1749 lines
50 KiB
Python
1749 lines
50 KiB
Python
from __future__ import annotations
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import functools
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import itertools
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from typing import (
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Any,
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Callable,
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cast,
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)
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import warnings
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import numpy as np
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from pandas._config import get_option
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from pandas._libs import (
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NaT,
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NaTType,
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iNaT,
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lib,
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)
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from pandas._typing import (
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ArrayLike,
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AxisInt,
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CorrelationMethod,
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Dtype,
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DtypeObj,
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F,
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Scalar,
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Shape,
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npt,
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)
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from pandas.compat._optional import import_optional_dependency
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from pandas.util._exceptions import find_stack_level
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from pandas.core.dtypes.common import (
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is_complex,
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is_float,
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is_float_dtype,
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is_integer,
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is_numeric_dtype,
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is_object_dtype,
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needs_i8_conversion,
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pandas_dtype,
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)
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from pandas.core.dtypes.missing import (
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isna,
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na_value_for_dtype,
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notna,
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)
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bn = import_optional_dependency("bottleneck", errors="warn")
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_BOTTLENECK_INSTALLED = bn is not None
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_USE_BOTTLENECK = False
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def set_use_bottleneck(v: bool = True) -> None:
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# set/unset to use bottleneck
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global _USE_BOTTLENECK
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if _BOTTLENECK_INSTALLED:
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_USE_BOTTLENECK = v
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set_use_bottleneck(get_option("compute.use_bottleneck"))
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class disallow:
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def __init__(self, *dtypes: Dtype) -> None:
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super().__init__()
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self.dtypes = tuple(pandas_dtype(dtype).type for dtype in dtypes)
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def check(self, obj) -> bool:
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return hasattr(obj, "dtype") and issubclass(obj.dtype.type, self.dtypes)
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def __call__(self, f: F) -> F:
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@functools.wraps(f)
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def _f(*args, **kwargs):
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obj_iter = itertools.chain(args, kwargs.values())
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if any(self.check(obj) for obj in obj_iter):
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f_name = f.__name__.replace("nan", "")
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raise TypeError(
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f"reduction operation '{f_name}' not allowed for this dtype"
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)
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try:
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return f(*args, **kwargs)
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except ValueError as e:
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# we want to transform an object array
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# ValueError message to the more typical TypeError
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# e.g. this is normally a disallowed function on
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# object arrays that contain strings
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if is_object_dtype(args[0]):
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raise TypeError(e) from e
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raise
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return cast(F, _f)
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class bottleneck_switch:
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def __init__(self, name=None, **kwargs) -> None:
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self.name = name
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self.kwargs = kwargs
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def __call__(self, alt: F) -> F:
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bn_name = self.name or alt.__name__
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try:
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bn_func = getattr(bn, bn_name)
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except (AttributeError, NameError): # pragma: no cover
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bn_func = None
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@functools.wraps(alt)
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def f(
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values: np.ndarray,
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*,
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axis: AxisInt | None = None,
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skipna: bool = True,
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**kwds,
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):
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if len(self.kwargs) > 0:
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for k, v in self.kwargs.items():
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if k not in kwds:
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kwds[k] = v
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if values.size == 0 and kwds.get("min_count") is None:
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# We are empty, returning NA for our type
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# Only applies for the default `min_count` of None
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# since that affects how empty arrays are handled.
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# TODO(GH-18976) update all the nanops methods to
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# correctly handle empty inputs and remove this check.
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# It *may* just be `var`
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return _na_for_min_count(values, axis)
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if _USE_BOTTLENECK and skipna and _bn_ok_dtype(values.dtype, bn_name):
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if kwds.get("mask", None) is None:
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# `mask` is not recognised by bottleneck, would raise
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# TypeError if called
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kwds.pop("mask", None)
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result = bn_func(values, axis=axis, **kwds)
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# prefer to treat inf/-inf as NA, but must compute the func
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# twice :(
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if _has_infs(result):
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result = alt(values, axis=axis, skipna=skipna, **kwds)
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else:
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result = alt(values, axis=axis, skipna=skipna, **kwds)
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else:
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result = alt(values, axis=axis, skipna=skipna, **kwds)
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return result
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return cast(F, f)
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def _bn_ok_dtype(dtype: DtypeObj, name: str) -> bool:
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# Bottleneck chokes on datetime64, PeriodDtype (or and EA)
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if dtype != object and not needs_i8_conversion(dtype):
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# GH 42878
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# Bottleneck uses naive summation leading to O(n) loss of precision
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# unlike numpy which implements pairwise summation, which has O(log(n)) loss
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# crossref: https://github.com/pydata/bottleneck/issues/379
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# GH 15507
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# bottleneck does not properly upcast during the sum
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# so can overflow
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# GH 9422
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# further we also want to preserve NaN when all elements
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# are NaN, unlike bottleneck/numpy which consider this
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# to be 0
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return name not in ["nansum", "nanprod", "nanmean"]
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return False
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def _has_infs(result) -> bool:
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if isinstance(result, np.ndarray):
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if result.dtype in ("f8", "f4"):
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# Note: outside of an nanops-specific test, we always have
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# result.ndim == 1, so there is no risk of this ravel making a copy.
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return lib.has_infs(result.ravel("K"))
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try:
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return np.isinf(result).any()
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except (TypeError, NotImplementedError):
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# if it doesn't support infs, then it can't have infs
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return False
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def _get_fill_value(
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dtype: DtypeObj, fill_value: Scalar | None = None, fill_value_typ=None
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):
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"""return the correct fill value for the dtype of the values"""
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if fill_value is not None:
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return fill_value
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if _na_ok_dtype(dtype):
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if fill_value_typ is None:
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return np.nan
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else:
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if fill_value_typ == "+inf":
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return np.inf
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else:
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return -np.inf
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else:
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if fill_value_typ == "+inf":
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# need the max int here
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return lib.i8max
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else:
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return iNaT
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def _maybe_get_mask(
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values: np.ndarray, skipna: bool, mask: npt.NDArray[np.bool_] | None
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) -> npt.NDArray[np.bool_] | None:
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"""
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Compute a mask if and only if necessary.
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This function will compute a mask iff it is necessary. Otherwise,
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return the provided mask (potentially None) when a mask does not need to be
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computed.
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A mask is never necessary if the values array is of boolean or integer
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dtypes, as these are incapable of storing NaNs. If passing a NaN-capable
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dtype that is interpretable as either boolean or integer data (eg,
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timedelta64), a mask must be provided.
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If the skipna parameter is False, a new mask will not be computed.
