Series.dt.total_seconds(*args, **kwargs) [source] Total duration of each element expressed in seconds. New in version 0.17.0.

MultiIndex.size return the number of elements in the underlying data

CategoricalIndex.base return the base object if the memory of the underlying data is shared

Series.equals(other) [source] Determines if two NDFrame objects contain the same elements. NaNs in the same location are considered equal.

Panel.ftypes Return the ftypes (indication of sparse/dense and dtype) in this object.

Series.is_copy = None

By ?group by? we are referring to a process involving one or more of the following steps Splitting the data into groups based on some criteria Applying a function to each group independently Combining the results into a data structure Of these, the split step is the most straightforward. In fact, in many situations you may wish to split the data set into groups and do something with those groups yourself. In the apply step, we might wish to one of the following: Aggregation: computing

TimedeltaIndex.union(other) [source] Specialized union for TimedeltaIndex objects. If combine overlapping ranges with the same DateOffset, will be much faster than Index.union Parameters: other : TimedeltaIndex or array-like Returns: y : Index or TimedeltaIndex

Panel4D.lt(other, axis=None) [source] Wrapper for comparison method lt

Panel4D.mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs) [source] Return the mean 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 : boolean, def