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1095 lines
38 KiB
1095 lines
38 KiB
from functools import partial
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import operator
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import warnings
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.common import is_integer_dtype
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import pandas as pd
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from pandas import (
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Series,
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isna,
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)
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import pandas._testing as tm
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from pandas.core.arrays import DatetimeArray
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import pandas.core.nanops as nanops
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use_bn = nanops._USE_BOTTLENECK
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has_c16 = hasattr(np, "complex128")
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@pytest.fixture(params=[True, False])
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def skipna(request):
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"""
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Fixture to pass skipna to nanops functions.
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"""
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return request.param
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class TestnanopsDataFrame:
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def setup_method(self, method):
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np.random.seed(11235)
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nanops._USE_BOTTLENECK = False
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arr_shape = (11, 7)
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self.arr_float = np.random.randn(*arr_shape)
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self.arr_float1 = np.random.randn(*arr_shape)
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self.arr_complex = self.arr_float + self.arr_float1 * 1j
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self.arr_int = np.random.randint(-10, 10, arr_shape)
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self.arr_bool = np.random.randint(0, 2, arr_shape) == 0
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self.arr_str = np.abs(self.arr_float).astype("S")
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self.arr_utf = np.abs(self.arr_float).astype("U")
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self.arr_date = np.random.randint(0, 20000, arr_shape).astype("M8[ns]")
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self.arr_tdelta = np.random.randint(0, 20000, arr_shape).astype("m8[ns]")
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self.arr_nan = np.tile(np.nan, arr_shape)
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self.arr_float_nan = np.vstack([self.arr_float, self.arr_nan])
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self.arr_float1_nan = np.vstack([self.arr_float1, self.arr_nan])
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self.arr_nan_float1 = np.vstack([self.arr_nan, self.arr_float1])
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self.arr_nan_nan = np.vstack([self.arr_nan, self.arr_nan])
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self.arr_inf = self.arr_float * np.inf
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self.arr_float_inf = np.vstack([self.arr_float, self.arr_inf])
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self.arr_nan_inf = np.vstack([self.arr_nan, self.arr_inf])
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self.arr_float_nan_inf = np.vstack([self.arr_float, self.arr_nan, self.arr_inf])
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self.arr_nan_nan_inf = np.vstack([self.arr_nan, self.arr_nan, self.arr_inf])
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self.arr_obj = np.vstack(
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[
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self.arr_float.astype("O"),
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self.arr_int.astype("O"),
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self.arr_bool.astype("O"),
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self.arr_complex.astype("O"),
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self.arr_str.astype("O"),
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self.arr_utf.astype("O"),
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self.arr_date.astype("O"),
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self.arr_tdelta.astype("O"),
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]
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)
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with np.errstate(invalid="ignore"):
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self.arr_nan_nanj = self.arr_nan + self.arr_nan * 1j
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self.arr_complex_nan = np.vstack([self.arr_complex, self.arr_nan_nanj])
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self.arr_nan_infj = self.arr_inf * 1j
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self.arr_complex_nan_infj = np.vstack([self.arr_complex, self.arr_nan_infj])
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self.arr_float_2d = self.arr_float
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self.arr_float1_2d = self.arr_float1
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self.arr_nan_2d = self.arr_nan
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self.arr_float_nan_2d = self.arr_float_nan
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self.arr_float1_nan_2d = self.arr_float1_nan
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self.arr_nan_float1_2d = self.arr_nan_float1
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self.arr_float_1d = self.arr_float[:, 0]
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self.arr_float1_1d = self.arr_float1[:, 0]
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self.arr_nan_1d = self.arr_nan[:, 0]
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self.arr_float_nan_1d = self.arr_float_nan[:, 0]
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self.arr_float1_nan_1d = self.arr_float1_nan[:, 0]
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self.arr_nan_float1_1d = self.arr_nan_float1[:, 0]
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def teardown_method(self, method):
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nanops._USE_BOTTLENECK = use_bn
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def check_results(self, targ, res, axis, check_dtype=True):
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res = getattr(res, "asm8", res)
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if (
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axis != 0
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and hasattr(targ, "shape")
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and targ.ndim
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and targ.shape != res.shape
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):
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res = np.split(res, [targ.shape[0]], axis=0)[0]
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try:
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tm.assert_almost_equal(targ, res, check_dtype=check_dtype)
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except AssertionError:
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# handle timedelta dtypes
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if hasattr(targ, "dtype") and targ.dtype == "m8[ns]":
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raise
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# There are sometimes rounding errors with
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# complex and object dtypes.
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# If it isn't one of those, re-raise the error.
