CategoricalIndex.is_type_compatible(kind) [source]

MultiIndex.equals(other) [source] Determines if two MultiIndex objects have the same labeling information (the levels themselves do not necessarily have to be the same) See also equal_levels

pandas.notnull(obj) [source] Replacement for numpy.isfinite / -numpy.isnan which is suitable for use on object arrays. Parameters: arr : ndarray or object value Object to check for not-null-ness Returns: isnulled : array-like of bool or bool Array or bool indicating whether an object is not null or if an array is given which of the element is not null. See also pandas.isnull boolean inverse of pandas.notnull

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

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

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

Series.dt.nanoseconds Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.

DataFrame.first_valid_index() [source] Return label for first non-NA/null value