def fillna(
self: NDArrayBackedExtensionArrayT, value=None, method=None, limit=None
) -> NDArrayBackedExtensionArrayT:
value, method = validate_fillna_kwargs(
value, method, validate_scalar_dict_value=False
)
mask = self.isna()
# error: Argument 2 to "check_value_size" has incompatible type
# "ExtensionArray"; expected "ndarray"
value = missing.check_value_size(
value, mask, len(self) # type: ignore[arg-type]
)
if mask.any():
if method is not None:
# TODO: check value is None
# (for now) when self.ndim == 2, we assume axis=0
func = missing.get_fill_func(method, ndim=self.ndim)
new_values, _ = func(self._ndarray.T.copy(), limit=limit, mask=mask.T)
new_values = new_values.T
# TODO: PandasArray didn't used to copy, need tests for this
new_values = self._from_backing_data(new_values)
else:
# fill with value
new_values = self.copy()
new_values[mask] = value
else:
# We validate the fill_value even if there is nothing to fill
if value is not None:
self._validate_setitem_value(value)
new_values = self.copy()
return new_values
def fillna(self: NDArrayBackedExtensionArrayT,
value=None,
method=None,
limit=None) -> NDArrayBackedExtensionArrayT:
value, method = validate_fillna_kwargs(value, method)
mask = self.isna()
value = missing.check_value_size(value, mask, len(self))
if mask.any():
if method is not None:
# TODO: check value is None
# (for now) when self.ndim == 2, we assume axis=0
func = missing.get_fill_func(method, ndim=self.ndim)
new_values, _ = func(self._ndarray.T.copy(),
limit=limit,
mask=mask.T)
new_values = new_values.T
# TODO: PandasArray didn't used to copy, need tests for this
new_values = self._from_backing_data(new_values)
else:
# fill with value
new_values = self.copy()
new_values[mask] = value
else:
new_values = self.copy()
return new_values
def fillna(self, value=None, method=None, limit=None):
"""
Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, array-like
If a scalar value is passed it is used to fill all missing values.
Alternatively, an array-like 'value' can be given. It's expected
that the array-like have the same length as 'self'.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap.
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled.
Returns
-------
ExtensionArray
With NA/NaN filled.
"""
value, method = validate_fillna_kwargs(value, method)
mask = self.isna()
# error: Argument 2 to "check_value_size" has incompatible type
# "ExtensionArray"; expected "ndarray"
value = missing.check_value_size(
value,
mask,
len(self) # type: ignore[arg-type]
)
if mask.any():
if method is not None:
func = missing.get_fill_func(method)
new_values, _ = func(self.astype(object),
limit=limit,
mask=mask)
new_values = self._from_sequence(new_values, dtype=self.dtype)
else:
# fill with value
new_values = self.copy()
new_values[mask] = value
else:
new_values = self.copy()
return new_values
def fillna(
self: NDArrayBackedExtensionArrayT, value=None, method=None, limit=None
) -> NDArrayBackedExtensionArrayT:
value, method = validate_fillna_kwargs(value, method)
mask = self.isna()
value = missing.check_value_size(value, mask, len(self))
if mask.any():
if method is not None:
func = missing.get_fill_func(method)
new_values = func(self._ndarray.copy(), limit=limit, mask=mask)
# TODO: PandasArray didn't used to copy, need tests for this
new_values = self._from_backing_data(new_values)
else:
# fill with value
new_values = self.copy()
new_values[mask] = value
else:
new_values = self.copy()
return new_values
