Series.valid(inplace=False, **kwargs) [source]

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

TimedeltaIndex.all(other=None) [source]

Panel.interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs) [source] Interpolate values according to different methods. Please note that only method='linear' is supported for DataFrames/Series with a MultiIndex. Parameters: method : {?linear?, ?time?, ?index?, ?values?, ?nearest?, ?zero?, ?slinear?, ?quadratic?, ?cubic?, ?barycentric?, ?krogh?, ?polynomial?, ?spline?, ?piecewise_polynomial?, ?from_derivatives?, ?pchip?, ?akima

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

Series.pop(item) [source] Return item and drop from frame. Raise KeyError if not found.

Note The SparsePanel class has been removed in 0.19.0 We have implemented ?sparse? versions of Series and DataFrame. These are not sparse in the typical ?mostly 0?. Rather, you can view these objects as being ?compressed? where any data matching a specific value (NaN / missing value, though any value can be chosen) is omitted. A special SparseIndex object tracks where data has been ?sparsified?. This will make much more sense in an example. All of the standard pandas data structures have a t

DataFrame.reindex(index=None, columns=None, **kwargs) [source] Conform DataFrame 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, columns : 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?/

CategoricalIndex.inferred_type