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

Index.get_indexer_non_unique(target) [source] return an indexer suitable for taking from a non unique index return the labels in the same order as the target, and return a missing indexer into the target (missing are marked as -1 in the indexer); target must be an iterable

Panel4D.minor_xs(key) [source] Return slice of panel along minor axis Parameters: key : object Minor axis label Returns: y : DataFrame index -> major axis, columns -> items Notes minor_xs is only for getting, not setting values. MultiIndex Slicers is a generic way to get/set values on any level or levels and is a superset of minor_xs functionality, see MultiIndex Slicers

Index.map(mapper) [source] Apply mapper function to its values. Parameters: mapper : callable Function to be applied. Returns: applied : array

DataFrame.rmul(other, axis='columns', level=None, fill_value=None) [source] Multiplication of dataframe and other, element-wise (binary operator rmul). 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 value.

DataFrameGroupBy.boxplot(grouped, subplots=True, column=None, fontsize=None, rot=0, grid=True, ax=None, figsize=None, layout=None, **kwds) [source] Make box plots from DataFrameGroupBy data. Parameters: grouped : Grouped DataFrame subplots : False - no subplots will be used True - create a subplot for each group column : column name or list of names, or vector Can be any valid input to groupby fontsize : int or string rot : label rotation angle grid : Setting this to True will show th

Expanding.count(**kwargs) [source] expanding count of number of non-NaN observations inside provided window. Returns: same type as input See also pandas.Series.expanding, pandas.DataFrame.expanding

DataFrame.sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs) [source] Return unbiased standard error of the mean over requested axis. Normalized by N-1 by default. This can be changed using the ddof argument 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 particu

Series.median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the median of the values for the requested axis Parameters: axis : {index (0)} 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 scalar numeric_only : boolean, default None Include only float, int, bool

pandas.test = > [source] Run tests for module using nose. Parameters: label : {?fast?, ?full?, ??, attribute identifier}, optional Identifies the tests to run. This can be a string to pass to the nosetests executable with the ?-A? option, or one of several special values. Special values are: ?fast? - the default - which corresponds to the nosetests -A option of ?not slow?. ?full? - fast (as above) and slow tests as in the ?no -A? option to nosetests - this is the same as ??. None or ??