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CLN: simplify mask_missing #38127

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Nov 28, 2020
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CLN: simplify mask_missing #38127

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56 changes: 22 additions & 34 deletions pandas/core/missing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,65 +7,53 @@
import numpy as np

from pandas._libs import algos, lib
from pandas._typing import DtypeObj
from pandas._typing import ArrayLike, DtypeObj
from pandas.compat._optional import import_optional_dependency

from pandas.core.dtypes.cast import infer_dtype_from_array
from pandas.core.dtypes.common import (
ensure_float64,
is_integer_dtype,
is_numeric_v_string_like,
is_scalar,
needs_i8_conversion,
)
from pandas.core.dtypes.missing import isna


def mask_missing(arr, values_to_mask):
def mask_missing(arr: ArrayLike, values_to_mask) -> np.ndarray:
"""
Return a masking array of same size/shape as arr
with entries equaling any member of values_to_mask set to True
"""
dtype, values_to_mask = infer_dtype_from_array(values_to_mask)

try:
values_to_mask = np.array(values_to_mask, dtype=dtype)
Parameters
----------
arr : ArrayLike
values_to_mask: list, tuple, or scalar

except Exception:
values_to_mask = np.array(values_to_mask, dtype=object)
Returns
-------
np.ndarray[bool]
"""
# When called from Block.replace/replace_list, values_to_mask is a scalar
# known to be holdable by arr.
# When called from Series._single_replace, values_to_mask is tuple or list
dtype, values_to_mask = infer_dtype_from_array(values_to_mask)
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ok, this is very confusing and ought to simplify and tighten up the signature here (to make it consistently called), but clearly for a followon.

values_to_mask = np.array(values_to_mask, dtype=dtype)

na_mask = isna(values_to_mask)
nonna = values_to_mask[~na_mask]

mask = None
# GH 21977
mask = np.zeros(arr.shape, dtype=bool)
for x in nonna:
if mask is None:
if is_numeric_v_string_like(arr, x):
# GH#29553 prevent numpy deprecation warnings
mask = False
else:
mask = arr == x

# if x is a string and arr is not, then we get False and we must
# expand the mask to size arr.shape
if is_scalar(mask):
mask = np.zeros(arr.shape, dtype=bool)
if is_numeric_v_string_like(arr, x):
# GH#29553 prevent numpy deprecation warnings
pass
else:
if is_numeric_v_string_like(arr, x):
# GH#29553 prevent numpy deprecation warnings
mask |= False
else:
mask |= arr == x
mask |= arr == x

if na_mask.any():
if mask is None:
mask = isna(arr)
else:
mask |= isna(arr)

# GH 21977
if mask is None:
mask = np.zeros(arr.shape, dtype=bool)
mask |= isna(arr)

return mask

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