pandas.merge_ordered — pandas 0.24.0rc1 documentation (original) (raw)

pandas. merge_ordered(left, right, on=None, left_on=None, right_on=None, left_by=None, right_by=None, fill_method=None, suffixes=('_x', '_y'), how='outer')[source]¶

Perform merge with optional filling/interpolation designed for ordered data like time series data. Optionally perform group-wise merge (see examples)

Parameters:	left : DataFrame right : DataFrame on : label or list Field names to join on. Must be found in both DataFrames. left_on : label or list, or array-like Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns right_on : label or list, or array-like Field names to join on in right DataFrame or vector/list of vectors per left_on docs left_by : column name or list of column names Group left DataFrame by group columns and merge piece by piece with right DataFrame right_by : column name or list of column names Group right DataFrame by group columns and merge piece by piece with left DataFrame fill_method : {‘ffill’, None}, default None Interpolation method for data suffixes : 2-length sequence (tuple, list, …) Suffix to apply to overlapping column names in the left and right side, respectively how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘outer’ left: use only keys from left frame (SQL: left outer join) right: use only keys from right frame (SQL: right outer join) outer: use union of keys from both frames (SQL: full outer join) inner: use intersection of keys from both frames (SQL: inner join) New in version 0.19.0.
Returns:	merged : DataFrame The output type will the be same as ‘left’, if it is a subclass of DataFrame.

Parameters:

left : DataFrame right : DataFrame on : label or list Field names to join on. Must be found in both DataFrames. left_on : label or list, or array-like Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns right_on : label or list, or array-like Field names to join on in right DataFrame or vector/list of vectors per left_on docs left_by : column name or list of column names Group left DataFrame by group columns and merge piece by piece with right DataFrame right_by : column name or list of column names Group right DataFrame by group columns and merge piece by piece with left DataFrame fill_method : {‘ffill’, None}, default None Interpolation method for data suffixes : 2-length sequence (tuple, list, …) Suffix to apply to overlapping column names in the left and right side, respectively how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘outer’ left: use only keys from left frame (SQL: left outer join) right: use only keys from right frame (SQL: right outer join) outer: use union of keys from both frames (SQL: full outer join) inner: use intersection of keys from both frames (SQL: inner join) New in version 0.19.0.

Returns:

merged : DataFrame The output type will the be same as ‘left’, if it is a subclass of DataFrame.

Examples

A >>> B key lvalue group key rvalue 0 a 1 a 0 b 1 1 c 2 a 1 c 2 2 e 3 a 2 d 3 3 a 1 b 4 c 2 b 5 e 3 b

merge_ordered(A, B, fill_method='ffill', left_by='group') group key lvalue rvalue 0 a a 1 NaN 1 a b 1 1.0 2 a c 2 2.0 3 a d 2 3.0 4 a e 3 3.0 5 b a 1 NaN 6 b b 1 1.0 7 b c 2 2.0 8 b d 2 3.0 9 b e 3 3.0