CategoricalIndex.view(cls=None) [source]

Series.reindex(index=None, **kwargs) [source] Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False Parameters: index : array-like, optional (can be specified in order, or as keywords) New labels / index to conform to. Preferably an Index object to avoid duplicating data method : {None, ?backfill?/?bfill?, ?pad?/?ffill?, ?near

MultiIndex.set_value(arr, key, value) [source] Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you?re doing

DataFrame.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0) [source] Provides exponential weighted functions New in version 0.18.0. Parameters: com : float, optional Specify decay in terms of center of mass, \alpha = 1 / (1 + com),\text{ for } com \geq 0 span : float, optional Specify decay in terms of span, \alpha = 2 / (span + 1),\text{ for } span \geq 1 halflife : float, optional Specify decay in terms of half-life

CategoricalIndex.putmask(mask, value) [source] return a new Index of the values set with the mask See also numpy.ndarray.putmask

Series.plot.bar(**kwds) [source] Vertical bar plot New in version 0.17.0. Parameters: **kwds : optional Keyword arguments to pass on to pandas.Series.plot(). Returns: axes : matplotlib.AxesSubplot or np.array of them

TimedeltaIndex.get_value_maybe_box(series, key) [source]

DatetimeIndex.get_value(series, key) [source] Fast lookup of value from 1-dimensional ndarray. Only use this if you know what you?re doing

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

DatetimeIndex.is_all_dates