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The mask is computed using isna() by default. Setting invert=True selects
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notna() as the masking function.
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Parameters
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----------
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values : ndarray
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input array to potentially compute mask for
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skipna : bool
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boolean for whether NaNs should be skipped
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mask : Optional[ndarray]
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nan-mask if known
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Returns
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-------
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Optional[np.ndarray[bool]]
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"""
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if mask is None:
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if values.dtype.kind in "biu":
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# Boolean data cannot contain nulls, so signal via mask being None
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return None
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if skipna or values.dtype.kind in "mM":
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mask = isna(values)
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return mask
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def _get_values(
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values: np.ndarray,
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skipna: bool,
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fill_value: Any = None,
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fill_value_typ: str | None = None,
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mask: npt.NDArray[np.bool_] | None = None,
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) -> tuple[np.ndarray, npt.NDArray[np.bool_] | None]:
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"""
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Utility to get the values view, mask, dtype, dtype_max, and fill_value.
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If both mask and fill_value/fill_value_typ are not None and skipna is True,
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the values array will be copied.
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For input arrays of boolean or integer dtypes, copies will only occur if a
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precomputed mask, a fill_value/fill_value_typ, and skipna=True are
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provided.
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Parameters
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----------
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values : ndarray
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input array to potentially compute mask for
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skipna : bool
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boolean for whether NaNs should be skipped
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fill_value : Any
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value to fill NaNs with
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fill_value_typ : str
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Set to '+inf' or '-inf' to handle dtype-specific infinities
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mask : Optional[np.ndarray[bool]]
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nan-mask if known
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Returns
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-------
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values : ndarray
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Potential copy of input value array
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mask : Optional[ndarray[bool]]
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Mask for values, if deemed necessary to compute
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"""
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# In _get_values is only called from within nanops, and in all cases
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# with scalar fill_value. This guarantee is important for the
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# np.where call below
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mask = _maybe_get_mask(values, skipna, mask)
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dtype = values.dtype
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datetimelike = False
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if values.dtype.kind in "mM":
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# changing timedelta64/datetime64 to int64 needs to happen after
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# finding `mask` above
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values = np.asarray(values.view("i8"))
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datetimelike = True
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if skipna and (mask is not None):
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# get our fill value (in case we need to provide an alternative
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# dtype for it)
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fill_value = _get_fill_value(
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dtype, fill_value=fill_value, fill_value_typ=fill_value_typ
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)
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if fill_value is not None:
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if mask.any():
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if datetimelike or _na_ok_dtype(dtype):
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values = values.copy()
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np.putmask(values, mask, fill_value)
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else:
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# np.where will promote if needed
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values = np.where(~mask, values, fill_value)
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return values, mask
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def _get_dtype_max(dtype: np.dtype) -> np.dtype:
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# return a platform independent precision dtype
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dtype_max = dtype
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if dtype.kind in "bi":
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dtype_max = np.dtype(np.int64)
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elif dtype.kind == "u":
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dtype_max = np.dtype(np.uint64)
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elif dtype.kind == "f":
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dtype_max = np.dtype(np.float64)
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return dtype_max
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def _na_ok_dtype(dtype: DtypeObj) -> bool:
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if needs_i8_conversion(dtype):
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return False
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return not issubclass(dtype.type, np.integer)
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def _wrap_results(result, dtype: np.dtype, fill_value=None):
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"""wrap our results if needed"""
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if result is NaT:
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pass
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elif dtype.kind == "M":
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if fill_value is None:
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# GH#24293
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fill_value = iNaT
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if not isinstance(result, np.ndarray):
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assert not isna(fill_value), "Expected non-null fill_value"
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if result == fill_value:
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result = np.nan
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if isna(result):
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result = np.datetime64("NaT", "ns").astype(dtype)
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else:
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result = np.int64(result).view(dtype)
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# retain original unit
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result = result.astype(dtype, copy=False)
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else:
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# If we have float dtype, taking a view will give the wrong result
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result = result.astype(dtype)
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elif dtype.kind == "m":
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if not isinstance(result, np.ndarray):
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if result == fill_value or np.isnan(result):
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result = np.timedelta64("NaT").astype(dtype)
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elif np.fabs(result) > lib.i8max:
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# raise if we have a timedelta64[ns] which is too large
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raise ValueError("overflow in timedelta operation")
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else:
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# return a timedelta64 with the original unit
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result = np.int64(result).astype(dtype, copy=False)
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else:
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result = result.astype("m8[ns]").view(dtype)
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return result
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def _datetimelike_compat(func: F) -> F:
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"""
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If we have datetime64 or timedelta64 values, ensure we have a correct
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mask before calling the wrapped function, then cast back afterwards.
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"""
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@functools.wraps(func)
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def new_func(
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values: np.ndarray,
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*,
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axis: AxisInt | None = None,
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skipna: bool = True,
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mask: npt.NDArray[np.bool_] | None = None,
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**kwargs,
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):
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orig_values = values
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datetimelike = values.dtype.kind in "mM"
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if datetimelike and mask is None:
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mask = isna(values)
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result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)
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if datetimelike:
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result = _wrap_results(result, orig_values.dtype, fill_value=iNaT)
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if not skipna:
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assert mask is not None # checked above
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result = _mask_datetimelike_result(result, axis, mask, orig_values)
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return result
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return cast(F, new_func)
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def _na_for_min_count(values: np.ndarray, axis: AxisInt | None) -> Scalar | np.ndarray:
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"""
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Return the missing value for `values`.
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Parameters
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----------
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values : ndarray
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axis : int or None
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axis for the reduction, required if values.ndim > 1.
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Returns
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-------
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result : scalar or ndarray
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For 1-D values, returns a scalar of the correct missing type.
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For 2-D values, returns a 1-D array where each element is missing.
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"""
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# we either return np.nan or pd.NaT
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if values.dtype.kind in "iufcb":
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values = values.astype("float64")
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fill_value = na_value_for_dtype(values.dtype)
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if values.ndim == 1:
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return fill_value
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elif axis is None:
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return fill_value
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else:
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result_shape = values.shape[:axis] + values.shape[axis + 1 :]
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return np.full(result_shape, fill_value, dtype=values.dtype)
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|
|
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def maybe_operate_rowwise(func: F) -> F:
|
|
"""
|
|
NumPy operations on C-contiguous ndarrays with axis=1 can be
|
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very slow if axis 1 >> axis 0.
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Operate row-by-row and concatenate the results.
