MultiIndex.take(indices, axis=0, allow_fill=True, fill_value=None, **kwargs) [source] return a new %(klass)s of the values selected by the indices For internal compatibility with numpy arrays. Parameters: indices : list Indices to be taken axis : int, optional The axis over which to select values, always 0. allow_fill : bool, default True fill_value : bool, default None If allow_fill=True and fill_value is not None, indices specified by -1 is regarded as NA. If Index doesn?t hold NA,

Expanding.std(ddof=1, *args, **kwargs) [source] expanding standard deviation Parameters: ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

DatetimeIndex.argmax(axis=None, *args, **kwargs) [source] Returns the indices of the maximum values along an axis. See numpy.ndarray.argmax for more information on the axis parameter. See also numpy.ndarray.argmax

Series.truncate(before=None, after=None, axis=None, copy=True) [source] Truncates a sorted NDFrame before and/or after some particular index value. If the axis contains only datetime values, before/after parameters are converted to datetime values. Parameters: before : date Truncate before index value after : date Truncate after index value axis : the truncation axis, defaults to the stat axis copy : boolean, default is True, return a copy of the truncated section Returns: truncate

Series.iteritems() [source] Lazily iterate over (index, value) tuples

DataFrame.rdiv(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator rtruediv). Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this

CategoricalIndex.join(other, how='left', level=None, return_indexers=False) [source] this is an internal non-public method Compute join_index and indexers to conform data structures to the new index. Parameters: other : Index how : {?left?, ?right?, ?inner?, ?outer?} level : int or level name, default None return_indexers : boolean, default False Returns: join_index, (left_indexer, right_indexer)

Series.str.capitalize() [source] Convert strings in the Series/Index to be capitalized. Equivalent to str.capitalize(). Returns: converted : Series/Index of objects

EWM.cov(other=None, pairwise=None, bias=False, **kwargs) [source] exponential weighted sample covariance 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 inpu

DataFrame.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the mean of the values for the requested axis Parameters: axis : {index (0), columns (1)} skipna : boolean, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series numeric_only : boolean, default None Include only floa