DataFrame.resample(rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None) [source] Convenience method for frequency conversion and resampling of time series. Object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or pass datetime-like values to the on or level keyword. Parameters: rule : string the offset string or object representing target conversion axis :

DataFrame.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 co

DataFrame.reorder_levels(order, axis=0) [source] Rearrange index levels using input order. May not drop or duplicate levels Parameters: order : list of int or list of str List representing new level order. Reference level by number (position) or by key (label). axis : int Where to reorder levels. Returns: type of caller (new object)

DataFrame.rename(index=None, columns=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, columns : scalar, list-like, dict-like or function, optional Scalar or list-like will alter the Series.name attribute, and raise on DataFrame or Panel. d

DataFrame.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 Note

DataFrame.reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan) [source] Conform input object 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: labels : array-like New labels / index to conform to. Preferably an Index object to avoid duplicating data axis : {0 or ?index?, 1 or ?columns?}

DataFrame.rdiv(other, axis='columns', level=None, fill_value=None) [source] Floating division of dataframe and other, element-wise (binary operator rtruediv). Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this

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?/

DataFrame.radd(other, axis='columns', level=None, fill_value=None) [source] Addition of dataframe and other, element-wise (binary operator radd). Equivalent to other + dataframe, but with support to substitute a fill_value for missing data in one of the inputs. Parameters: other : Series, DataFrame, or constant axis : {0, 1, ?index?, ?columns?} For Series input, axis to match Series index on fill_value : None or float value, default None Fill missing (NaN) values with this value. If bot

DataFrame.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 assign