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"""
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|
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@functools.wraps(func)
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def newfunc(values: np.ndarray, *, axis: AxisInt | None = None, **kwargs):
|
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if (
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axis == 1
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and values.ndim == 2
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and values.flags["C_CONTIGUOUS"]
|
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# only takes this path for wide arrays (long dataframes), for threshold see
|
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# https://github.com/pandas-dev/pandas/pull/43311#issuecomment-974891737
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and (values.shape[1] / 1000) > values.shape[0]
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and values.dtype != object
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and values.dtype != bool
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):
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arrs = list(values)
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if kwargs.get("mask") is not None:
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mask = kwargs.pop("mask")
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results = [
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func(arrs[i], mask=mask[i], **kwargs) for i in range(len(arrs))
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]
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else:
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results = [func(x, **kwargs) for x in arrs]
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return np.array(results)
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|
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return func(values, axis=axis, **kwargs)
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|
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return cast(F, newfunc)
|
|
|
|
|
|
def nanany(
|
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values: np.ndarray,
|
|
*,
|
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axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> bool:
|
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"""
|
|
Check if any elements along an axis evaluate to True.
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
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axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : bool
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
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>>> s = pd.Series([1, 2])
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>>> nanops.nanany(s.values)
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True
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|
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>>> from pandas.core import nanops
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>>> s = pd.Series([np.nan])
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>>> nanops.nanany(s.values)
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False
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"""
|
|
if values.dtype.kind in "iub" and mask is None:
|
|
# GH#26032 fastpath
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|
# error: Incompatible return value type (got "Union[bool_, ndarray]",
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|
# expected "bool")
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return values.any(axis) # type: ignore[return-value]
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|
|
|
if values.dtype.kind == "M":
|
|
# GH#34479
|
|
warnings.warn(
|
|
"'any' with datetime64 dtypes is deprecated and will raise in a "
|
|
"future version. Use (obj != pd.Timestamp(0)).any() instead.",
|
|
FutureWarning,
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|
stacklevel=find_stack_level(),
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|
)
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|
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values, _ = _get_values(values, skipna, fill_value=False, mask=mask)
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|
|
# For object type, any won't necessarily return
|
|
# boolean values (numpy/numpy#4352)
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|
if values.dtype == object:
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values = values.astype(bool)
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|
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# error: Incompatible return value type (got "Union[bool_, ndarray]", expected
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# "bool")
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return values.any(axis) # type: ignore[return-value]
|
|
|
|
|
|
def nanall(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> bool:
|
|
"""
|
|
Check if all elements along an axis evaluate to True.
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : bool
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, 2, np.nan])
|
|
>>> nanops.nanall(s.values)
|
|
True
|
|
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, 0])
|
|
>>> nanops.nanall(s.values)
|
|
False
|
|
"""
|
|
if values.dtype.kind in "iub" and mask is None:
|
|
# GH#26032 fastpath
|
|
# error: Incompatible return value type (got "Union[bool_, ndarray]",
|
|
# expected "bool")
|
|
return values.all(axis) # type: ignore[return-value]
|
|
|
|
if values.dtype.kind == "M":
|
|
# GH#34479
|
|
warnings.warn(
|
|
"'all' with datetime64 dtypes is deprecated and will raise in a "
|
|
"future version. Use (obj != pd.Timestamp(0)).all() instead.",
|
|
FutureWarning,
|
|
stacklevel=find_stack_level(),
|
|
)
|
|
|
|
values, _ = _get_values(values, skipna, fill_value=True, mask=mask)
|
|
|
|
# For object type, all won't necessarily return
|
|
# boolean values (numpy/numpy#4352)
|
|
if values.dtype == object:
|
|
values = values.astype(bool)
|
|
|
|
# error: Incompatible return value type (got "Union[bool_, ndarray]", expected
|
|
# "bool")
|
|
return values.all(axis) # type: ignore[return-value]
|
|
|
|
|
|
@disallow("M8")
|
|
@_datetimelike_compat
|
|
@maybe_operate_rowwise
|
|
def nansum(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
min_count: int = 0,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> float:
|
|
"""
|
|
Sum the elements along an axis ignoring NaNs
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray[dtype]
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
min_count: int, default 0
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : dtype
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, 2, np.nan])
|
|
>>> nanops.nansum(s.values)
|
|
3.0
|
|
"""
|
|
dtype = values.dtype
|
|
values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
|
|
dtype_sum = _get_dtype_max(dtype)
|
|
if dtype.kind == "f":
|
|
dtype_sum = dtype
|
|
elif dtype.kind == "m":
|
|
dtype_sum = np.dtype(np.float64)
|
|
|
|
the_sum = values.sum(axis, dtype=dtype_sum)
|
|
the_sum = _maybe_null_out(the_sum, axis, mask, values.shape, min_count=min_count)
|
|
|
|
return the_sum
|
|
|
|
|
|
def _mask_datetimelike_result(
