Series.reshape(*args, **kwargs) [source] DEPRECATED: calling this method will raise an error in a future release. Please call .values.reshape(...) instead. return an ndarray with the values shape if the specified shape matches exactly the current shape, then return self (for compat) See also numpy.ndarray.reshape

Series.take(indices, axis=0, convert=True, is_copy=False, **kwargs) [source] return Series corresponding to requested indices Parameters: indices : list / array of ints convert : translate negative to positive indices (default) Returns: taken : Series See also numpy.ndarray.take

MultiIndex.difference(other) [source] Compute sorted set difference of two MultiIndex objects Returns: diff : MultiIndex

TimedeltaIndex.isin(values) [source] Compute boolean array of whether each index value is found in the passed set of values Parameters: values : set or sequence of values Returns: is_contained : ndarray (boolean dtype)

DatetimeIndex.unique() [source] Return Index of unique values in the object. Significantly faster than numpy.unique. Includes NA values. The order of the original is preserved. Returns: uniques : Index

TimedeltaIndex.fillna(value=None, downcast=None) [source] Fill NA/NaN values with the specified value Parameters: value : scalar Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes. downcast : dict, default is None a dict of item->dtype of what to downcast if possible, or the string ?infer? which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible) Returns: filled : %(klass)s

Panel.iterkv(*args, **kwargs) [source] iteritems alias used to get around 2to3. Deprecated

Rolling.corr(other=None, pairwise=None, **kwargs) [source] rolling sample correlation Parameters: other : Series, DataFrame, or ndarray, optional if not supplied then will default to self and produce pairwise output pairwise : bool, default None If False then only matching columns between self and other will be used and the output will be a DataFrame. If True then all pairwise combinations will be calculated and the output will be a Panel in the case of DataFrame inputs. In the case of

Panel.values Numpy representation of NDFrame Notes The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type convention, mixing int64 and uint64 w

TimedeltaIndex.name = None