Series.cat.categories The categories of this categorical. Setting assigns new values to each category (effectively a rename of each individual category). The assigned value has to be a list-like object. All items must be unique and the number of items in the new categories must be the same as the number of items in the old categories. Assigning to categories is a inplace operation! Raises: ValueError If the new categories do not validate as categories or if the number of new categories is

Series.cat.as_unordered(*args, **kwargs) [source] Sets the Categorical to be unordered Parameters: inplace : boolean (default: False) Whether or not to set the ordered attribute inplace or return a copy of this categorical with ordered set to False

Series.cat.as_ordered(*args, **kwargs) [source] Sets the Categorical to be ordered Parameters: inplace : boolean (default: False) Whether or not to set the ordered attribute inplace or return a copy of this categorical with ordered set to True

Series.cat.add_categories(*args, **kwargs) [source] Add new categories. new_categories will be included at the last/highest place in the categories and will be unused directly after this call. Parameters: new_categories : category or list-like of category The new categories to be included. inplace : boolean (default: False) Whether or not to add the categories inplace or return a copy of this categorical with added categories. Returns: cat : Categorical with new categories added or N

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

Series.bool() [source] Return the bool of a single element PandasObject. This must be a boolean scalar value, either True or False. Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean

Series.blocks Internal property, property synonym for as_blocks()

Series.bfill(axis=None, inplace=False, limit=None, downcast=None) [source] Synonym for NDFrame.fillna(method=?bfill?)

Series.between_time(start_time, end_time, include_start=True, include_end=True) [source] Select values between particular times of the day (e.g., 9:00-9:30 AM). Parameters: start_time : datetime.time or string end_time : datetime.time or string include_start : boolean, default True include_end : boolean, default True Returns: values_between_time : type of caller

Series.between(left, right, inclusive=True) [source] Return boolean Series equivalent to left <= series <= right. NA values will be treated as False Parameters: left : scalar Left boundary right : scalar Right boundary Returns: is_between : Series