|
|
result: np.ndarray | np.datetime64 | np.timedelta64,
|
|
axis: AxisInt | None,
|
|
mask: npt.NDArray[np.bool_],
|
|
orig_values: np.ndarray,
|
|
) -> np.ndarray | np.datetime64 | np.timedelta64 | NaTType:
|
|
if isinstance(result, np.ndarray):
|
|
# we need to apply the mask
|
|
result = result.astype("i8").view(orig_values.dtype)
|
|
axis_mask = mask.any(axis=axis)
|
|
# error: Unsupported target for indexed assignment ("Union[ndarray[Any, Any],
|
|
# datetime64, timedelta64]")
|
|
result[axis_mask] = iNaT # type: ignore[index]
|
|
else:
|
|
if mask.any():
|
|
return np.int64(iNaT).view(orig_values.dtype)
|
|
return result
|
|
|
|
|
|
@bottleneck_switch()
|
|
@_datetimelike_compat
|
|
def nanmean(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> float:
|
|
"""
|
|
Compute the mean of the element along an axis ignoring NaNs
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
float
|
|
Unless input is a float array, in which case use the same
|
|
precision as the input array.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, 2, np.nan])
|
|
>>> nanops.nanmean(s.values)
|
|
1.5
|
|
"""
|
|
dtype = values.dtype
|
|
values, mask = _get_values(values, skipna, fill_value=0, mask=mask)
|
|
dtype_sum = _get_dtype_max(dtype)
|
|
dtype_count = np.dtype(np.float64)
|
|
|
|
# not using needs_i8_conversion because that includes period
|
|
if dtype.kind in "mM":
|
|
dtype_sum = np.dtype(np.float64)
|
|
elif dtype.kind in "iu":
|
|
dtype_sum = np.dtype(np.float64)
|
|
elif dtype.kind == "f":
|
|
dtype_sum = dtype
|
|
dtype_count = dtype
|
|
|
|
count = _get_counts(values.shape, mask, axis, dtype=dtype_count)
|
|
the_sum = values.sum(axis, dtype=dtype_sum)
|
|
the_sum = _ensure_numeric(the_sum)
|
|
|
|
if axis is not None and getattr(the_sum, "ndim", False):
|
|
count = cast(np.ndarray, count)
|
|
with np.errstate(all="ignore"):
|
|
# suppress division by zero warnings
|
|
the_mean = the_sum / count
|
|
ct_mask = count == 0
|
|
if ct_mask.any():
|
|
the_mean[ct_mask] = np.nan
|
|
else:
|
|
the_mean = the_sum / count if count > 0 else np.nan
|
|
|
|
return the_mean
|
|
|
|
|
|
@bottleneck_switch()
|
|
def nanmedian(values, *, axis: AxisInt | None = None, skipna: bool = True, mask=None):
|
|
"""
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : float
|
|
Unless input is a float array, in which case use the same
|
|
precision as the input array.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, np.nan, 2, 2])
|
|
>>> nanops.nanmedian(s.values)
|
|
2.0
|
|
"""
|
|
# for floats without mask, the data already uses NaN as missing value
|
|
# indicator, and `mask` will be calculated from that below -> in those
|
|
# cases we never need to set NaN to the masked values
|
|
using_nan_sentinel = values.dtype.kind == "f" and mask is None
|
|
|
|
def get_median(x, _mask=None):
|
|
if _mask is None:
|
|
_mask = notna(x)
|
|
else:
|
|
_mask = ~_mask
|
|
if not skipna and not _mask.all():
|
|
return np.nan
|
|
with warnings.catch_warnings():
|
|
# Suppress RuntimeWarning about All-NaN slice
|
|
warnings.filterwarnings(
|
|
"ignore", "All-NaN slice encountered", RuntimeWarning
|
|
)
|
|
res = np.nanmedian(x[_mask])
|
|
return res
|
|
|
|
dtype = values.dtype
|
|
values, mask = _get_values(values, skipna, mask=mask, fill_value=None)
|
|
if values.dtype.kind != "f":
|
|
if values.dtype == object:
|
|
# GH#34671 avoid casting strings to numeric
|
|
inferred = lib.infer_dtype(values)
|
|
if inferred in ["string", "mixed"]:
|
|
raise TypeError(f"Cannot convert {values} to numeric")
|
|
try:
|
|
values = values.astype("f8")
|
|
except ValueError as err:
|
|
# e.g. "could not convert string to float: 'a'"
|
|
raise TypeError(str(err)) from err
|
|
if not using_nan_sentinel and mask is not None:
|
|
if not values.flags.writeable:
|
|
values = values.copy()
|
|
values[mask] = np.nan
|
|
|
|
notempty = values.size
|
|
|
|
# an array from a frame
|
|
if values.ndim > 1 and axis is not None:
|
|
# there's a non-empty array to apply over otherwise numpy raises
|
|
if notempty:
|
|
if not skipna:
|
|
res = np.apply_along_axis(get_median, axis, values)
|
|
|
|
else:
|
|
# fastpath for the skipna case
|
|
with warnings.catch_warnings():
|
|
# Suppress RuntimeWarning about All-NaN slice
|
|
warnings.filterwarnings(
|
|
"ignore", "All-NaN slice encountered", RuntimeWarning
|
|
)
|
|
if (values.shape[1] == 1 and axis == 0) or (
|
|
values.shape[0] == 1 and axis == 1
|
|
):
|
|
# GH52788: fastpath when squeezable, nanmedian for 2D array slow
|
|
res = np.nanmedian(np.squeeze(values), keepdims=True)
|
|
else:
|
|
res = np.nanmedian(values, axis=axis)
|
|
|
|
else:
|
|
# must return the correct shape, but median is not defined for the
|
|
# empty set so return nans of shape "everything but the passed axis"
|
|
# since "axis" is where the reduction would occur if we had a nonempty
|
|
# array
|
|
res = _get_empty_reduction_result(values.shape, axis)
|
|
|
|
else:
|
|
# otherwise return a scalar value
|
|
res = get_median(values, mask) if notempty else np.nan
|
|
return _wrap_results(res, dtype)
|
|
|
|
|
|
def _get_empty_reduction_result(
|
|
shape: Shape,
|
|
axis: AxisInt,
|
|
) -> np.ndarray:
|
|
"""
|
|
The result from a reduction on an empty ndarray.
|
|
|
|
Parameters
|
|
----------
|
|
shape : Tuple[int, ...]
|
|
axis : int
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray
|
|
"""
|
|
shp = np.array(shape)
|
|
dims = np.arange(len(shape))
|
|
ret = np.empty(shp[dims != axis], dtype=np.float64)
|
|
ret.fill(np.nan)
|
|
return ret
|
|
|
|
|
|
def _get_counts_nanvar(
|
|
values_shape: Shape,
|
|
mask: npt.NDArray[np.bool_] | None,
|
|
axis: AxisInt | None,
|
|
ddof: int,
|
|
dtype: np.dtype = np.dtype(np.float64),
|
|
) -> tuple[float | np.ndarray, float | np.ndarray]:
|
|
"""
|
|
Get the count of non-null values along an axis, accounting
|
|
for degrees of freedom.
|
|
|
|
Parameters
|
|
----------
|
|
values_shape : Tuple[int, ...]
|
|
shape tuple from values ndarray, used if mask is None
|
|
mask : Optional[ndarray[bool]]
|
|
locations in values that should be considered missing
|
|
axis : Optional[int]
|
|
axis to count along
|
|
ddof : int
|
|
degrees of freedom
|
|
dtype : type, optional
|
|
type to use for count
|
|
|
|
Returns
|
|
-------
|
|
count : int, np.nan or np.ndarray
|
|
d : int, np.nan or np.ndarray
|
|
"""
|
|
count = _get_counts(values_shape, mask, axis, dtype=dtype)
|
|
d = count - dtype.type(ddof)
|
|
|
|
# always return NaN, never inf
|
|
if is_float(count):
|
|
if count <= ddof:
|
|
# error: Incompatible types in assignment (expression has type
|
|
# "float", variable has type "Union[floating[Any], ndarray[Any,
|
|
# dtype[floating[Any]]]]")
|
|
count = np.nan # type: ignore[assignment]
|
|
d = np.nan
|
|
else:
|
|
# count is not narrowed by is_float check
|
|
count = cast(np.ndarray, count)
|
|
mask = count <= ddof
|
|
if mask.any():
|
|
np.putmask(d, mask, np.nan)
|
|
np.putmask(count, mask, np.nan)
|
|
return count, d
|
|
|
|
|
|
@bottleneck_switch(ddof=1)
|
|
def nanstd(
|
|
values,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
ddof: int = 1,
|
|
mask=None,
|
|
):
|
|
"""
|
|
Compute the standard deviation along given axis while ignoring NaNs
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
ddof : int, default 1
|
|
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
|
|
where N represents the number of elements.
