Series.cat() [source] Accessor object for categorical properties of the Series values. Be aware that assigning to categories is a inplace operation, while all methods return new categorical data per default (but can be called with inplace=True). Examples >>> s.cat.categories >>> s.cat.categories = list('abc') >>> s.cat.rename_categories(list('cab')) >>> s.cat.reorder_categories(list('cab')) >>> s.cat.add_categories(['d','e']) >>> s.cat.r

DatetimeIndex.is_lexsorted_for_tuple(tup) [source]

DatetimeIndex.ravel(order='C') [source] return an ndarray of the flattened values of the underlying data See also numpy.ndarray.ravel

Series.dt.weekofyear The week ordinal of the year

Panel4D.consolidate(inplace=False) [source] Compute NDFrame with ?consolidated? internals (data of each dtype grouped together in a single ndarray). Mainly an internal API function, but available here to the savvy user Parameters: inplace : boolean, default False If False return new object, otherwise modify existing object Returns: consolidated : type of caller

MultiIndex.set_levels(levels, level=None, inplace=False, verify_integrity=True) [source] Set new levels on MultiIndex. Defaults to returning new index. Parameters: levels : sequence or list of sequence new level(s) to apply level : int, level name, or sequence of int/level names (default None) level(s) to set (None for all levels) inplace : bool if True, mutates in place verify_integrity : bool (default True) if True, checks that levels and labels are compatible Returns: new inde

Series.xs(key, axis=0, level=None, drop_level=True) [source] Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. Defaults to cross-section on the rows (axis=0). Parameters: key : object Some label contained in the index, or partially in a MultiIndex axis : int, default 0 Axis to retrieve cross-section on level : object, defaults to first n levels (n=1 or len(key)) In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be r

MultiIndex.set_labels(labels, level=None, inplace=False, verify_integrity=True) [source] Set new labels on MultiIndex. Defaults to returning new index. Parameters: labels : sequence or list of sequence new labels to apply level : int, level name, or sequence of int/level names (default None) level(s) to set (None for all levels) inplace : bool if True, mutates in place verify_integrity : bool (default True) if True, checks that levels and labels are compatible Returns: new index

Series.memory_usage(index=True, deep=False) [source] Memory usage of the Series Parameters: index : bool Specifies whether to include memory usage of Series index deep : bool Introspect the data deeply, interrogate object dtypes for system-level memory consumption Returns: scalar bytes of memory consumed See also numpy.ndarray.nbytes Notes Memory usage does not include memory consumed by elements that are not components of the array if deep=False

Series.str.islower() [source] Check whether all characters in each string in the Series/Index are lowercase. Equivalent to str.islower(). Returns: is : Series/array of boolean values