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if not hasattr(res, "dtype") or res.dtype.kind not in ["c", "O"]:
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raise
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# convert object dtypes to something that can be split into
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# real and imaginary parts
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if res.dtype.kind == "O":
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if targ.dtype.kind != "O":
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res = res.astype(targ.dtype)
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else:
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cast_dtype = "c16" if has_c16 else "f8"
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res = res.astype(cast_dtype)
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targ = targ.astype(cast_dtype)
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# there should never be a case where numpy returns an object
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# but nanops doesn't, so make that an exception
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elif targ.dtype.kind == "O":
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raise
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tm.assert_almost_equal(np.real(targ), np.real(res), check_dtype=check_dtype)
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tm.assert_almost_equal(np.imag(targ), np.imag(res), check_dtype=check_dtype)
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def check_fun_data(
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self,
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testfunc,
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targfunc,
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testarval,
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targarval,
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skipna,
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check_dtype=True,
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empty_targfunc=None,
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**kwargs,
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):
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for axis in list(range(targarval.ndim)) + [None]:
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targartempval = targarval if skipna else testarval
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if skipna and empty_targfunc and isna(targartempval).all():
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targ = empty_targfunc(targartempval, axis=axis, **kwargs)
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else:
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targ = targfunc(targartempval, axis=axis, **kwargs)
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if targartempval.dtype == object and (
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targfunc is np.any or targfunc is np.all
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):
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# GH#12863 the numpy functions will retain e.g. floatiness
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if isinstance(targ, np.ndarray):
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targ = targ.astype(bool)
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else:
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targ = bool(targ)
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res = testfunc(testarval, axis=axis, skipna=skipna, **kwargs)
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self.check_results(targ, res, axis, check_dtype=check_dtype)
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if skipna:
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res = testfunc(testarval, axis=axis, **kwargs)
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self.check_results(targ, res, axis, check_dtype=check_dtype)
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if axis is None:
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res = testfunc(testarval, skipna=skipna, **kwargs)
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self.check_results(targ, res, axis, check_dtype=check_dtype)
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if skipna and axis is None:
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res = testfunc(testarval, **kwargs)
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self.check_results(targ, res, axis, check_dtype=check_dtype)
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if testarval.ndim <= 1:
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return
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# Recurse on lower-dimension
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testarval2 = np.take(testarval, 0, axis=-1)
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targarval2 = np.take(targarval, 0, axis=-1)
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self.check_fun_data(
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testfunc,
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targfunc,
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testarval2,
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targarval2,
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skipna=skipna,
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check_dtype=check_dtype,
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empty_targfunc=empty_targfunc,
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**kwargs,
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)
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def check_fun(
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self, testfunc, targfunc, testar, skipna, empty_targfunc=None, **kwargs
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):
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targar = testar
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if testar.endswith("_nan") and hasattr(self, testar[:-4]):
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targar = testar[:-4]
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testarval = getattr(self, testar)
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targarval = getattr(self, targar)
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self.check_fun_data(
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testfunc,
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targfunc,
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testarval,
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targarval,
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skipna=skipna,
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empty_targfunc=empty_targfunc,
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**kwargs,
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)