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : float
|
|
Unless input is a float array, in which case use the same
|
|
precision as the input array.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, np.nan, 2, 3])
|
|
>>> nanops.nanstd(s.values)
|
|
1.0
|
|
"""
|
|
if values.dtype == "M8[ns]":
|
|
values = values.view("m8[ns]")
|
|
|
|
orig_dtype = values.dtype
|
|
values, mask = _get_values(values, skipna, mask=mask)
|
|
|
|
result = np.sqrt(nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask))
|
|
return _wrap_results(result, orig_dtype)
|
|
|
|
|
|
@disallow("M8", "m8")
|
|
@bottleneck_switch(ddof=1)
|
|
def nanvar(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
ddof: int = 1,
|
|
mask=None,
|
|
):
|
|
"""
|
|
Compute the variance along given axis while ignoring NaNs
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
ddof : int, default 1
|
|
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
|
|
where N represents the number of elements.
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : float
|
|
Unless input is a float array, in which case use the same
|
|
precision as the input array.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, np.nan, 2, 3])
|
|
>>> nanops.nanvar(s.values)
|
|
1.0
|
|
"""
|
|
dtype = values.dtype
|
|
mask = _maybe_get_mask(values, skipna, mask)
|
|
if dtype.kind in "iu":
|
|
values = values.astype("f8")
|
|
if mask is not None:
|
|
values[mask] = np.nan
|
|
|
|
if values.dtype.kind == "f":
|
|
count, d = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
|
|
else:
|
|
count, d = _get_counts_nanvar(values.shape, mask, axis, ddof)
|
|
|
|
if skipna and mask is not None:
|
|
values = values.copy()
|
|
np.putmask(values, mask, 0)
|
|
|
|
# xref GH10242
|
|
# Compute variance via two-pass algorithm, which is stable against
|
|
# cancellation errors and relatively accurate for small numbers of
|
|
# observations.
|
|
#
|
|
# See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
|
|
avg = _ensure_numeric(values.sum(axis=axis, dtype=np.float64)) / count
|
|
if axis is not None:
|
|
avg = np.expand_dims(avg, axis)
|
|
sqr = _ensure_numeric((avg - values) ** 2)
|
|
if mask is not None:
|
|
np.putmask(sqr, mask, 0)
|
|
result = sqr.sum(axis=axis, dtype=np.float64) / d
|
|
|
|
# Return variance as np.float64 (the datatype used in the accumulator),
|
|
# unless we were dealing with a float array, in which case use the same
|
|
# precision as the original values array.
|
|
if dtype.kind == "f":
|
|
result = result.astype(dtype, copy=False)
|
|
return result
|
|
|
|
|
|
@disallow("M8", "m8")
|
|
def nansem(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
ddof: int = 1,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> float:
|
|
"""
|
|
Compute the standard error in the mean along given axis while ignoring NaNs
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
ddof : int, default 1
|
|
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
|
|
where N represents the number of elements.
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : float64
|
|
Unless input is a float array, in which case use the same
|
|
precision as the input array.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, np.nan, 2, 3])
|
|
>>> nanops.nansem(s.values)
|
|
0.5773502691896258
|
|
"""
|
|
# This checks if non-numeric-like data is passed with numeric_only=False
|
|
# and raises a TypeError otherwise
|
|
nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask)
|
|
|
|
mask = _maybe_get_mask(values, skipna, mask)
|
|
if values.dtype.kind != "f":
|
|
values = values.astype("f8")
|
|
|
|
if not skipna and mask is not None and mask.any():
|
|
return np.nan
|
|
|
|
count, _ = _get_counts_nanvar(values.shape, mask, axis, ddof, values.dtype)
|
|
var = nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask)
|
|
|
|
return np.sqrt(var) / np.sqrt(count)
|
|
|
|
|
|
def _nanminmax(meth, fill_value_typ):
|
|
@bottleneck_switch(name=f"nan{meth}")
|
|
@_datetimelike_compat
|
|
def reduction(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
):
|
|
if values.size == 0:
|
|
return _na_for_min_count(values, axis)
|
|
|
|
values, mask = _get_values(
|
|
values, skipna, fill_value_typ=fill_value_typ, mask=mask
|
|
)
|
|
result = getattr(values, meth)(axis)
|
|
result = _maybe_null_out(result, axis, mask, values.shape)
|
|
return result
|
|
|
|
return reduction
|
|
|
|
|
|
nanmin = _nanminmax("min", fill_value_typ="+inf")
|
|
nanmax = _nanminmax("max", fill_value_typ="-inf")
|
|
|
|
|
|
def nanargmax(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> int | np.ndarray:
|
|
"""
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : int or ndarray[int]
|
|
The index/indices of max value in specified axis or -1 in the NA case
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> arr = np.array([1, 2, 3, np.nan, 4])
|
|
>>> nanops.nanargmax(arr)
|
|
4
|
|
|
|
>>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
|
|
>>> arr[2:, 2] = np.nan
|
|
>>> arr
|
|
array([[ 0., 1., 2.],
|
|
[ 3., 4., 5.],
|
|
[ 6., 7., nan],
|
|
[ 9., 10., nan]])
|
|
>>> nanops.nanargmax(arr, axis=1)
|
|
array([2, 2, 1, 1])
|
|
"""
|
|
values, mask = _get_values(values, True, fill_value_typ="-inf", mask=mask)
|
|
result = values.argmax(axis)
|
|
# error: Argument 1 to "_maybe_arg_null_out" has incompatible type "Any |
|
|
# signedinteger[Any]"; expected "ndarray[Any, Any]"
|
|
result = _maybe_arg_null_out(result, axis, mask, skipna) # type: ignore[arg-type]
|
|
return result
|
|
|
|
|
|
def nanargmin(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> int | np.ndarray:
|
|
"""
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : int or ndarray[int]
|
|
The index/indices of min value in specified axis or -1 in the NA case
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> arr = np.array([1, 2, 3, np.nan, 4])
|
|
>>> nanops.nanargmin(arr)
|
|
0
|
|
|
|
>>> arr = np.array(range(12), dtype=np.float64).reshape(4, 3)
|
|
>>> arr[2:, 0] = np.nan
|
|
>>> arr
|
|
array([[ 0., 1., 2.],
|
|
[ 3., 4., 5.],
|
|
[nan, 7., 8.],
|
|
[nan, 10., 11.]])
