classmethod MultiIndex.from_arrays(arrays, sortorder=None, names=None) [source] Convert arrays to MultiIndex Parameters: arrays : list / sequence of array-likes Each array-like gives one level?s value for each data point. len(arrays) is the number of levels. sortorder : int or None Level of sortedness (must be lexicographically sorted by that level) Returns: index : MultiIndex See also MultiIndex.from_tuples Convert list of tuples to MultiIndex MultiIndex.from_product Make a

Series.nbytes return the number of bytes in the underlying data

Panel4D.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

Panel.pow(other, axis=0) [source] Exponential power of series and other, element-wise (binary operator pow). Equivalent to panel ** other. Parameters: other : DataFrame or Panel axis : {items, major_axis, minor_axis} Axis to broadcast over Returns: Panel See also Panel.rpow

Index.is_integer() [source]

DataFrame.pipe(func, *args, **kwargs) [source] Apply func(self, *args, **kwargs) New in version 0.16.2. Parameters: func : function function to apply to the NDFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the NDFrame. args : positional arguments passed into func. kwargs : a dictionary of keyword arguments passed into func. Returns: object : the return type of

Panel.get_values() [source] same as values (but handles sparseness conversions)

Series.subtract(other, level=None, fill_value=None, axis=0) [source] Subtraction of series and other, element-wise (binary operator sub). Equivalent to series - other, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a level, m

Resampler.max(_method='max', *args, **kwargs) [source] Compute max of group values See also pandas.Series.groupby, pandas.DataFrame.groupby, pandas.Panel.groupby

CategoricalIndex.nlevels