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def check_funs(
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self,
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testfunc,
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targfunc,
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skipna,
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allow_complex=True,
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allow_all_nan=True,
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allow_date=True,
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allow_tdelta=True,
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allow_obj=True,
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**kwargs,
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):
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self.check_fun(testfunc, targfunc, "arr_float", skipna, **kwargs)
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self.check_fun(testfunc, targfunc, "arr_float_nan", skipna, **kwargs)
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self.check_fun(testfunc, targfunc, "arr_int", skipna, **kwargs)
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self.check_fun(testfunc, targfunc, "arr_bool", skipna, **kwargs)
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objs = [
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self.arr_float.astype("O"),
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self.arr_int.astype("O"),
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self.arr_bool.astype("O"),
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]
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if allow_all_nan:
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self.check_fun(testfunc, targfunc, "arr_nan", skipna, **kwargs)
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if allow_complex:
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self.check_fun(testfunc, targfunc, "arr_complex", skipna, **kwargs)
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self.check_fun(testfunc, targfunc, "arr_complex_nan", skipna, **kwargs)
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if allow_all_nan:
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self.check_fun(testfunc, targfunc, "arr_nan_nanj", skipna, **kwargs)
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objs += [self.arr_complex.astype("O")]
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if allow_date:
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targfunc(self.arr_date)
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self.check_fun(testfunc, targfunc, "arr_date", skipna, **kwargs)
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objs += [self.arr_date.astype("O")]
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if allow_tdelta:
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try:
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targfunc(self.arr_tdelta)
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except TypeError:
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pass
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else:
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self.check_fun(testfunc, targfunc, "arr_tdelta", skipna, **kwargs)
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objs += [self.arr_tdelta.astype("O")]
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if allow_obj:
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self.arr_obj = np.vstack(objs)
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# some nanops handle object dtypes better than their numpy
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# counterparts, so the numpy functions need to be given something
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# else
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if allow_obj == "convert":
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targfunc = partial(
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self._badobj_wrap, func=targfunc, allow_complex=allow_complex
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)
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self.check_fun(testfunc, targfunc, "arr_obj", skipna, **kwargs)
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def _badobj_wrap(self, value, func, allow_complex=True, **kwargs):
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if value.dtype.kind == "O":
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if allow_complex:
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value = value.astype("c16")
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else:
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value = value.astype("f8")
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return func(value, **kwargs)
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@pytest.mark.parametrize(
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"nan_op,np_op", [(nanops.nanany, np.any), (nanops.nanall, np.all)]
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)
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def test_nan_funcs(self, nan_op, np_op, skipna):
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self.check_funs(nan_op, np_op, skipna, allow_all_nan=False, allow_date=False)
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def test_nansum(self, skipna):
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self.check_funs(
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nanops.nansum,
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np.sum,
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skipna,
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allow_date=False,
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check_dtype=False,
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empty_targfunc=np.nansum,
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)
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def test_nanmean(self, skipna):
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self.check_funs(
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nanops.nanmean, np.mean, skipna, allow_obj=False, allow_date=False
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)
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def test_nanmean_overflow(self):
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# GH 10155
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# In the previous implementation mean can overflow for int dtypes, it
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# is now consistent with numpy
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for a in [2**55, -(2**55), 20150515061816532]:
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s = Series(a, index=range(500), dtype=np.int64)
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result = s.mean()
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np_result = s.values.mean()
