Panel.swapaxes(axis1, axis2, copy=True) [source] Interchange axes and swap values axes appropriately Returns: y : same as input

TimedeltaIndex.inferred_type

Index.any(*args, **kwargs) [source] Return whether any element is True Parameters: All arguments to numpy.any are accepted. Returns: any : bool or array_like (if axis is specified) A single element array_like may be converted to bool.

Panel4D.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) [source] Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values Parameters: axis: {0 or ?index?, 1 or ?columns?}, default 0 index to direct ranking method : {?average?, ?min?, ?max?, ?first?, ?dense?} average: average rank of group min: lowest rank in group max: highest rank in group first: ranks assigned

Panel4D.transpose(*args, **kwargs) [source] Permute the dimensions of the Panel Parameters: args : three positional arguments: each oneof {0, 1, 2, ?items?, ?major_axis?, ?minor_axis?} copy : boolean, default False Make a copy of the underlying data. Mixed-dtype data will always result in a copy Returns: y : same as input Examples >>> p.transpose(2, 0, 1) >>> p.transpose(2, 0, 1, copy=True)

TimedeltaIndex.repeat(repeats, *args, **kwargs) [source] Analogous to ndarray.repeat

MultiIndex.itemsize return the size of the dtype of the item of the underlying data

DataFrame.gt(other, axis='columns', level=None) [source] Wrapper for flexible comparison methods gt

class pandas.Panel4D(data=None, labels=None, items=None, major_axis=None, minor_axis=None, copy=False, dtype=None) [source] Panel4D is a 4-Dimensional named container very much like a Panel, but having 4 named dimensions. It is intended as a test bed for more N-Dimensional named containers. DEPRECATED. Panel4D is deprecated and will be removed in a future version. The recommended way to represent these types of n-dimensional data are with the xarray package. Pandas provides a .to_xarray() m

TimedeltaIndex.is_boolean() [source]