|
|
>>> nanops.nanargmin(arr, axis=1)
|
|
array([0, 0, 1, 1])
|
|
"""
|
|
values, mask = _get_values(values, True, fill_value_typ="+inf", mask=mask)
|
|
result = values.argmin(axis)
|
|
# error: Argument 1 to "_maybe_arg_null_out" has incompatible type "Any |
|
|
# signedinteger[Any]"; expected "ndarray[Any, Any]"
|
|
result = _maybe_arg_null_out(result, axis, mask, skipna) # type: ignore[arg-type]
|
|
return result
|
|
|
|
|
|
@disallow("M8", "m8")
|
|
@maybe_operate_rowwise
|
|
def nanskew(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> float:
|
|
"""
|
|
Compute the sample skewness.
|
|
|
|
The statistic computed here is the adjusted Fisher-Pearson standardized
|
|
moment coefficient G1. The algorithm computes this coefficient directly
|
|
from the second and third central moment.
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : float64
|
|
Unless input is a float array, in which case use the same
|
|
precision as the input array.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, np.nan, 1, 2])
|
|
>>> nanops.nanskew(s.values)
|
|
1.7320508075688787
|
|
"""
|
|
mask = _maybe_get_mask(values, skipna, mask)
|
|
if values.dtype.kind != "f":
|
|
values = values.astype("f8")
|
|
count = _get_counts(values.shape, mask, axis)
|
|
else:
|
|
count = _get_counts(values.shape, mask, axis, dtype=values.dtype)
|
|
|
|
if skipna and mask is not None:
|
|
values = values.copy()
|
|
np.putmask(values, mask, 0)
|
|
elif not skipna and mask is not None and mask.any():
|
|
return np.nan
|
|
|
|
with np.errstate(invalid="ignore", divide="ignore"):
|
|
mean = values.sum(axis, dtype=np.float64) / count
|
|
if axis is not None:
|
|
mean = np.expand_dims(mean, axis)
|
|
|
|
adjusted = values - mean
|
|
if skipna and mask is not None:
|
|
np.putmask(adjusted, mask, 0)
|
|
adjusted2 = adjusted**2
|
|
adjusted3 = adjusted2 * adjusted
|
|
m2 = adjusted2.sum(axis, dtype=np.float64)
|
|
m3 = adjusted3.sum(axis, dtype=np.float64)
|
|
|
|
# floating point error
|
|
#
|
|
# #18044 in _libs/windows.pyx calc_skew follow this behavior
|
|
# to fix the fperr to treat m2 <1e-14 as zero
|
|
m2 = _zero_out_fperr(m2)
|
|
m3 = _zero_out_fperr(m3)
|
|
|
|
with np.errstate(invalid="ignore", divide="ignore"):
|
|
result = (count * (count - 1) ** 0.5 / (count - 2)) * (m3 / m2**1.5)
|
|
|
|
dtype = values.dtype
|
|
if dtype.kind == "f":
|
|
result = result.astype(dtype, copy=False)
|
|
|
|
if isinstance(result, np.ndarray):
|
|
result = np.where(m2 == 0, 0, result)
|
|
result[count < 3] = np.nan
|
|
else:
|
|
result = dtype.type(0) if m2 == 0 else result
|
|
if count < 3:
|
|
return np.nan
|
|
|
|
return result
|
|
|
|
|
|
@disallow("M8", "m8")
|
|
@maybe_operate_rowwise
|
|
def nankurt(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> float:
|
|
"""
|
|
Compute the sample excess kurtosis
|
|
|
|
The statistic computed here is the adjusted Fisher-Pearson standardized
|
|
moment coefficient G2, computed directly from the second and fourth
|
|
central moment.
|
|
|
|
Parameters
|
|
----------
|
|
values : ndarray
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
result : float64
|
|
Unless input is a float array, in which case use the same
|
|
precision as the input array.
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, np.nan, 1, 3, 2])
|
|
>>> nanops.nankurt(s.values)
|
|
-1.2892561983471076
|
|
"""
|
|
mask = _maybe_get_mask(values, skipna, mask)
|
|
if values.dtype.kind != "f":
|
|
values = values.astype("f8")
|
|
count = _get_counts(values.shape, mask, axis)
|
|
else:
|
|
count = _get_counts(values.shape, mask, axis, dtype=values.dtype)
|
|
|
|
if skipna and mask is not None:
|
|
values = values.copy()
|
|
np.putmask(values, mask, 0)
|
|
elif not skipna and mask is not None and mask.any():
|
|
return np.nan
|
|
|
|
with np.errstate(invalid="ignore", divide="ignore"):
|
|
mean = values.sum(axis, dtype=np.float64) / count
|
|
if axis is not None:
|
|
mean = np.expand_dims(mean, axis)
|
|
|
|
adjusted = values - mean
|
|
if skipna and mask is not None:
|
|
np.putmask(adjusted, mask, 0)
|
|
adjusted2 = adjusted**2
|
|
adjusted4 = adjusted2**2
|
|
m2 = adjusted2.sum(axis, dtype=np.float64)
|
|
m4 = adjusted4.sum(axis, dtype=np.float64)
|
|
|
|
with np.errstate(invalid="ignore", divide="ignore"):
|
|
adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
|
|
numerator = count * (count + 1) * (count - 1) * m4
|
|
denominator = (count - 2) * (count - 3) * m2**2
|
|
|
|
# floating point error
|
|
#
|
|
# #18044 in _libs/windows.pyx calc_kurt follow this behavior
|
|
# to fix the fperr to treat denom <1e-14 as zero
|
|
numerator = _zero_out_fperr(numerator)
|
|
denominator = _zero_out_fperr(denominator)
|
|
|
|
if not isinstance(denominator, np.ndarray):
|
|
# if ``denom`` is a scalar, check these corner cases first before
|
|
# doing division
|
|
if count < 4:
|
|
return np.nan
|
|
if denominator == 0:
|
|
return values.dtype.type(0)
|
|
|
|
with np.errstate(invalid="ignore", divide="ignore"):
|
|
