Index.is_monotonic_decreasing return if the index is monotonic decreasing (only equal or decreasing) values.

DataFrame.plot.hist(by=None, bins=10, **kwds) [source] Histogram New in version 0.17.0. Parameters: by : string or sequence Column in the DataFrame to group by. bins: integer, default 10 Number of histogram bins to be used **kwds : optional Keyword arguments to pass on to pandas.DataFrame.plot(). Returns: axes : matplotlib.AxesSubplot or np.array of them

Panel.at Fast label-based scalar accessor Similarly to loc, at provides label based scalar lookups. You can also set using these indexers.

Panel4D.to_pickle(path) [source] Pickle (serialize) object to input file path. Parameters: path : string File path

Panel.reindex_like(other, method=None, copy=True, limit=None, tolerance=None) [source] Return an object with matching indices to myself. Parameters: other : Object method : string or None copy : boolean, default True limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between labels of the other object and this object for inexact matches. New in version 0.17.0. Returns: reindexed : same as input Notes Li

DataFrame.irow(i, copy=False) [source] DEPRECATED. Use .iloc[i] instead

Panel.iloc Purely integer-location based indexing for selection by position. .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. Allowed inputs are: An integer, e.g. 5. A list or array of integers, e.g. [4, 3, 0]. A slice object with ints, e.g. 1:7. A boolean array. A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above) .iloc will raise I

CategoricalIndex.dropna(how='any') [source] Return Index without NA/NaN values Parameters: how : {?any?, ?all?}, default ?any? If the Index is a MultiIndex, drop the value when any or all levels are NaN. Returns: valid : Index

DatetimeIndex.is_integer() [source]

CategoricalIndex.sym_diff(*args, **kwargs) [source]