Python pandas.core.dtypes.cast.astype_nansafe() Examples
The following are 7
code examples of pandas.core.dtypes.cast.astype_nansafe().
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Example #1
| Source File: integer.py From recruit with Apache License 2.0 | 5 votes |
|
def astype(self, dtype, copy=True):
"""
Cast to a NumPy array or IntegerArray with 'dtype'.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
copy : bool, default True
Whether to copy the data, even if not necessary. If False,
a copy is made only if the old dtype does not match the
new dtype.
Returns
-------
array : ndarray or IntegerArray
NumPy ndarray or IntergerArray with 'dtype' for its dtype.
Raises
------
TypeError
if incompatible type with an IntegerDtype, equivalent of same_kind
casting
"""
# if we are astyping to an existing IntegerDtype we can fastpath
if isinstance(dtype, _IntegerDtype):
result = self._data.astype(dtype.numpy_dtype, copy=False)
return type(self)(result, mask=self._mask, copy=False)
# coerce
data = self._coerce_to_ndarray()
return astype_nansafe(data, dtype, copy=None)
Example #2
| Source File: parsers.py From vnpy_crypto with MIT License | 5 votes |
|
def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
Returns
-------
converted : ndarray
"""
if is_categorical_dtype(cast_type):
known_cats = (isinstance(cast_type, CategoricalDtype) and
cast_type.categories is not None)
if not is_object_dtype(values) and not known_cats:
# XXX this is for consistency with
# c-parser which parses all categories
# as strings
values = astype_nansafe(values, str)
cats = Index(values).unique().dropna()
values = Categorical._from_inferred_categories(
cats, cats.get_indexer(values), cast_type
)
else:
try:
values = astype_nansafe(values, cast_type, copy=True)
except ValueError:
raise ValueError("Unable to convert column %s to "
"type %s" % (column, cast_type))
return values
Example #3
| Source File: integer.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def astype(self, dtype, copy=True):
"""
Cast to a NumPy array or IntegerArray with 'dtype'.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
copy : bool, default True
Whether to copy the data, even if not necessary. If False,
a copy is made only if the old dtype does not match the
new dtype.
Returns
-------
array : ndarray or IntegerArray
NumPy ndarray or IntergerArray with 'dtype' for its dtype.
Raises
------
TypeError
if incompatible type with an IntegerDtype, equivalent of same_kind
casting
"""
# if we are astyping to an existing IntegerDtype we can fastpath
if isinstance(dtype, _IntegerDtype):
result = self._data.astype(dtype.numpy_dtype, copy=False)
return type(self)(result, mask=self._mask, copy=False)
# coerce
data = self._coerce_to_ndarray()
return astype_nansafe(data, dtype, copy=None)
Example #4
| Source File: parsers.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
Returns
-------
converted : ndarray
"""
if is_categorical_dtype(cast_type):
known_cats = (isinstance(cast_type, CategoricalDtype) and
cast_type.categories is not None)
if not is_object_dtype(values) and not known_cats:
# XXX this is for consistency with
# c-parser which parses all categories
# as strings
values = astype_nansafe(values, str)
cats = Index(values).unique().dropna()
values = Categorical._from_inferred_categories(
cats, cats.get_indexer(values), cast_type
)
else:
try:
values = astype_nansafe(values, cast_type, copy=True)
except ValueError:
raise ValueError("Unable to convert column %s to "
"type %s" % (column, cast_type))
return values
Example #5
| Source File: parsers.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
Returns
-------
converted : ndarray
"""
if is_categorical_dtype(cast_type):
known_cats = (isinstance(cast_type, CategoricalDtype) and
cast_type.categories is not None)
if not is_object_dtype(values) and not known_cats:
# XXX this is for consistency with
# c-parser which parses all categories
# as strings
values = astype_nansafe(values, str)
cats = Index(values).unique().dropna()
values = Categorical._from_inferred_categories(
cats, cats.get_indexer(values), cast_type
)
else:
try:
values = astype_nansafe(values, cast_type, copy=True)
except ValueError:
raise ValueError("Unable to convert column %s to "
"type %s" % (column, cast_type))
return values
Example #6
| Source File: parsers.py From recruit with Apache License 2.0 | 4 votes |
|
def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
Returns
-------
converted : ndarray
"""
if is_categorical_dtype(cast_type):
known_cats = (isinstance(cast_type, CategoricalDtype) and
cast_type.categories is not None)
if not is_object_dtype(values) and not known_cats:
# XXX this is for consistency with
# c-parser which parses all categories
# as strings
values = astype_nansafe(values, str)
cats = Index(values).unique().dropna()
values = Categorical._from_inferred_categories(
cats, cats.get_indexer(values), cast_type,
true_values=self.true_values)
# use the EA's implementation of casting
elif is_extension_array_dtype(cast_type):
# ensure cast_type is an actual dtype and not a string
cast_type = pandas_dtype(cast_type)
array_type = cast_type.construct_array_type()
try:
return array_type._from_sequence_of_strings(values,
dtype=cast_type)
except NotImplementedError:
raise NotImplementedError(
"Extension Array: {ea} must implement "
"_from_sequence_of_strings in order "
"to be used in parser methods".format(ea=array_type))
else:
try:
values = astype_nansafe(values, cast_type,
copy=True, skipna=True)
except ValueError:
raise ValueError("Unable to convert column %s to "
"type %s" % (column, cast_type))
return values
Example #7
| Source File: parsers.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 4 votes |
|
def _cast_types(self, values, cast_type, column):
"""
Cast values to specified type
Parameters
----------
values : ndarray
cast_type : string or np.dtype
dtype to cast values to
column : string
column name - used only for error reporting
Returns
-------
converted : ndarray
"""
if is_categorical_dtype(cast_type):
known_cats = (isinstance(cast_type, CategoricalDtype) and
cast_type.categories is not None)
if not is_object_dtype(values) and not known_cats:
# XXX this is for consistency with
# c-parser which parses all categories
# as strings
values = astype_nansafe(values, str)
cats = Index(values).unique().dropna()
values = Categorical._from_inferred_categories(
cats, cats.get_indexer(values), cast_type,
true_values=self.true_values)
# use the EA's implementation of casting
elif is_extension_array_dtype(cast_type):
# ensure cast_type is an actual dtype and not a string
cast_type = pandas_dtype(cast_type)
array_type = cast_type.construct_array_type()
try:
return array_type._from_sequence_of_strings(values,
dtype=cast_type)
except NotImplementedError:
raise NotImplementedError(
"Extension Array: {ea} must implement "
"_from_sequence_of_strings in order "
"to be used in parser methods".format(ea=array_type))
else:
try:
values = astype_nansafe(values, cast_type,
copy=True, skipna=True)
except ValueError:
raise ValueError("Unable to convert column %s to "
"type %s" % (column, cast_type))
return values
