DataFrame.unstack(level=-1, fill_value=None) [source] Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex). The level involved will automatically get sorted. Parameters: level : int, string, or list of these, default -1 (last level) Level(s)

Panel.to_msgpack(path_or_buf=None, encoding='utf-8', **kwargs) [source] msgpack (serialize) object to input file path THIS IS AN EXPERIMENTAL LIBRARY and the storage format may not be stable until a future release. Parameters: path : string File path, buffer-like, or None if None, return generated string append : boolean whether to append to an existing msgpack (default is False) compress : type of compressor (zlib or blosc), default to None (no compression)

Series.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs) [source] Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns. Parameters: by : mapping function / list of functions, dict, Series, or tuple / list of column names. Called on each element of the object index to determine the groups. If a dict or Series is passed, the Series or dict VALUES will be used

Series.notnull() [source] Return a boolean same-sized object indicating if the values are not null. See also isnull boolean inverse of notnull

Panel4D.apply(func, axis='major', **kwargs) [source] Applies function along axis (or axes) of the Panel Parameters: func : function Function to apply to each combination of ?other? axes e.g. if axis = ?items?, the combination of major_axis/minor_axis will each be passed as a Series; if axis = (?items?, ?major?), DataFrames of items & major axis will be passed axis : {?items?, ?minor?, ?major?}, or {0, 1, 2}, or a tuple with two axes Additional keyword arguments will be passed as ke

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

Panel4D.rtruediv(other, axis=0) [source] Floating division of series and other, element-wise (binary operator rtruediv). Equivalent to other / panel. Parameters: other : Panel or Panel4D axis : {labels, items, major_axis, minor_axis} Axis to broadcast over Returns: Panel4D See also Panel4D.truediv

EWM.corr(other=None, pairwise=None, **kwargs) [source] exponential weighted sample correlation Parameters: other : Series, DataFrame, or ndarray, optional if not supplied then will default to self and produce pairwise output pairwise : bool, default None If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a Panel in the case of DataFrame inputs. In the

Panel4D.fromDict(data, intersect=False, orient='items', dtype=None) [source] Construct Panel from dict of DataFrame objects Parameters: data : dict {field : DataFrame} intersect : boolean Intersect indexes of input DataFrames orient : {?items?, ?minor?}, default ?items? The ?orientation? of the data. If the keys of the passed dict should be the items of the result panel, pass ?items? (default). Otherwise if the columns of the values of the passed DataFrame objects should be the items

DatetimeIndex.to_julian_date() [source] Convert DatetimeIndex to Float64Index of Julian Dates. 0 Julian date is noon January 1, 4713 BC. http://en.wikipedia.org/wiki/Julian_day