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assert result == a
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assert result == np_result
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assert result.dtype == np.float64
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@pytest.mark.parametrize(
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"dtype",
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[
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np.int16,
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np.int32,
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np.int64,
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np.float32,
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np.float64,
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getattr(np, "float128", None),
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],
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)
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def test_returned_dtype(self, dtype):
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if dtype is None:
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# no float128 available
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return
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s = Series(range(10), dtype=dtype)
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group_a = ["mean", "std", "var", "skew", "kurt"]
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group_b = ["min", "max"]
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for method in group_a + group_b:
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result = getattr(s, method)()
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if is_integer_dtype(dtype) and method in group_a:
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assert result.dtype == np.float64
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else:
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assert result.dtype == dtype
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def test_nanmedian(self, skipna):
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with warnings.catch_warnings(record=True):
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warnings.simplefilter("ignore", RuntimeWarning)
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self.check_funs(
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nanops.nanmedian,
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np.median,
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skipna,
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allow_complex=False,
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allow_date=False,
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allow_obj="convert",
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)
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@pytest.mark.parametrize("ddof", range(3))
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def test_nanvar(self, ddof, skipna):
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self.check_funs(
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nanops.nanvar,
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np.var,
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skipna,
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allow_complex=False,
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allow_date=False,
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allow_obj="convert",
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ddof=ddof,
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)
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@pytest.mark.parametrize("ddof", range(3))
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def test_nanstd(self, ddof, skipna):
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self.check_funs(
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nanops.nanstd,
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np.std,
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skipna,
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allow_complex=False,
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allow_date=False,
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allow_obj="convert",
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ddof=ddof,
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)
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|
|
@td.skip_if_no_scipy
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@pytest.mark.parametrize("ddof", range(3))
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def test_nansem(self, ddof, skipna):
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from scipy.stats import sem
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|
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with np.errstate(invalid="ignore"):
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self.check_funs(
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nanops.nansem,
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sem,
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skipna,
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allow_complex=False,
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allow_date=False,
|
|
allow_tdelta=False,
|
|
allow_obj="convert",
|
|
ddof=ddof,
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|
)
|
|
|
|
@pytest.mark.parametrize(
|
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"nan_op,np_op", [(nanops.nanmin, np.min), (nanops.nanmax, np.max)]
|
|
)
|
|
def test_nanops_with_warnings(self, nan_op, np_op, skipna):
|
|
with warnings.catch_warnings(record=True):
|
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warnings.simplefilter("ignore", RuntimeWarning)
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|
self.check_funs(nan_op, np_op, skipna, allow_obj=False)
|
|
|
|
def _argminmax_wrap(self, value, axis=None, func=None):
|
|
res = func(value, axis)
|
|
nans = np.min(value, axis)
|
|
nullnan = isna(nans)
|
|
if res.ndim:
|
|
res[nullnan] = -1
|
|
elif (
|
|
hasattr(nullnan, "all")
|
|
and nullnan.all()
|
|
or not hasattr(nullnan, "all")
|
|
and nullnan
|
|
):
|
|
res = -1
|
|
return res
|
|
|
|
def test_nanargmax(self, skipna):
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.simplefilter("ignore", RuntimeWarning)
|
|
func = partial(self._argminmax_wrap, func=np.argmax)
|
|
self.check_funs(nanops.nanargmax, func, skipna, allow_obj=False)
|
|
|
|
def test_nanargmin(self, skipna):
|
|
with warnings.catch_warnings(record=True):
|
|
warnings.simplefilter("ignore", RuntimeWarning)
|
|
func = partial(self._argminmax_wrap, func=np.argmin)
|
|
self.check_funs(nanops.nanargmin, func, skipna, allow_obj=False)
|
|
|
|
def _skew_kurt_wrap(self, values, axis=None, func=None):
|
|
if not isinstance(values.dtype.type, np.floating):
|
|
values = values.astype("f8")
|
