def mask_missing(arr, values_to_mask): """ Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True """ if not isinstance(values_to_mask, (list, np.ndarray)): values_to_mask = [values_to_mask] try: values_to_mask = np.array(values_to_mask, dtype=arr.dtype) except Exception: values_to_mask = np.array(values_to_mask, dtype=object) na_mask = isnull(values_to_mask) nonna = values_to_mask[~na_mask] mask = None for x in nonna: if mask is None: # numpy elementwise comparison warning if is_numeric_v_string_like(arr, x): mask = False else: mask = arr == x # if x is a string and arr is not, then we get False and we must # expand the mask to size arr.shape if is_scalar(mask): mask = np.zeros(arr.shape, dtype=bool) else: # numpy elementwise comparison warning if is_numeric_v_string_like(arr, x): mask |= False else: mask |= arr == x if na_mask.any(): if mask is None: mask = isnull(arr) else: mask |= isnull(arr) return mask
def mask_missing(arr, values_to_mask): """ Return a masking array of same size/shape as arr with entries equaling any member of values_to_mask set to True """ if not isinstance(values_to_mask, (list, np.ndarray)): values_to_mask = [values_to_mask] try: values_to_mask = np.array(values_to_mask, dtype=arr.dtype) except Exception: values_to_mask = np.array(values_to_mask, dtype=object) na_mask = isnull(values_to_mask) nonna = values_to_mask[~na_mask] mask = None for x in nonna: if mask is None: # numpy elementwise comparison warning if is_numeric_v_string_like(arr, x): mask = False else: mask = arr == x # if x is a string and arr is not, then we get False and we must # expand the mask to size arr.shape if is_scalar(mask): mask = np.zeros(arr.shape, dtype=bool) else: # numpy elementwise comparison warning if is_numeric_v_string_like(arr, x): mask |= False else: mask |= arr == x if na_mask.any(): if mask is None: mask = isnull(arr) else: mask |= isnull(arr) return mask