def fillna(self: BaseMaskedArrayT,
value=None,
method=None,
limit=None) -> BaseMaskedArrayT:
value, method = validate_fillna_kwargs(value, method)
mask = self._mask
if is_array_like(value):
if len(value) != len(self):
raise ValueError(
f"Length of 'value' does not match. Got ({len(value)}) "
f" expected {len(self)}")
value = value[mask]
if mask.any():
if method is not None:
func = missing.get_fill_func(method)
new_values, new_mask = func(
self._data.copy(),
limit=limit,
mask=mask.copy(),
)
return type(self)(new_values, new_mask.view(np.bool_))
else:
# fill with value
new_values = self.copy()
new_values[mask] = value
else:
new_values = self.copy()
return new_values
def _fill_mask_inplace(
self, method: str, limit, mask: npt.NDArray[np.bool_]
) -> None:
# (for now) when self.ndim == 2, we assume axis=0
func = missing.get_fill_func(method, ndim=self.ndim)
func(self._ndarray.T, limit=limit, mask=mask.T)
return
def fillna(self, value=None, method=None, limit=None):
from pandas.api.types import is_array_like
from pandas.core.missing import get_fill_func
from pandas.util._validators import validate_fillna_kwargs
value, method = validate_fillna_kwargs(value, method)
mask = self.isna()
if is_array_like(value):
if len(value) != len(self):
raise ValueError("Length of 'value' does not match. Got ({}) "
" expected {}".format(len(value), len(self)))
value = value[mask]
if mask.any():
if method is not None:
func = get_fill_func(method)
new_values = func(self.astype(object), limit=limit, mask=mask)
new_values = self._from_sequence(new_values, self._dtype)
else:
# fill with value
new_values = np.asarray(self)
if isinstance(value, Geometry):
value = [value]
new_values[mask] = value
new_values = self.__class__(new_values, dtype=self.dtype)
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, 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()
# TODO: share this with EA base class implementation
if is_array_like(value):
if len(value) != len(self):
raise ValueError(
f"Length of 'value' does not match. Got ({len(value)}) "
f" expected {len(self)}")
value = value[mask]
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
def fillna(self, value=None, method=None, limit=None):
# Override in RaggedArray to handle ndarray fill values
from pandas.util._validators import validate_fillna_kwargs
from pandas.core.missing import get_fill_func
value, method = validate_fillna_kwargs(value, method)
mask = self.isna()
if isinstance(value, RaggedArray):
if len(value) != len(self):
raise ValueError("Length of 'value' does not match. Got ({}) "
" expected {}".format(len(value), len(self)))
value = value[mask]
if mask.any():
if method is not None:
func = 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 = list(self)
mask_indices, = np.where(mask)
for ind in mask_indices:
new_values[ind] = value
new_values = self._from_sequence(new_values, dtype=self.dtype)
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:
# 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()
if is_array_like(value):
if len(value) != len(self):
raise ValueError(
f"Length of 'value' does not match. Got ({len(value)}) "
f"expected {len(self)}")
value = value[mask]
if mask.any():
if method is not None:
func = get_fill_func(method)
new_values = func(self.to_numpy(object),
limit=limit,
mask=mask)
new_values = self._from_sequence(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