|
result = func(values, axis=axis, bias=False)
|
|
# fix for handling cases where all elements in an axis are the same
|
|
if isinstance(result, np.ndarray):
|
|
result[np.max(values, axis=axis) == np.min(values, axis=axis)] = 0
|
|
return result
|
|
elif np.max(values) == np.min(values):
|
|
return 0.0
|
|
return result
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_nanskew(self, skipna):
|
|
from scipy.stats import skew
|
|
|
|
func = partial(self._skew_kurt_wrap, func=skew)
|
|
with np.errstate(invalid="ignore"):
|
|
self.check_funs(
|
|
nanops.nanskew,
|
|
func,
|
|
skipna,
|
|
allow_complex=False,
|
|
allow_date=False,
|
|
allow_tdelta=False,
|
|
)
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_nankurt(self, skipna):
|
|
from scipy.stats import kurtosis
|
|
|
|
func1 = partial(kurtosis, fisher=True)
|
|
func = partial(self._skew_kurt_wrap, func=func1)
|
|
with np.errstate(invalid="ignore"):
|
|
self.check_funs(
|
|
nanops.nankurt,
|
|
func,
|
|
skipna,
|
|
allow_complex=False,
|
|
allow_date=False,
|
|
allow_tdelta=False,
|
|
)
|
|
|
|
def test_nanprod(self, skipna):
|
|
self.check_funs(
|
|
nanops.nanprod,
|
|
np.prod,
|
|
skipna,
|
|
allow_date=False,
|
|
allow_tdelta=False,
|
|
empty_targfunc=np.nanprod,
|
|
)
|
|
|
|
def check_nancorr_nancov_2d(self, checkfun, targ0, targ1, **kwargs):
|
|
res00 = checkfun(self.arr_float_2d, self.arr_float1_2d, **kwargs)
|
|
res01 = checkfun(
|
|
self.arr_float_2d,
|
|
self.arr_float1_2d,
|
|
min_periods=len(self.arr_float_2d) - 1,
|
|
**kwargs,
|
|
)
|
|
tm.assert_almost_equal(targ0, res00)
|
|
tm.assert_almost_equal(targ0, res01)
|
|
|
|
res10 = checkfun(self.arr_float_nan_2d, self.arr_float1_nan_2d, **kwargs)
|
|
res11 = checkfun(
|
|
self.arr_float_nan_2d,
|
|
self.arr_float1_nan_2d,
|
|
min_periods=len(self.arr_float_2d) - 1,
|
|
**kwargs,
|
|
)
|
|
tm.assert_almost_equal(targ1, res10)
|
|
tm.assert_almost_equal(targ1, res11)
|
|
|
|
targ2 = np.nan
|
|
res20 = checkfun(self.arr_nan_2d, self.arr_float1_2d, **kwargs)
|
|
res21 = checkfun(self.arr_float_2d, self.arr_nan_2d, **kwargs)
|
|
res22 = checkfun(self.arr_nan_2d, self.arr_nan_2d, **kwargs)
|
|
res23 = checkfun(self.arr_float_nan_2d, self.arr_nan_float1_2d, **kwargs)
|
|
res24 = checkfun(
|
|
self.arr_float_nan_2d,
|
|
self.arr_nan_float1_2d,
|
|
min_periods=len(self.arr_float_2d) - 1,
|
|
**kwargs,
|
|
)
|
|
res25 = checkfun(
|
|
self.arr_float_2d,
|
|
self.arr_float1_2d,
|
|
min_periods=len(self.arr_float_2d) + 1,
|
|
**kwargs,
|
|
)
|
|
tm.assert_almost_equal(targ2, res20)
|
|
tm.assert_almost_equal(targ2, res21)
|
|
tm.assert_almost_equal(targ2, res22)
|
|
tm.assert_almost_equal(targ2, res23)
|
|
tm.assert_almost_equal(targ2, res24)
|
|
tm.assert_almost_equal(targ2, res25)
|
|
|
|
def check_nancorr_nancov_1d(self, checkfun, targ0, targ1, **kwargs):
|
|
res00 = checkfun(self.arr_float_1d, self.arr_float1_1d, **kwargs)
|
|
res01 = checkfun(
|
|
self.arr_float_1d,
|
|
self.arr_float1_1d,
|
|
min_periods=len(self.arr_float_1d) - 1,
|
|
**kwargs,
|
|
)
|
|
tm.assert_almost_equal(targ0, res00)
|
|
tm.assert_almost_equal(targ0, res01)
|
|
|
|
res10 = checkfun(self.arr_float_nan_1d, self.arr_float1_nan_1d, **kwargs)
|
|
res11 = checkfun(
|
|
self.arr_float_nan_1d,
|
|
self.arr_float1_nan_1d,
|
|
min_periods=len(self.arr_float_1d) - 1,
|
|
**kwargs,
|
|
)
|
|
tm.assert_almost_equal(targ1, res10)
|
|
tm.assert_almost_equal(targ1, res11)
|
|
|
|
targ2 = np.nan
|
|
res20 = checkfun(self.arr_nan_1d, self.arr_float1_1d, **kwargs)
|
|
res21 = checkfun(self.arr_float_1d, self.arr_nan_1d, **kwargs)
|
|
res22 = checkfun(self.arr_nan_1d, self.arr_nan_1d, **kwargs)
|
|
res23 = checkfun(self.arr_float_nan_1d, self.arr_nan_float1_1d, **kwargs)
|
|
res24 = checkfun(
|
|
self.arr_float_nan_1d,
|
|
self.arr_nan_float1_1d,
|
|
min_periods=len(self.arr_float_1d) - 1,
|
|
**kwargs,
|
|
)
|
|
res25 = checkfun(
|
|
self.arr_float_1d,
|
|
self.arr_float1_1d,
|
|
min_periods=len(self.arr_float_1d) + 1,
|
|
**kwargs,
|
|
)
|
|
tm.assert_almost_equal(targ2, res20)
|
|
tm.assert_almost_equal(targ2, res21)
|
|
tm.assert_almost_equal(targ2, res22)
|
|
tm.assert_almost_equal(targ2, res23)
|
|
tm.assert_almost_equal(targ2, res24)
|
|
tm.assert_almost_equal(targ2, res25)
|
|
|
|
def test_nancorr(self):
|
|
targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
|
|
targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
|
|
self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1)
|
|
targ0 = np.corrcoef(self.arr_float_1d, self.arr_float1_1d)[0, 1]
|
|
targ1 = np.corrcoef(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
|
|
self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="pearson")
|
|
|
|
def test_nancorr_pearson(self):
|
|
targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
|
|
targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
|
|
self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="pearson")
|
|
targ0 = np.corrcoef(self.arr_float_1d, self.arr_float1_1d)[0, 1]
|
|
targ1 = np.corrcoef(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
|
|
self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="pearson")
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_nancorr_kendall(self):
|
|
from scipy.stats import kendalltau
|
|
|
|
targ0 = kendalltau(self.arr_float_2d, self.arr_float1_2d)[0]
|
|
targ1 = kendalltau(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0]
|
|
self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="kendall")
|
|
targ0 = kendalltau(self.arr_float_1d, self.arr_float1_1d)[0]
|
|
targ1 = kendalltau(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0]
|
|
self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="kendall")
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_nancorr_spearman(self):
|
|
from scipy.stats import spearmanr
|
|
|
|
targ0 = spearmanr(self.arr_float_2d, self.arr_float1_2d)[0]
|
|
targ1 = spearmanr(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0]
|
|
self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="spearman")
|
|
targ0 = spearmanr(self.arr_float_1d, self.arr_float1_1d)[0]
|
|
targ1 = spearmanr(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0]
|
|
self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="spearman")
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_invalid_method(self):
|
|
targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1]
|
|
targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
|
|
msg = "Unknown method 'foo', expected one of 'kendall', 'spearman'"
|
|
with pytest.raises(ValueError, match=msg):
|
|
self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="foo")
|
|
|
|
def test_nancov(self):
|
|
targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1]
|
|
targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1]
|
|
self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1)
|
|
targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1]
|
|
targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1]
