DataFrame.clip(lower=None, upper=None, axis=None, *args, **kwargs) [source] Trim values at input threshold(s). Parameters: lower : float or array_like, default None upper : float or array_like, default None axis : int or string axis name, optional Align object with lower and upper along the given axis. Returns: clipped : Series Examples >>> df 0 1 0 0.335232 -1.256177 1 -1.367855 0.746646 2 0.027753 -1.176076 3 0.230930 -0.679613 4 1.261967 0.570967 >>&g

DataFrame.boxplot(column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, **kwds) [source] Make a box plot from DataFrame column optionally grouped by some columns or other inputs Parameters: data : the pandas object holding the data column : column name or list of names, or vector Can be any valid input to groupby by : string or sequence Column in the DataFrame to group by ax : Matplotlib axes object, optional fontsize : int or stri

DataFrame.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

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

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

DataFrame.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

DataFrame.axes Return a list with the row axis labels and column axis labels as the only members. They are returned in that order.

DataFrame.at_time(time, asof=False) [source] Select values at particular time of day (e.g. 9:30AM). Parameters: time : datetime.time or string Returns: values_at_time : type of caller

DataFrame.at Fast label-based scalar accessor Similarly to loc, at provides label based scalar lookups. You can also set using these indexers.

DataFrame.as_matrix(columns=None) [source] Convert the frame to its Numpy-array representation. Parameters: columns: list, optional, default:None If None, return all columns, otherwise, returns specified columns. Returns: values : ndarray If the caller is heterogeneous and contains booleans or objects, the result will be of dtype=object. See Notes. See also pandas.DataFrame.values Notes Return is NOT a Numpy-matrix, rather, a Numpy-array. The dtype will be a lower-common-denominat