#37827
|
|
df = DataFrame({"A": [0, 1, 2], "B": ["a", "b", "c"]}, dtype=object)
|
|
|
|
# We should only exclude "B", not "A"
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
result = df.mean()
|
|
expected = Series([1.0], index=["A"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# Same behavior but heterogeneous dtype
|
|
df["C"] = df["A"].astype(int) + 4
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="Select only valid columns"
|
|
):
|
|
result = df.mean()
|
|
expected = Series([1.0, 5.0], index=["A", "C"])
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_sum_timedelta64_skipna_false():
|
|
# GH#17235
|
|
arr = np.arange(8).astype(np.int64).view("m8[s]").reshape(4, 2)
|
|
arr[-1, -1] = "Nat"
|
|
|
|
df = DataFrame(arr)
|
|
|
|
result = df.sum(skipna=False)
|
|
expected = Series([pd.Timedelta(seconds=12), pd.NaT])
|
|
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,
|
|
]
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
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)], index=Index(["Int64"], dtype="object")
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_mad_nullable_integer(any_signed_int_ea_dtype):
|
|
# GH#33036
|
|
df = DataFrame(np.random.randn(100, 4).astype(np.int64))
|
|
df2 = df.astype(any_signed_int_ea_dtype)
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="The 'mad' method is deprecated"
|
|
):
|
|
result = df2.mad()
|
|
expected = df.mad()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="The 'mad' method is deprecated"
|
|
):
|
|
result = df2.mad(axis=1)
|
|
expected = df.mad(axis=1)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# case with NAs present
|
|
df2.iloc[::2, 1] = pd.NA
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="The 'mad' method is deprecated"
|
|
):
|
|
result = df2.mad()
|
|
expected = df.mad()
|
|
expected[1] = df.iloc[1::2, 1].mad()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="The 'mad' method is deprecated"
|
|
):
|
|
result = df2.mad(axis=1)
|
|
expected = df.mad(axis=1)
|
|
expected[::2] = df.T.loc[[0, 2, 3], ::2].mad()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.xfail(reason="GH#42895 caused by lack of 2D EA")
|
|
def test_mad_nullable_integer_all_na(any_signed_int_ea_dtype):
|
|
# GH#33036
|
|
df = DataFrame(np.random.randn(100, 4).astype(np.int64))
|
|
df2 = df.astype(any_signed_int_ea_dtype)
|
|
|
|
# case with all-NA row/column
|
|
msg = "will attempt to set the values inplace instead"
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
df2.iloc[:, 1] = pd.NA # FIXME(GH#44199): this doesn't operate in-place
|
|
df2.iloc[:, 1] = pd.array([pd.NA] * len(df2), dtype=any_signed_int_ea_dtype)
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning, match="The 'mad' method is deprecated"
|
|
):
|
|
result = df2.mad()
|
|
expected = df.mad()
|
|
|
|
expected[1] = pd.NA
|
|
expected = expected.astype("Float64")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("meth", ["max", "min", "sum", "mean", "median"])
|
|
def test_groupby_regular_arithmetic_equivalent(meth):
|
|
# GH#40660
|
|
df = DataFrame(
|
|
{"a": [pd.Timedelta(hours=6), pd.Timedelta(hours=7)], "b": [12.1, 13.3]}
|
|
)
|
|
expected = df.copy()
|
|
|
|
with tm.assert_produces_warning(FutureWarning):
|
|
result = getattr(df, meth)(level=0)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = getattr(df.groupby(level=0), meth)(numeric_only=False)
|
|
tm.assert_frame_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 tm.assert_produces_warning(
|
|
FutureWarning, match="The default value of numeric_only"
|
|
):
|
|
result = df.sum()
|
|
expected = Series([1, 1.1, "foo"], index=list("abc"))
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
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"])
|
|
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"])
|
|
def test_reduction_axis_none_deprecation(method):
|
|
# GH#21597 deprecate axis=None defaulting to axis=0 so that we can change it
|
|
# to reducing over all axes.
|
|
|
|
df = DataFrame(np.random.randn(4, 4))
|
|
meth = getattr(df, method)
|
|
|
|
msg = f"scalar {method} over the entire DataFrame"
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
res = meth(axis=None)
|
|
with tm.assert_produces_warning(None):
|
|
expected = meth()
|
|
tm.assert_series_equal(res, expected)
|
|
tm.assert_series_equal(res, meth(axis=0))
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"kernel",
|
|
[
|
|
"corr",
|
|
"corrwith",
|
|
"count",
|
|
"cov",
|
|
"idxmax",
|
|
"idxmin",
|
|
"kurt",
|
|
"kurt",
|
|
"max",
|
|
"mean",
|
|
"median",
|
|
"min",
|
|
"mode",
|
|
"prod",
|
|
"prod",
|
|
"quantile",
|
|
"sem",
|
|
"skew",
|
|
"std",
|
|
"sum",
|
|
"var",
|
|
],
|
|
)
|
|
def test_numeric_only_deprecation(kernel):
|
|
# GH#46852
|
|
df = DataFrame({"a": [1, 2, 3], "b": object})
|
|
args = (df,) if kernel == "corrwith" else ()
|
|
signature = inspect.signature(getattr(DataFrame, kernel))
|
|
default = signature.parameters["numeric_only"].default
|
|
assert default is not True
|
|
|
|
if kernel in ("idxmax", "idxmin"):
|
|
# kernels that default to numeric_only=False and fail on nuisance columns
|
|
assert default is False
|
|
with pytest.raises(TypeError, match="not allowed for this dtype"):
|
|
getattr(df, kernel)(*args)
|
|
else:
|
|
if default is None or default is lib.no_default:
|
|
expected = getattr(df[["a"]], kernel)(*args)
|
|
warn = FutureWarning
|
|
else:
|
|
# default must be False and works on any nuisance columns
|
|
expected = getattr(df, kernel)(*args)
|
|
if kernel == "mode":
|
|
assert "b" in expected.columns
|
|
else:
|
|
assert "b" in expected.index
|
|
warn = None
|
|
msg = f"The default value of numeric_only in DataFrame.{kernel}"
|
|
with tm.assert_produces_warning(warn, match=msg):
|
|
result = getattr(df, kernel)(*args)
|
|
tm.assert_equal(result, expected)
|