|
|
self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1)
|
|
|
|
def check_nancomp(self, checkfun, targ0):
|
|
arr_float = self.arr_float
|
|
arr_float1 = self.arr_float1
|
|
arr_nan = self.arr_nan
|
|
arr_nan_nan = self.arr_nan_nan
|
|
arr_float_nan = self.arr_float_nan
|
|
arr_float1_nan = self.arr_float1_nan
|
|
arr_nan_float1 = self.arr_nan_float1
|
|
|
|
while targ0.ndim:
|
|
res0 = checkfun(arr_float, arr_float1)
|
|
tm.assert_almost_equal(targ0, res0)
|
|
|
|
if targ0.ndim > 1:
|
|
targ1 = np.vstack([targ0, arr_nan])
|
|
else:
|
|
targ1 = np.hstack([targ0, arr_nan])
|
|
res1 = checkfun(arr_float_nan, arr_float1_nan)
|
|
tm.assert_numpy_array_equal(targ1, res1, check_dtype=False)
|
|
|
|
targ2 = arr_nan_nan
|
|
res2 = checkfun(arr_float_nan, arr_nan_float1)
|
|
tm.assert_numpy_array_equal(targ2, res2, check_dtype=False)
|
|
|
|
# Lower dimension for next step in the loop
|
|
arr_float = np.take(arr_float, 0, axis=-1)
|
|
arr_float1 = np.take(arr_float1, 0, axis=-1)
|
|
arr_nan = np.take(arr_nan, 0, axis=-1)
|
|
arr_nan_nan = np.take(arr_nan_nan, 0, axis=-1)
|
|
arr_float_nan = np.take(arr_float_nan, 0, axis=-1)
|
|
arr_float1_nan = np.take(arr_float1_nan, 0, axis=-1)
|
|
arr_nan_float1 = np.take(arr_nan_float1, 0, axis=-1)
|
|
targ0 = np.take(targ0, 0, axis=-1)
|
|
|
|
@pytest.mark.parametrize(
|
|
"op,nanop",
|
|
[
|
|
(operator.eq, nanops.naneq),
|
|
(operator.ne, nanops.nanne),
|
|
(operator.gt, nanops.nangt),
|
|
(operator.ge, nanops.nange),
|
|
(operator.lt, nanops.nanlt),
|
|
(operator.le, nanops.nanle),
|
|
],
|
|
)
|
|
def test_nan_comparison(self, op, nanop):
|
|
targ0 = op(self.arr_float, self.arr_float1)
|
|
self.check_nancomp(nanop, targ0)
|
|
|
|
def check_bool(self, func, value, correct):
|
|
while getattr(value, "ndim", True):
|
|
res0 = func(value)
|
|
if correct:
|
|
assert res0
|
|
else:
|
|
assert not res0
|
|
|
|
if not hasattr(value, "ndim"):
|
|
break
|
|
|
|
# Reduce dimension for next step in the loop
|
|
value = np.take(value, 0, axis=-1)
|
|
|
|
def test__has_infs(self):
|
|
pairs = [
|
|
("arr_complex", False),
|
|
("arr_int", False),
|
|
("arr_bool", False),
|
|
("arr_str", False),
|
|
("arr_utf", False),
|
|
("arr_complex", False),
|
|
("arr_complex_nan", False),
|
|
("arr_nan_nanj", False),
|
|
("arr_nan_infj", True),
|
|
("arr_complex_nan_infj", True),
|
|
]
|
|
pairs_float = [
|
|
("arr_float", False),
|
|
("arr_nan", False),
|
|
("arr_float_nan", False),
|
|
("arr_nan_nan", False),
|
|
("arr_float_inf", True),
|
|
("arr_inf", True),
|
|
("arr_nan_inf", True),
|
|
("arr_float_nan_inf", True),
|
|
("arr_nan_nan_inf", True),
|
|
]
|
|
|
|
for arr, correct in pairs:
|
|
val = getattr(self, arr)
|
|
self.check_bool(nanops._has_infs, val, correct)
|
|
|
|
for arr, correct in pairs_float:
|
|
val = getattr(self, arr)
|
|
self.check_bool(nanops._has_infs, val, correct)
|
|
self.check_bool(nanops._has_infs, val.astype("f4"), correct)
|
|
self.check_bool(nanops._has_infs, val.astype("f2"), correct)
|
|
|
|
def test__bn_ok_dtype(self):
|
|
assert nanops._bn_ok_dtype(self.arr_float.dtype, "test")
|
|
assert nanops._bn_ok_dtype(self.arr_complex.dtype, "test")
|
|
assert nanops._bn_ok_dtype(self.arr_int.dtype, "test")
|
|
assert nanops._bn_ok_dtype(self.arr_bool.dtype, "test")
|
|
assert nanops._bn_ok_dtype(self.arr_str.dtype, "test")
|
|
assert nanops._bn_ok_dtype(self.arr_utf.dtype, "test")
|
|
assert not nanops._bn_ok_dtype(self.arr_date.dtype, "test")
|
|
assert not nanops._bn_ok_dtype(self.arr_tdelta.dtype, "test")
|
|
assert not nanops._bn_ok_dtype(self.arr_obj.dtype, "test")
|
|
|
|
|
|
class TestEnsureNumeric:
|
|
def test_numeric_values(self):
|
|
# Test integer
|
|
assert nanops._ensure_numeric(1) == 1
|
|
|
|
# Test float
|
|
assert nanops._ensure_numeric(1.1) == 1.1
|
|
|
|
# Test complex
|
|
assert nanops._ensure_numeric(1 + 2j) == 1 + 2j
|
|
|
|
def test_ndarray(self):
|
|
# Test numeric ndarray
|
|
values = np.array([1, 2, 3])
|
|
assert np.allclose(nanops._ensure_numeric(values), values)
|
|
|
|
# Test object ndarray
|
|
o_values = values.astype(object)
|
|
assert np.allclose(nanops._ensure_numeric(o_values), values)
|
|
|
|
# Test convertible string ndarray
|
|
s_values = np.array(["1", "2", "3"], dtype=object)
|
|
assert np.allclose(nanops._ensure_numeric(s_values), values)
|
|
|
|
# Test non-convertible string ndarray
|
|
s_values = np.array(["foo", "bar", "baz"], dtype=object)
|
|
msg = r"Could not convert .* to numeric"
|
|
with pytest.raises(TypeError, match=msg):
|
|
nanops._ensure_numeric(s_values)
|
|
|
|
def test_convertable_values(self):
|
|
assert np.allclose(nanops._ensure_numeric("1"), 1.0)
|
|
assert np.allclose(nanops._ensure_numeric("1.1"), 1.1)
|
|
assert np.allclose(nanops._ensure_numeric("1+1j"), 1 + 1j)
|
|
|
|
def test_non_convertable_values(self):
|
|
msg = "Could not convert foo to numeric"
|
|
with pytest.raises(TypeError, match=msg):
|
|
nanops._ensure_numeric("foo")
|
|
|
|
# with the wrong type, python raises TypeError for us
|
|
msg = "argument must be a string or a number"
|
|
with pytest.raises(TypeError, match=msg):
|
|
nanops._ensure_numeric({})
|
|
with pytest.raises(TypeError, match=msg):
|
|
nanops._ensure_numeric([])
|
|
|
|
|
|
class TestNanvarFixedValues:
|
|
|
|
# xref GH10242
|
|
|
|
def setup_method(self, method):
|
|
# Samples from a normal distribution.
|
|
self.variance = variance = 3.0
|
|
self.samples = self.prng.normal(scale=variance**0.5, size=100000)
|
|
|
|
def test_nanvar_all_finite(self):
|
|
samples = self.samples
|
|
actual_variance = nanops.nanvar(samples)
|
|
tm.assert_almost_equal(actual_variance, self.variance, rtol=1e-2)
|
|
|
|
def test_nanvar_nans(self):
|
|
samples = np.nan * np.ones(2 * self.samples.shape[0])
|
|
samples[::2] = self.samples
|
|
|
|
actual_variance = nanops.nanvar(samples, skipna=True)
|
|
tm.assert_almost_equal(actual_variance, self.variance, rtol=1e-2)
|
|
|
|
actual_variance = nanops.nanvar(samples, skipna=False)
|
|
tm.assert_almost_equal(actual_variance, np.nan, rtol=1e-2)
|
|
|
|
def test_nanstd_nans(self):
|
|
samples = np.nan * np.ones(2 * self.samples.shape[0])
|
|
samples[::2] = self.samples
|
|
|
|
actual_std = nanops.nanstd(samples, skipna=True)
|
|
tm.assert_almost_equal(actual_std, self.variance**0.5, rtol=1e-2)
|
|
|
|
actual_std = nanops.nanvar(samples, skipna=False)
|
|
tm.assert_almost_equal(actual_std, np.nan, rtol=1e-2)
|
|
|
|
def test_nanvar_axis(self):
|
|
# Generate some sample data.
|
|
samples_norm = self.samples
|
|
samples_unif = self.prng.uniform(size=samples_norm.shape[0])
|
|
samples = np.vstack([samples_norm, samples_unif])
|
|
|
|
actual_variance = nanops.nanvar(samples, axis=1)
|
|
tm.assert_almost_equal(
|
|
actual_variance, np.array([self.variance, 1.0 / 12]), rtol=1e-2
|
|
)
|
|
|
|
def test_nanvar_ddof(self):
|
|
n = 5
|
|
samples = self.prng.uniform(size=(10000, n + 1))
|
|
samples[:, -1] = np.nan # Force use of our own algorithm.
