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TYP: check_untyped_defs pandas.core.nanops #34689

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Merged
merged 4 commits into from
Jun 14, 2020
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TYP: check_untyped_defs pandas.core.nanops #34689

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4b0f27b
TYP: check_untyped_defs pandas.core.nanops
simonjayhawkins Jun 10, 2020
11636d0
Merge remote-tracking branch 'upstream/master' into pandas.core.nanops
simonjayhawkins Jun 11, 2020
9a80a16
address comments
simonjayhawkins Jun 11, 2020
23e7737
Merge remote-tracking branch 'upstream/master' into pandas.core.nanops
simonjayhawkins Jun 13, 2020
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20 changes: 13 additions & 7 deletions pandas/core/nanops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,7 @@ def __init__(self, name=None, **kwargs):
self.name = name
self.kwargs = kwargs

def __call__(self, alt):
def __call__(self, alt: F) -> F:
bn_name = self.name or alt.__name__

try:
Expand Down Expand Up @@ -130,7 +130,7 @@ def f(

return result

return f
return cast(F, f)
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What is the cast required here for? Wouldn't the return annotation already provide this info?

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This is how we have been typing all our decorators to preserve the signature. Its recommended best practice. https://mypy.readthedocs.io/en/stable/generics.html#declaring-decorators

if we don't cast, we get a mypy error pandas\core\nanops.py:133: error: Incompatible return value type (got "Callable[..., Any]", expected "F")



def _bn_ok_dtype(dtype: DtypeObj, name: str) -> bool:
Expand Down Expand Up @@ -514,7 +514,12 @@ def nansum(

@disallow(PeriodDtype)
@bottleneck_switch()
def nanmean(values, axis=None, skipna=True, mask=None):
def nanmean(
values: np.ndarray,
axis: Optional[int] = None,
skipna: bool = True,
mask: Optional[np.ndarray] = None,
) -> float:
"""
Compute the mean of the element along an axis ignoring NaNs

Expand All @@ -528,7 +533,7 @@ def nanmean(values, axis=None, skipna=True, mask=None):

Returns
-------
result : float
float
Unless input is a float array, in which case use the same
precision as the input array.

Expand Down Expand Up @@ -558,6 +563,7 @@ def nanmean(values, axis=None, skipna=True, mask=None):
the_sum = _ensure_numeric(values.sum(axis, dtype=dtype_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
Expand Down Expand Up @@ -1205,17 +1211,17 @@ def _maybe_arg_null_out(


def _get_counts(
values_shape: Tuple[int],
values_shape: Tuple[int, ...],
mask: Optional[np.ndarray],
axis: Optional[int],
dtype: Dtype = float,
) -> Union[int, np.ndarray]:
) -> Union[int, float, np.ndarray]:
"""
Get the count of non-null values along an axis

Parameters
----------
values_shape : Tuple[int]
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
Expand Down
3 changes: 0 additions & 3 deletions setup.cfg
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Expand Up @@ -211,9 +211,6 @@ check_untyped_defs=False
[mypy-pandas.core.missing]
check_untyped_defs=False

[mypy-pandas.core.nanops]
check_untyped_defs=False

[mypy-pandas.core.ops.docstrings]
check_untyped_defs=False

Expand Down