CategoricalIndex.hasnans = None

CategoricalIndex.groupby(values) [source] Group the index labels by a given array of values. Parameters: values : array Values used to determine the groups. Returns: groups : dict {group name -> group labels}

CategoricalIndex.get_values() [source] return the underlying data as an ndarray

CategoricalIndex.get_value(series, key) [source] Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you?re doing

CategoricalIndex.get_slice_bound(label, side, kind) [source] Calculate slice bound that corresponds to given label. Returns leftmost (one-past-the-rightmost if side=='right') position of given label. Parameters: label : object side : {?left?, ?right?} kind : {?ix?, ?loc?, ?getitem?}

CategoricalIndex.get_loc(key, method=None) [source] Get integer location for requested label Parameters: key : label method : {None} default: exact matches only. Returns: loc : int if unique index, possibly slice or mask if not

CategoricalIndex.get_level_values(level) [source] Return vector of label values for requested level, equal to the length of the index Parameters: level : int Returns: values : ndarray

CategoricalIndex.get_indexer_non_unique(target) [source] this is the same for a CategoricalIndex for get_indexer; the API returns the missing values as well

CategoricalIndex.get_indexer_for(target, **kwargs) [source] guaranteed return of an indexer even when non-unique

CategoricalIndex.get_indexer(target, method=None, limit=None, tolerance=None) [source] Compute indexer and mask for new index given the current index. The indexer should be then used as an input to ndarray.take to align the current data to the new index. The mask determines whether labels are found or not in the current index Parameters: target : MultiIndex or Index (of tuples) method : {?pad?, ?ffill?, ?backfill?, ?bfill?} pad / ffill: propagate LAST valid observation forward to next val