def _where_compat(mask, arr1, arr2):
if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
return new_vals.view(_NS_DTYPE)
import pandas.tslib as tslib
if arr1.dtype == _NS_DTYPE:
arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
if arr2.dtype == _NS_DTYPE:
arr2 = tslib.ints_to_pydatetime(arr2.view('i8'))
return np.where(mask, arr1, arr2)
def _where_compat(mask, arr1, arr2):
if arr1.dtype == _NS_DTYPE and arr2.dtype == _NS_DTYPE:
new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
return new_vals.view(_NS_DTYPE)
import pandas.tslib as tslib
if arr1.dtype == _NS_DTYPE:
arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
if arr2.dtype == _NS_DTYPE:
arr2 = tslib.ints_to_pydatetime(arr2.view('i8'))
return np.where(mask, arr1, arr2)
def _to_pydatetime(x):
if x.dtype == _NS_DTYPE:
shape = x.shape
x = tslib.ints_to_pydatetime(x.view(np.int64).ravel())
x = x.reshape(shape)
return x
def _astype_nansafe(arr, dtype, copy=True):
""" return a view if copy is False, but
need to be very careful as the result shape could change! """
if not isinstance(dtype, np.dtype):
dtype = _coerce_to_dtype(dtype)
if issubclass(dtype.type, text_type):
# in Py3 that's str, in Py2 that's unicode
return lib.astype_unicode(arr.ravel()).reshape(arr.shape)
elif issubclass(dtype.type, string_types):
return lib.astype_str(arr.ravel()).reshape(arr.shape)
elif is_datetime64_dtype(arr):
if dtype == object:
return tslib.ints_to_pydatetime(arr.view(np.int64))
elif dtype == np.int64:
return arr.view(dtype)
elif dtype != _NS_DTYPE:
raise TypeError("cannot astype a datetimelike from [%s] to [%s]" %
(arr.dtype, dtype))
return arr.astype(_NS_DTYPE)
elif is_timedelta64_dtype(arr):
if dtype == np.int64:
return arr.view(dtype)
elif dtype == object:
return tslib.ints_to_pytimedelta(arr.view(np.int64))
# in py3, timedelta64[ns] are int64
elif ((PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE])
or (not PY3 and dtype != _TD_DTYPE)):
# allow frequency conversions
if dtype.kind == 'm':
mask = isnull(arr)
result = arr.astype(dtype).astype(np.float64)
result[mask] = np.nan
return result
raise TypeError("cannot astype a timedelta from [%s] to [%s]" %
(arr.dtype, dtype))
return arr.astype(_TD_DTYPE)
elif (np.issubdtype(arr.dtype, np.floating)
and np.issubdtype(dtype, np.integer)):
if not np.isfinite(arr).all():
raise ValueError('Cannot convert non-finite values (NA or inf) to '
'integer')
elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
# work around NumPy brokenness, #1987
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
if copy:
return arr.astype(dtype)
return arr.view(dtype)
def _astype_nansafe(arr, dtype, copy=True):
""" return a view if copy is False, but
need to be very careful as the result shape could change! """
if not isinstance(dtype, np.dtype):
dtype = _coerce_to_dtype(dtype)
if issubclass(dtype.type, text_type):
# in Py3 that's str, in Py2 that's unicode
return lib.astype_unicode(arr.ravel()).reshape(arr.shape)
elif issubclass(dtype.type, string_types):
return lib.astype_str(arr.ravel()).reshape(arr.shape)
elif is_datetime64_dtype(arr):
if dtype == object:
return tslib.ints_to_pydatetime(arr.view(np.int64))
elif dtype == np.int64:
return arr.view(dtype)
elif dtype != _NS_DTYPE:
raise TypeError("cannot astype a datetimelike from [%s] to [%s]" %
(arr.dtype, dtype))
return arr.astype(_NS_DTYPE)
elif is_timedelta64_dtype(arr):
if dtype == np.int64:
return arr.view(dtype)
elif dtype == object:
return tslib.ints_to_pytimedelta(arr.view(np.int64))
# in py3, timedelta64[ns] are int64
elif ((PY3 and dtype not in [_INT64_DTYPE, _TD_DTYPE]) or
(not PY3 and dtype != _TD_DTYPE)):
# allow frequency conversions
if dtype.kind == 'm':
mask = isnull(arr)
result = arr.astype(dtype).astype(np.float64)
result[mask] = np.nan
return result
raise TypeError("cannot astype a timedelta from [%s] to [%s]" %
(arr.dtype, dtype))
return arr.astype(_TD_DTYPE)
elif (np.issubdtype(arr.dtype, np.floating) and
np.issubdtype(dtype, np.integer)):
if not np.isfinite(arr).all():
raise ValueError('Cannot convert non-finite values (NA or inf) to '
'integer')
elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
# work around NumPy brokenness, #1987
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
if copy:
return arr.astype(dtype)
return arr.view(dtype)
def convert_to_pydatetime(x, axis):
# coerce to an object dtype
if x.dtype == _NS_DTYPE:
shape = x.shape
x = tslib.ints_to_pydatetime(x.view(np.int64).ravel())