|
|
|
|
variance_0 = nanops.nanvar(samples, axis=1, skipna=True, ddof=0).mean()
|
|
variance_1 = nanops.nanvar(samples, axis=1, skipna=True, ddof=1).mean()
|
|
variance_2 = nanops.nanvar(samples, axis=1, skipna=True, ddof=2).mean()
|
|
|
|
# The unbiased estimate.
|
|
var = 1.0 / 12
|
|
tm.assert_almost_equal(variance_1, var, rtol=1e-2)
|
|
|
|
# The underestimated variance.
|
|
tm.assert_almost_equal(variance_0, (n - 1.0) / n * var, rtol=1e-2)
|
|
|
|
# The overestimated variance.
|
|
tm.assert_almost_equal(variance_2, (n - 1.0) / (n - 2.0) * var, rtol=1e-2)
|
|
|
|
@pytest.mark.parametrize("axis", range(2))
|
|
@pytest.mark.parametrize("ddof", range(3))
|
|
def test_ground_truth(self, axis, ddof):
|
|
# Test against values that were precomputed with Numpy.
|
|
samples = np.empty((4, 4))
|
|
samples[:3, :3] = np.array(
|
|
[
|
|
[0.97303362, 0.21869576, 0.55560287],
|
|
[0.72980153, 0.03109364, 0.99155171],
|
|
[0.09317602, 0.60078248, 0.15871292],
|
|
]
|
|
)
|
|
samples[3] = samples[:, 3] = np.nan
|
|
|
|
# Actual variances along axis=0, 1 for ddof=0, 1, 2
|
|
variance = np.array(
|
|
[
|
|
[
|
|
[0.13762259, 0.05619224, 0.11568816],
|
|
[0.20643388, 0.08428837, 0.17353224],
|
|
[0.41286776, 0.16857673, 0.34706449],
|
|
],
|
|
[
|
|
[0.09519783, 0.16435395, 0.05082054],
|
|
[0.14279674, 0.24653093, 0.07623082],
|
|
[0.28559348, 0.49306186, 0.15246163],
|
|
],
|
|
]
|
|
)
|
|
|
|
# Test nanvar.
|
|
var = nanops.nanvar(samples, skipna=True, axis=axis, ddof=ddof)
|
|
tm.assert_almost_equal(var[:3], variance[axis, ddof])
|
|
assert np.isnan(var[3])
|
|
|
|
# Test nanstd.
|
|
std = nanops.nanstd(samples, skipna=True, axis=axis, ddof=ddof)
|
|
tm.assert_almost_equal(std[:3], variance[axis, ddof] ** 0.5)
|
|
assert np.isnan(std[3])
|
|
|
|
@pytest.mark.parametrize("ddof", range(3))
|
|
def test_nanstd_roundoff(self, ddof):
|
|
# Regression test for GH 10242 (test data taken from GH 10489). Ensure
|
|
# that variance is stable.
|
|
data = Series(766897346 * np.ones(10))
|
|
result = data.std(ddof=ddof)
|
|
assert result == 0.0
|
|
|
|
@property
|
|
def prng(self):
|
|
return np.random.RandomState(1234)
|
|
|
|
|
|
class TestNanskewFixedValues:
|
|
|
|
# xref GH 11974
|
|
|
|
def setup_method(self, method):
|
|
# Test data + skewness value (computed with scipy.stats.skew)
|
|
self.samples = np.sin(np.linspace(0, 1, 200))
|
|
self.actual_skew = -0.1875895205961754
|
|
|
|
def test_constant_series(self):
|
|
# xref GH 11974
|
|
for val in [3075.2, 3075.3, 3075.5]:
|
|
data = val * np.ones(300)
|
|
skew = nanops.nanskew(data)
|
|
assert skew == 0.0
|
|
|
|
def test_all_finite(self):
|
|
alpha, beta = 0.3, 0.1
|
|
left_tailed = self.prng.beta(alpha, beta, size=100)
|
|
assert nanops.nanskew(left_tailed) < 0
|
|
|
|
alpha, beta = 0.1, 0.3
|
|
right_tailed = self.prng.beta(alpha, beta, size=100)
|
|
assert nanops.nanskew(right_tailed) > 0
|
|
|
|
def test_ground_truth(self):
|
|
skew = nanops.nanskew(self.samples)
|
|
tm.assert_almost_equal(skew, self.actual_skew)
|
|
|
|
def test_axis(self):
|
|
samples = np.vstack([self.samples, np.nan * np.ones(len(self.samples))])
|
|
skew = nanops.nanskew(samples, axis=1)
|
|
tm.assert_almost_equal(skew, np.array([self.actual_skew, np.nan]))
|
|
|
|
def test_nans(self):
|
|
samples = np.hstack([self.samples, np.nan])
|
|
skew = nanops.nanskew(samples, skipna=False)
|
|
assert np.isnan(skew)
|
|
|
|
def test_nans_skipna(self):
|
|
samples = np.hstack([self.samples, np.nan])
|
|
skew = nanops.nanskew(samples, skipna=True)
|
|
tm.assert_almost_equal(skew, self.actual_skew)
|
|
|
|
@property
|
|
def prng(self):
|
|
return np.random.RandomState(1234)
|
|
|
|
|
|
class TestNankurtFixedValues:
|
|
|
|
# xref GH 11974
|
|
|
|
def setup_method(self, method):
|
|
# Test data + kurtosis value (computed with scipy.stats.kurtosis)
|
|
self.samples = np.sin(np.linspace(0, 1, 200))
