if not isinstance(string, str):
raise TypeError(
"'construct_from_string' expects a string, got {}".format(
type(string)))
elif string == cls.name:
return cls()
else:
raise TypeError("Cannot construct a '{}' from '{}'".format(
cls.__name__, string))
@classmethod
def construct_array_type(cls):
return GeometryArray
register_extension_dtype(GeometryDtype)
def _isna(value):
"""
Check if scalar value is NA-like (None, np.nan or pd.NA).
Custom version that only works for scalars (returning True or False),
as `pd.isna` also works for array-like input returning a boolean array.
"""
if value is None:
return True
elif isinstance(value, float) and np.isnan(value):
return True
elif compat.PANDAS_GE_10 and value is pd.NA:
return True
# Note: the base tests provided by pandas only test the basics.
# We do not test
# 1. Values outside the range of the `data_for_sorting` fixture
# 2. Values between the values in the `data_for_sorting` fixture
# 3. Missing values.
arr = self._data
if isinstance(value, _Quantity):
value = value.to(self.units).magnitude
elif is_list_like(value) and isinstance(value[0], _Quantity):
value = [item.to(self.units).magnitude for item in value]
return arr.searchsorted(value, side=side, sorter=sorter)
PintArray._add_arithmetic_ops()
PintArray._add_comparison_ops()
register_extension_dtype(PintType)
@register_dataframe_accessor("pint")
class PintDataFrameAccessor(object):
def __init__(self, pandas_obj):
self._obj = pandas_obj
def quantify(self, level=-1):
df = self._obj
df_columns = df.columns.to_frame()
unit_col_name = df_columns.columns[level]
units = df_columns[unit_col_name]
df_columns = df_columns.drop(columns=unit_col_name)
df_new = DataFrame(
else:
raise TypeError(
f'Cannot construct a "{cls.__name__}" from "{string}"')
@classmethod
def construct_array_type(cls):
"""
Return the array type associated with this dtype.
Return:
GeosArray: Associated ExtensionArray.
"""
return GeosArray
register_extension_dtype(GeosDtype)
class GeosArray(ExtensionArray):
dtype = GeosDtype() #: Dtype for this ExtensionArray
ndim = 1 #: Number of dimensions of this ExtensionArray
# -------------------------------------------------------------------------
# (De-)Serialization
# -------------------------------------------------------------------------
def __init__(self, data):
"""
Create a GeosArray.
Args:
data (Iterable): Data for the GeosArray (see Note)
na_value = None
@classmethod
def construct_from_string(cls, string):
if string == cls.name:
return cls()
else:
raise TypeError(f"Cannot construct a 'TimeDtype' from '{string}'")
@classmethod
def construct_array_type(cls):
return TimeArray
# Register the datatype with pandas
register_extension_dtype(TimeDtype)
# ------------------------------------------------------------------------------
# Constructors / converters to other formats
# ------------------------------------------------------------------------------
def from_list(data):
"""
Create TimeArray object from list of timeseries-like objects
Parameters
----------
data : list
each element in `data` represents a single timeseries that will be
converted into a row in the TimeArray. Elements of `data` need to
na_value = np.nan
@classmethod
def construct_from_string(cls, string):
if string == cls.name:
return cls()
else:
raise TypeError("Cannot construct a '{}' from '{}'".format(
cls.__name__, string))
@classmethod
def construct_array_type(cls):
return QAArray
register_extension_dtype(QADtype)
class QAArray(ExtensionArray):
"""
Class wrapping a numpy array of QA bits and
holding the array-based implementations.
"""
_dtype = QADtype()
def __init__(self, data):
if isinstance(data, self.__class__):
data = data.data
elif not isinstance(data, np.ndarray):
raise TypeError("'data' should be array of qa objects.")
