pandas.merge_ordered

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.

See also

merge, merge_asof

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