x = x.reshape(shape)
elif x.dtype == _TD_DTYPE:
shape = x.shape
x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel())
x = x.reshape(shape)
return x
def convert_to_pydatetime(x, axis):
# coerce to an object dtype
if x.dtype == _NS_DTYPE:
shape = x.shape
x = tslib.ints_to_pydatetime(x.view(np.int64).ravel())
x = x.reshape(shape)
elif x.dtype == _TD_DTYPE:
shape = x.shape
x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel())
x = x.reshape(shape)
return x
def _astype_nansafe(arr, dtype):
if not isinstance(dtype, np.dtype):
dtype = np.dtype(dtype)
if issubclass(arr.dtype.type, np.datetime64):
if dtype == object:
return tslib.ints_to_pydatetime(arr.view(np.int64))
elif np.issubdtype(arr.dtype, np.floating) and np.issubdtype(dtype, np.integer):
if np.isnan(arr).any():
raise ValueError("Cannot convert NA to integer")
elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
# work around NumPy brokenness, #1987
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
return arr.astype(dtype)
def _astype_nansafe(arr, dtype):
if not isinstance(dtype, np.dtype):
dtype = np.dtype(dtype)
if issubclass(arr.dtype.type, np.datetime64):
if dtype == object:
return tslib.ints_to_pydatetime(arr.view(np.int64))
elif (np.issubdtype(arr.dtype, np.floating) and
np.issubdtype(dtype, np.integer)):
if np.isnan(arr).any():
raise ValueError('Cannot convert NA to integer')
elif arr.dtype == np.object_ and np.issubdtype(dtype.type, np.integer):
# work around NumPy brokenness, #1987
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
return arr.astype(dtype)
def convert_to_pydatetime(x, axis):
# coerce to an object dtype
# if dtype is of datetimetz or timezone
if x.dtype.kind == _NS_DTYPE.kind:
if getattr(x, 'tz', None) is not None:
x = x.asobject
else:
shape = x.shape
x = tslib.ints_to_pydatetime(x.view(np.int64).ravel())
x = x.reshape(shape)
elif x.dtype == _TD_DTYPE:
shape = x.shape
x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel())
x = x.reshape(shape)
return x
def convert_to_pydatetime(x, axis):
# coerce to an object dtype
# if dtype is of datetimetz or timezone
if x.dtype.kind == _NS_DTYPE.kind:
if getattr(x, 'tz', None) is not None:
x = x.asobject
else:
shape = x.shape
x = tslib.ints_to_pydatetime(x.view(np.int64).ravel())
x = x.reshape(shape)
elif x.dtype == _TD_DTYPE:
shape = x.shape
x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel())
x = x.reshape(shape)
return x
def convert_to_pydatetime(x, axis):
# coerce to an object dtype
# if dtype is of datetimetz or timezone
if x.dtype.kind == com._NS_DTYPE.kind:
if getattr(x, "tz", None) is not None:
x = x.asobject.values
else:
shape = x.shape
x = tslib.ints_to_pydatetime(x.view(np.int64).ravel())
x = x.reshape(shape)
elif x.dtype == com._TD_DTYPE:
shape = x.shape
x = tslib.ints_to_pytimedelta(x.view(np.int64).ravel())
x = x.reshape(shape)
if axis == 1:
x = np.atleast_2d(x)
return x
def _possibly_cast_to_datetime(value, dtype, errors='raise'):
""" try to cast the array/value to a datetimelike dtype, converting float
nan to iNaT
"""
from pandas.tseries.timedeltas import to_timedelta
from pandas.tseries.tools import to_datetime
if dtype is not None:
if isinstance(dtype, string_types):
dtype = np.dtype(dtype)
is_datetime64 = is_datetime64_dtype(dtype)
is_datetime64tz = is_datetime64tz_dtype(dtype)
is_timedelta64 = is_timedelta64_dtype(dtype)
if is_datetime64 or is_datetime64tz or is_timedelta64:
# force the dtype if needed
if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE):
if dtype.name == 'datetime64[ns]':
dtype = _NS_DTYPE
else:
raise TypeError("cannot convert datetimelike to "
"dtype [%s]" % dtype)
elif is_datetime64tz:
# our NaT doesn't support tz's
# this will coerce to DatetimeIndex with
# a matching dtype below
if is_scalar(value) and isnull(value):
value = [value]
elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE):
if dtype.name == 'timedelta64[ns]':
dtype = _TD_DTYPE
else:
raise TypeError("cannot convert timedeltalike to "
"dtype [%s]" % dtype)
if is_scalar(value):
if value == tslib.iNaT or isnull(value):
value = tslib.iNaT
else:
value = np.array(value, copy=False)
# have a scalar array-like (e.g. NaT)
if value.ndim == 0:
value = tslib.iNaT
# we have an array of datetime or timedeltas & nulls
elif np.prod(
value.shape) or not is_dtype_equal(value.dtype, dtype):
try:
if is_datetime64:
value = to_datetime(value, errors=errors)._values
elif is_datetime64tz:
