Series.rename_axis(mapper, axis=0, copy=True, inplace=False) [source] Alter index and / or columns using input function or functions. A scaler or list-like for mapper will alter the Index.name or MultiIndex.names attribute. A function or dict for mapper will alter the labels. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Parameters: mapper : scalar, list-like, dict-like or function, optional axis : int or string, default 0 copy

Series.rename(index=None, **kwargs) [source] Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don?t throw an error. Alternatively, change Series.name with a scalar value (Series only). Parameters: index : scalar, list-like, dict-like or function, optional Scalar or list-like will alter the Series.name attribute, and raise on DataFrame or Panel. dict-like or functions are

Series.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 L

Series.reindex_axis(labels, axis=0, **kwargs) [source] for compatibility with higher dims

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

Series.real

Series.rdiv(other, level=None, fill_value=None, axis=0) [source] Floating division of series and other, element-wise (binary operator rtruediv). Equivalent to other / series, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a l

Series.ravel(order='C') [source] Return the flattened underlying data as an ndarray See also numpy.ndarray.ravel

Series.rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False) [source] Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values Parameters: axis: {0 or ?index?, 1 or ?columns?}, default 0 index to direct ranking method : {?average?, ?min?, ?max?, ?first?, ?dense?} average: average rank of group min: lowest rank in group max: highest rank in group first: ranks assigned

Series.radd(other, level=None, fill_value=None, axis=0) [source] Addition of series and other, element-wise (binary operator radd). Equivalent to other + series, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other: Series or scalar value fill_value : None or float value, default None (NaN) Fill missing (NaN) values with this value. If both Series are missing, the result will be missing level : int or name Broadcast across a level, matchin