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 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 == 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