Panel4D.product(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the product of the values for the requested axis Parameters: axis : {labels (0), items (1), major_axis (2), minor_axis (3)} 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 Panel numeric_only : boolea

Series.quantile(q=0.5, interpolation='linear') [source] Return value at the given quantile, a la numpy.percentile. Parameters: q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute interpolation : {?linear?, ?lower?, ?higher?, ?midpoint?, ?nearest?} New in version 0.18.0. This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j: linear: i + (j - i) * fraction, where fr

CategoricalIndex.add_categories(*args, **kwargs) [source] Add new categories. new_categories will be included at the last/highest place in the categories and will be unused directly after this call. Parameters: new_categories : category or list-like of category The new categories to be included. inplace : boolean (default: False) Whether or not to add the categories inplace or return a copy of this categorical with added categories. Returns: cat : Categorical with new categories adde

TimedeltaIndex.T return the transpose, which is by definition self

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

TimedeltaIndex.seconds Number of seconds (>= 0 and less than 1 day) for each element.

Series.filter(items=None, like=None, regex=None, axis=None) [source] Subset rows or columns of dataframe according to labels in the specified index. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters: items : list-like List of info axis to restrict to (must not all be present) like : string Keep info axis where ?arg in col == True? regex : string (regular expression) Keep info axis with re.search(regex, col)

Resampler.transform(arg, *args, **kwargs) [source] Call function producing a like-indexed Series on each group and return a Series with the transformed values Parameters: func : function To apply to each group. Should return a Series with the same index Returns: transformed : Series Examples >>> resampled.transform(lambda x: (x - x.mean()) / x.std())

DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear') [source] Return values at the given quantile over requested axis, a la numpy.percentile. Parameters: q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute axis : {0, 1, ?index?, ?columns?} (default 0) 0 or ?index? for row-wise, 1 or ?columns? for column-wise interpolation : {?linear?, ?lower?, ?higher?, ?midpoint?, ?nearest?} New in version 0.18.0. This optional p

Series.kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return unbiased kurtosis over requested axis using Fisher?s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1 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 sc