DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False) [source] Set the DataFrame index (row labels) using one or more existing columns. By default yields a new object. Parameters: keys : column label or list of column labels / arrays drop : boolean, default True Delete columns to be used as the new index append : boolean, default False Whether to append columns to existing index inplace : boolean, default False Modify the DataFrame in place (do not

Series.to_dict() [source] Convert Series to {label -> value} dict Returns: value_dict : dict

CategoricalIndex.repeat(n, *args, **kwargs) [source] Repeat elements of an Index. Refer to numpy.ndarray.repeat for more information about the n argument. See also numpy.ndarray.repeat

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.difference(other) [source] Return a new Index with elements from the index that are not in other. This is the set difference of two Index objects. It?s sorted if sorting is possible. Parameters: other : Index or array-like Returns: difference : Index Examples >>> idx1 = pd.Index([1, 2, 3, 4]) >>> idx2 = pd.Index([3, 4, 5, 6]) >>> idx1.difference(idx2) Int64Index([1, 2], dtype='int64')

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

Resampler.aggregate(arg, *args, **kwargs) [source] Apply aggregation function or functions to resampled groups, yielding most likely Series but in some cases DataFrame depending on the output of the aggregation function Parameters: func_or_funcs : function or list / dict of functions List/dict of functions will produce DataFrame with column names determined by the function names themselves (list) or the keys in the dict Returns: Series or DataFrame See also transform Notes agg is an

DataFrame.get(key, default=None) [source] Get item from object for given key (DataFrame column, Panel slice, etc.). Returns default value if not found. Parameters: key : object Returns: value : type of items contained in object

Series.get_ftype_counts() [source] Return the counts of ftypes in this object.

Series.dt.is_year_start Logical indicating if first day of year (defined by frequency)