def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: xrav = x.ravel() if isinstance(y, (np.ndarray, ABCSeries)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) yrav = y.ravel() mask = notnull(xrav) & notnull(yrav) xrav = xrav[mask] yrav = yrav[mask] if np.prod(xrav.shape) and np.prod(yrav.shape): result[mask] = op(xrav, yrav) elif hasattr(x, 'size'): result = np.empty(x.size, dtype=x.dtype) mask = notnull(xrav) xrav = xrav[mask] if np.prod(xrav.shape): result[mask] = op(xrav, y) else: raise TypeError("cannot perform operation {op} between " "objects of type {x} and {y}".format( op=name, x=type(x), y=type(y))) result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = result.reshape(x.shape) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: # TODO: might need to find_common_type here? result = np.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result
def _get_values(values, skipna, fill_value=None, fill_value_typ=None, isfinite=False, copy=True): """ utility to get the values view, mask, dtype if necessary copy and mask using the specified fill_value copy = True will force the copy """ values = _values_from_object(values) if isfinite: mask = _isfinite(values) else: mask = isnull(values) dtype = values.dtype dtype_ok = _na_ok_dtype(dtype) # get our fill value (in case we need to provide an alternative # dtype for it) fill_value = _get_fill_value(dtype, fill_value=fill_value, fill_value_typ=fill_value_typ) if skipna: if copy: values = values.copy() if dtype_ok: np.putmask(values, mask, fill_value) # promote if needed else: values, changed = _maybe_upcast_putmask(values, mask, fill_value) elif copy: values = values.copy() values = _view_if_needed(values) # return a platform independent precision dtype dtype_max = dtype if is_integer_dtype(dtype) or is_bool_dtype(dtype): dtype_max = np.int64 elif is_float_dtype(dtype): dtype_max = np.float64 return values, mask, dtype, dtype_max
def na_op(x, y): try: result = expressions.evaluate(op, str_rep, x, y, raise_on_error=True, **eval_kwargs) except TypeError: if isinstance(y, (np.ndarray, ABCSeries, pd.Index)): dtype = np.find_common_type([x.dtype, y.dtype], []) result = np.empty(x.size, dtype=dtype) mask = notnull(x) & notnull(y) result[mask] = op(x[mask], _values_from_object(y[mask])) elif isinstance(x, np.ndarray): result = np.empty(len(x), dtype=x.dtype) mask = notnull(x) result[mask] = op(x[mask], y) else: raise TypeError("{typ} cannot perform the operation " "{op}".format(typ=type(x).__name__, op=str_rep)) result, changed = _maybe_upcast_putmask(result, ~mask, np.nan) result = missing.fill_zeros(result, x, y, name, fill_zeros) return result