result = numerator / denominator - adj
|
|
|
|
dtype = values.dtype
|
|
if dtype.kind == "f":
|
|
result = result.astype(dtype, copy=False)
|
|
|
|
if isinstance(result, np.ndarray):
|
|
result = np.where(denominator == 0, 0, result)
|
|
result[count < 4] = np.nan
|
|
|
|
return result
|
|
|
|
|
|
@disallow("M8", "m8")
|
|
@maybe_operate_rowwise
|
|
def nanprod(
|
|
values: np.ndarray,
|
|
*,
|
|
axis: AxisInt | None = None,
|
|
skipna: bool = True,
|
|
min_count: int = 0,
|
|
mask: npt.NDArray[np.bool_] | None = None,
|
|
) -> float:
|
|
"""
|
|
Parameters
|
|
----------
|
|
values : ndarray[dtype]
|
|
axis : int, optional
|
|
skipna : bool, default True
|
|
min_count: int, default 0
|
|
mask : ndarray[bool], optional
|
|
nan-mask if known
|
|
|
|
Returns
|
|
-------
|
|
Dtype
|
|
The product of all elements on a given axis. ( NaNs are treated as 1)
|
|
|
|
Examples
|
|
--------
|
|
>>> from pandas.core import nanops
|
|
>>> s = pd.Series([1, 2, 3, np.nan])
|
|
>>> nanops.nanprod(s.values)
|
|
6.0
|
|
"""
|
|
mask = _maybe_get_mask(values, skipna, mask)
|
|
|
|
if skipna and mask is not None:
|
|
values = values.copy()
|
|
values[mask] = 1
|
|
result = values.prod(axis)
|
|
# error: Incompatible return value type (got "Union[ndarray, float]", expected
|
|
# "float")
|
|
return _maybe_null_out( # type: ignore[return-value]
|
|
result, axis, mask, values.shape, min_count=min_count
|
|
)
|
|
|
|
|
|
def _maybe_arg_null_out(
|
|
result: np.ndarray,
|
|
axis: AxisInt | None,
|
|
mask: npt.NDArray[np.bool_] | None,
|
|
skipna: bool,
|
|
) -> np.ndarray | int:
|
|
# helper function for nanargmin/nanargmax
|
|
if mask is None:
|
|
return result
|
|
|
|
if axis is None or not getattr(result, "ndim", False):
|
|
if skipna:
|
|
if mask.all():
|
|
return -1
|
|
else:
|
|
if mask.any():
|
|
return -1
|
|
else:
|
|
if skipna:
|
|
na_mask = mask.all(axis)
|
|
else:
|
|
na_mask = mask.any(axis)
|
|
if na_mask.any():
|
|
result[na_mask] = -1
|
|
return result
|
|
|
|
|
|
def _get_counts(
|
|
values_shape: Shape,
|
|
mask: npt.NDArray[np.bool_] | None,
|
|
axis: AxisInt | None,
|
|
dtype: np.dtype[np.floating] = np.dtype(np.float64),
|
|
) -> np.floating | npt.NDArray[np.floating]:
|
|
"""
|
|
Get the count of non-null values along an axis
|
|
|
|
Parameters
|
|
----------
|
|
values_shape : tuple of int
|
|
shape tuple from values ndarray, used if mask is None
|
|
mask : Optional[ndarray[bool]]
|
|
locations in values that should be considered missing
|
|
axis : Optional[int]
|
|
axis to count along
|
|
dtype : type, optional
|
|
type to use for count
|
|
|
|
Returns
|
|
-------
|
|
count : scalar or array
|
|
"""
|
|
if axis is None:
|
|
if mask is not None:
|
|
n = mask.size - mask.sum()
|
|
else:
|
|
n = np.prod(values_shape)
|
|
return dtype.type(n)
|
|
|
|
if mask is not None:
|
|
count = mask.shape[axis] - mask.sum(axis)
|
|
else:
|
|
count = values_shape[axis]
|
|
|
|
if is_integer(count):
|
|
return dtype.type(count)
|
|
return count.astype(dtype, copy=False)
|
|
|
|
|
|
def _maybe_null_out(
|
|
result: np.ndarray | float | NaTType,
|
|
axis: AxisInt | None,
|
|
mask: npt.NDArray[np.bool_] | None,
|
|
shape: tuple[int, ...],
|
|
min_count: int = 1,
|
|
) -> np.ndarray | float | NaTType:
|
|
"""
|
|
Returns
|
|
-------
|
|
Dtype
|
|
The product of all elements on a given axis. ( NaNs are treated as 1)
|
|
"""
|
|
if mask is None and min_count == 0:
|
|
# nothing to check; short-circuit
|
|
return result
|
|
|
|
if axis is not None and isinstance(result, np.ndarray):
|
|
if mask is not None:
|
|
null_mask = (mask.shape[axis] - mask.sum(axis) - min_count) < 0
|
|
else:
|
|
# we have no nulls, kept mask=None in _maybe_get_mask
|
|
below_count = shape[axis] - min_count < 0
|
|
new_shape = shape[:axis] + shape[axis + 1 :]
|
|
null_mask = np.broadcast_to(below_count, new_shape)
|
|
|
|
if np.any(null_mask):
|
|
if is_numeric_dtype(result):
|
|
if np.iscomplexobj(result):
|
|
result = result.astype("c16")
|
|
elif not is_float_dtype(result):
|
|
result = result.astype("f8", copy=False)
|
|
result[null_mask] = np.nan
|
|
else:
|
|
# GH12941, use None to auto cast null
|
|
result[null_mask] = None
|
|
elif result is not NaT:
|
|
if check_below_min_count(shape, mask, min_count):
|
|
result_dtype = getattr(result, "dtype", None)
|
|
if is_float_dtype(result_dtype):
|
|
# error: Item "None" of "Optional[Any]" has no attribute "type"
|
|
result = result_dtype.type("nan") # type: ignore[union-attr]
|
|
else:
|
|
result = np.nan
|
|
|
|
return result
|
|
|
|
|
|
def check_below_min_count(
|
|
shape: tuple[int, ...], mask: npt.NDArray[np.bool_] | None, min_count: int
|
|
) -> bool:
|
|
"""
|
|
Check for the `min_count` keyword. Returns True if below `min_count` (when
|
|
missing value should be returned from the reduction).
|
|
|
|
Parameters
|
|
----------
|
|
shape : tuple
|
|
The shape of the values (`values.shape`).
|
|
mask : ndarray[bool] or None
|
|
Boolean numpy array (typically of same shape as `shape`) or None.