|
|
self.actual_kurt = -1.2058303433799713
|
|
|
|
@pytest.mark.parametrize("val", [3075.2, 3075.3, 3075.5])
|
|
def test_constant_series(self, val):
|
|
# xref GH 11974
|
|
data = val * np.ones(300)
|
|
kurt = nanops.nankurt(data)
|
|
assert kurt == 0.0
|
|
|
|
def test_all_finite(self):
|
|
alpha, beta = 0.3, 0.1
|
|
left_tailed = self.prng.beta(alpha, beta, size=100)
|
|
assert nanops.nankurt(left_tailed) < 0
|
|
|
|
alpha, beta = 0.1, 0.3
|
|
right_tailed = self.prng.beta(alpha, beta, size=100)
|
|
assert nanops.nankurt(right_tailed) > 0
|
|
|
|
def test_ground_truth(self):
|
|
kurt = nanops.nankurt(self.samples)
|
|
tm.assert_almost_equal(kurt, self.actual_kurt)
|
|
|
|
def test_axis(self):
|
|
samples = np.vstack([self.samples, np.nan * np.ones(len(self.samples))])
|
|
kurt = nanops.nankurt(samples, axis=1)
|
|
tm.assert_almost_equal(kurt, np.array([self.actual_kurt, np.nan]))
|
|
|
|
def test_nans(self):
|
|
samples = np.hstack([self.samples, np.nan])
|
|
kurt = nanops.nankurt(samples, skipna=False)
|
|
assert np.isnan(kurt)
|
|
|
|
def test_nans_skipna(self):
|
|
samples = np.hstack([self.samples, np.nan])
|
|
kurt = nanops.nankurt(samples, skipna=True)
|
|
tm.assert_almost_equal(kurt, self.actual_kurt)
|
|
|
|
@property
|
|
def prng(self):
|
|
return np.random.RandomState(1234)
|
|
|
|
|
|
class TestDatetime64NaNOps:
|
|
# Enabling mean changes the behavior of DataFrame.mean
|
|
# See https://github.com/pandas-dev/pandas/issues/24752
|
|
def test_nanmean(self):
|
|
dti = pd.date_range("2016-01-01", periods=3)
|
|
expected = dti[1]
|
|
|
|
for obj in [dti, DatetimeArray(dti), Series(dti)]:
|
|
result = nanops.nanmean(obj)
|
|
assert result == expected
|
|
|
|
dti2 = dti.insert(1, pd.NaT)
|
|
|
|
for obj in [dti2, DatetimeArray(dti2), Series(dti2)]:
|
|
result = nanops.nanmean(obj)
|
|
assert result == expected
|
|
|
|
@pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
|
|
def test_nanmean_skipna_false(self, dtype):
|
|
arr = np.arange(12).astype(np.int64).view(dtype).reshape(4, 3)
|
|
|
|
arr[-1, -1] = "NaT"
|
|
|
|
result = nanops.nanmean(arr, skipna=False)
|
|
assert result is pd.NaT
|
|
|
|
result = nanops.nanmean(arr, axis=0, skipna=False)
|
|
expected = np.array([4, 5, "NaT"], dtype=arr.dtype)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
result = nanops.nanmean(arr, axis=1, skipna=False)
|
|
expected = np.array([arr[0, 1], arr[1, 1], arr[2, 1], arr[-1, -1]])
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
def test_use_bottleneck():
|
|
|
|
if nanops._BOTTLENECK_INSTALLED:
|
|
|
|
pd.set_option("use_bottleneck", True)
|
|
assert pd.get_option("use_bottleneck")
|
|
|
|
pd.set_option("use_bottleneck", False)
|
|
assert not pd.get_option("use_bottleneck")
|
|
|
|
pd.set_option("use_bottleneck", use_bn)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"numpy_op, expected",
|
|
[
|
|
(np.sum, 10),
|
|
(np.nansum, 10),
|
|
(np.mean, 2.5),
|
|
(np.nanmean, 2.5),
|
|
(np.median, 2.5),
|
|
(np.nanmedian, 2.5),
|
|
(np.min, 1),
|
|
(np.max, 4),
|
|
(np.nanmin, 1),
|
|
(np.nanmax, 4),
|
|
],
|
|
)
|
|
def test_numpy_ops(numpy_op, expected):
|
|
# GH8383
|
|
result = numpy_op(Series([1, 2, 3, 4]))
|
|
assert result == expected
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"operation",
|
|
[
|
|
nanops.nanany,
|
|
nanops.nanall,
|
|
nanops.nansum,
|
|
nanops.nanmean,
|
|
nanops.nanmedian,
|
|
nanops.nanstd,
|
|
nanops.nanvar,
|
|
nanops.nansem,
|
|
nanops.nanargmax,
|
|
nanops.nanargmin,
|
|
nanops.nanmax,
|
|
nanops.nanmin,
|
|
nanops.nanskew,
|
|
nanops.nankurt,
|
|
nanops.nanprod,
|
|
],
|
|
)
|
|
def test_nanops_independent_of_mask_param(operation):
|
|
# GH22764
|
|
s = Series([1, 2, np.nan, 3, np.nan, 4])
|
|
mask = s.isna()
|
|
median_expected = operation(s)
|
|
median_result = operation(s, mask=mask)
|
|
assert median_expected == median_result
|
|
|