# input has to be UTC at this point, so just
# localize
value = to_datetime(
value, errors=errors).tz_localize(dtype.tz)
elif is_timedelta64:
value = to_timedelta(value, errors=errors)._values
except (AttributeError, ValueError, TypeError):
pass
# coerce datetimelike to object
elif is_datetime64_dtype(value) and not is_datetime64_dtype(dtype):
if is_object_dtype(dtype):
if value.dtype != _NS_DTYPE:
value = value.astype(_NS_DTYPE)
ints = np.asarray(value).view('i8')
return tslib.ints_to_pydatetime(ints)
# we have a non-castable dtype that was passed
raise TypeError('Cannot cast datetime64 to %s' % dtype)
else:
is_array = isinstance(value, np.ndarray)
# catch a datetime/timedelta that is not of ns variety
# and no coercion specified
if is_array and value.dtype.kind in ['M', 'm']:
dtype = value.dtype
if dtype.kind == 'M' and dtype != _NS_DTYPE:
value = value.astype(_NS_DTYPE)
elif dtype.kind == 'm' and dtype != _TD_DTYPE:
value = to_timedelta(value)
# only do this if we have an array and the dtype of the array is not
# setup already we are not an integer/object, so don't bother with this
# conversion
elif not (is_array and not (issubclass(value.dtype.type, np.integer)
or value.dtype == np.object_)):
value = _possibly_infer_to_datetimelike(value)
return value
def get_values(self, dtype):
if dtype == object:
flat_i8 = self.values.ravel().view(np.int64)
res = tslib.ints_to_pydatetime(flat_i8)
return res.reshape(self.values.shape)
return self.values
def _possibly_cast_to_datetime(value, dtype, errors='raise'):
""" try to cast the array/value to a datetimelike dtype, converting float
nan to iNaT
"""
from pandas.tseries.timedeltas import to_timedelta
from pandas.tseries.tools import to_datetime
if dtype is not None:
if isinstance(dtype, string_types):
dtype = np.dtype(dtype)
is_datetime64 = is_datetime64_dtype(dtype)
is_datetime64tz = is_datetime64tz_dtype(dtype)
is_timedelta64 = is_timedelta64_dtype(dtype)
if is_datetime64 or is_datetime64tz or is_timedelta64:
# force the dtype if needed
if is_datetime64 and not is_dtype_equal(dtype, _NS_DTYPE):
if dtype.name == 'datetime64[ns]':
dtype = _NS_DTYPE
else:
raise TypeError("cannot convert datetimelike to "
"dtype [%s]" % dtype)
elif is_datetime64tz:
# our NaT doesn't support tz's
# this will coerce to DatetimeIndex with
# a matching dtype below
if is_scalar(value) and isnull(value):
value = [value]
elif is_timedelta64 and not is_dtype_equal(dtype, _TD_DTYPE):
if dtype.name == 'timedelta64[ns]':
dtype = _TD_DTYPE
else:
raise TypeError("cannot convert timedeltalike to "
"dtype [%s]" % dtype)
if is_scalar(value):
if value == tslib.iNaT or isnull(value):
value = tslib.iNaT
else:
value = np.array(value, copy=False)
# have a scalar array-like (e.g. NaT)
if value.ndim == 0:
value = tslib.iNaT
# we have an array of datetime or timedeltas & nulls
elif np.prod(value.shape) or not is_dtype_equal(value.dtype,
dtype):
try:
if is_datetime64:
value = to_datetime(value, errors=errors)._values
elif is_datetime64tz:
# input has to be UTC at this point, so just
# localize
value = to_datetime(
value,
errors=errors).tz_localize(dtype.tz)
elif is_timedelta64:
value = to_timedelta(value, errors=errors)._values
except (AttributeError, ValueError, TypeError):
pass
# coerce datetimelike to object
elif is_datetime64_dtype(value) and not is_datetime64_dtype(dtype):
if is_object_dtype(dtype):
if value.dtype != _NS_DTYPE:
value = value.astype(_NS_DTYPE)
ints = np.asarray(value).view('i8')
return tslib.ints_to_pydatetime(ints)
# we have a non-castable dtype that was passed
raise TypeError('Cannot cast datetime64 to %s' % dtype)
else:
is_array = isinstance(value, np.ndarray)
# catch a datetime/timedelta that is not of ns variety
# and no coercion specified
if is_array and value.dtype.kind in ['M', 'm']:
dtype = value.dtype
if dtype.kind == 'M' and dtype != _NS_DTYPE:
value = value.astype(_NS_DTYPE)
elif dtype.kind == 'm' and dtype != _TD_DTYPE:
value = to_timedelta(value)
# only do this if we have an array and the dtype of the array is not
# setup already we are not an integer/object, so don't bother with this
# conversion
elif not (is_array and not (issubclass(value.dtype.type, np.integer) or
value.dtype == np.object_)):
value = _possibly_infer_to_datetimelike(value)
return value
def get_values(self, dtype):
if dtype == object:
flat_i8 = self.values.ravel().view(np.int64)
res = tslib.ints_to_pydatetime(flat_i8)
return res.reshape(self.values.shape)
return self.values