|
|
min_count : int
|
|
Keyword passed through from sum/prod call.
|
|
|
|
Returns
|
|
-------
|
|
bool
|
|
"""
|
|
if min_count > 0:
|
|
if mask is None:
|
|
# no missing values, only check size
|
|
non_nulls = np.prod(shape)
|
|
else:
|
|
non_nulls = mask.size - mask.sum()
|
|
if non_nulls < min_count:
|
|
return True
|
|
return False
|
|
|
|
|
|
def _zero_out_fperr(arg):
|
|
# #18044 reference this behavior to fix rolling skew/kurt issue
|
|
if isinstance(arg, np.ndarray):
|
|
return np.where(np.abs(arg) < 1e-14, 0, arg)
|
|
else:
|
|
return arg.dtype.type(0) if np.abs(arg) < 1e-14 else arg
|
|
|
|
|
|
@disallow("M8", "m8")
|
|
def nancorr(
|
|
a: np.ndarray,
|
|
b: np.ndarray,
|
|
*,
|
|
method: CorrelationMethod = "pearson",
|
|
min_periods: int | None = None,
|
|
) -> float:
|
|
"""
|
|
a, b: ndarrays
|
|
"""
|
|
if len(a) != len(b):
|
|
raise AssertionError("Operands to nancorr must have same size")
|
|
|
|
if min_periods is None:
|
|
min_periods = 1
|
|
|
|
valid = notna(a) & notna(b)
|
|
if not valid.all():
|
|
a = a[valid]
|
|
b = b[valid]
|
|
|
|
if len(a) < min_periods:
|
|
return np.nan
|
|
|
|
a = _ensure_numeric(a)
|
|
b = _ensure_numeric(b)
|
|
|
|
f = get_corr_func(method)
|
|
return f(a, b)
|
|
|
|
|
|
def get_corr_func(
|
|
method: CorrelationMethod,
|
|
) -> Callable[[np.ndarray, np.ndarray], float]:
|
|
if method == "kendall":
|
|
from scipy.stats import kendalltau
|
|
|
|
def func(a, b):
|
|
return kendalltau(a, b)[0]
|
|
|
|
return func
|
|
elif method == "spearman":
|
|
from scipy.stats import spearmanr
|
|
|
|
def func(a, b):
|
|
return spearmanr(a, b)[0]
|
|
|
|
return func
|
|
elif method == "pearson":
|
|
|
|
def func(a, b):
|
|
return np.corrcoef(a, b)[0, 1]
|
|
|
|
return func
|
|
elif callable(method):
|
|
return method
|
|
|
|
raise ValueError(
|
|
f"Unknown method '{method}', expected one of "
|
|
"'kendall', 'spearman', 'pearson', or callable"
|
|
)
|
|
|
|
|
|
@disallow("M8", "m8")
|
|
def nancov(
|
|
a: np.ndarray,
|
|
b: np.ndarray,
|
|
*,
|
|
min_periods: int | None = None,
|
|
ddof: int | None = 1,
|
|
) -> float:
|
|
if len(a) != len(b):
|
|
raise AssertionError("Operands to nancov must have same size")
|
|
|
|
if min_periods is None:
|
|
min_periods = 1
|
|
|
|
valid = notna(a) & notna(b)
|
|
if not valid.all():
|
|
a = a[valid]
|
|
b = b[valid]
|
|
|
|
if len(a) < min_periods:
|
|
return np.nan
|
|
|
|
a = _ensure_numeric(a)
|
|
b = _ensure_numeric(b)
|
|
|
|
return np.cov(a, b, ddof=ddof)[0, 1]
|
|
|
|
|
|
def _ensure_numeric(x):
|
|
if isinstance(x, np.ndarray):
|
|
if x.dtype.kind in "biu":
|
|
x = x.astype(np.float64)
|
|
elif x.dtype == object:
|
|
inferred = lib.infer_dtype(x)
|
|
if inferred in ["string", "mixed"]:
|
|
# GH#44008, GH#36703 avoid casting e.g. strings to numeric
|
|
raise TypeError(f"Could not convert {x} to numeric")
|
|
try:
|
|
x = x.astype(np.complex128)
|
|
except (TypeError, ValueError):
|
|
try:
|
|
x = x.astype(np.float64)
|
|
except ValueError as err:
|
|
# GH#29941 we get here with object arrays containing strs
|
|
raise TypeError(f"Could not convert {x} to numeric") from err
|
|
else:
|
|
if not np.any(np.imag(x)):
|
|
x = x.real
|
|
elif not (is_float(x) or is_integer(x) or is_complex(x)):
|
|
if isinstance(x, str):
|
|
# GH#44008, GH#36703 avoid casting e.g. strings to numeric
|
|
raise TypeError(f"Could not convert string '{x}' to numeric")
|
|
try:
|
|
x = float(x)
|
|
except (TypeError, ValueError):
|
|
# e.g. "1+1j" or "foo"
|
|
try:
|
|
x = complex(x)
|
|
except ValueError as err:
|
|
# e.g. "foo"
|
|
raise TypeError(f"Could not convert {x} to numeric") from err
|
|
return x
|
|
|
|
|
|
def na_accum_func(values: ArrayLike, accum_func, *, skipna: bool) -> ArrayLike:
|
|
"""
|
|
Cumulative function with skipna support.
|
|
|
|
Parameters
|
|
----------
|
|
values : np.ndarray or ExtensionArray
|
|
accum_func : {np.cumprod, np.maximum.accumulate, np.cumsum, np.minimum.accumulate}
|
|
skipna : bool
|
|
|
|
Returns
|
|
-------
|
|
np.ndarray or ExtensionArray
|
|
"""
|
|
mask_a, mask_b = {
|
|
np.cumprod: (1.0, np.nan),
|
|
np.maximum.accumulate: (-np.inf, np.nan),
|
|
np.cumsum: (0.0, np.nan),
|
|
np.minimum.accumulate: (np.inf, np.nan),
|
|
}[accum_func]
|
|
|
|
# This should go through ea interface
|
|
assert values.dtype.kind not in "mM"
|
|
|
|
# We will be applying this function to block values
|
|
if skipna and not issubclass(values.dtype.type, (np.integer, np.bool_)):
|
|
vals = values.copy()
|
|
mask = isna(vals)
|
|
vals[mask] = mask_a
|
|
result = accum_func(vals, axis=0)
|
|
result[mask] = mask_b
|
|
else:
|
|
result = accum_func(values, axis=0)
|
|
|
|
return result
|