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:list-like, default is (“_x”, “_y”)

A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in left and right respectively. Pass a value of None instead of a string to indicate that the column name from left or right should be left as-is, with no suffix. At least one of the values must not be None.

Changed in version 0.25.0.

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).

Returns

DataFrame

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

See also

merge

Merge with a database-style join.

merge_asof

Merge on nearest keys.

Examples

>>> df1 = pd.DataFrame(
...     {
...         "key": ["a", "c", "e", "a", "c", "e"],
...         "lvalue": [1, 2, 3, 1, 2, 3],
...         "group": ["a", "a", "a", "b", "b", "b"]
...     }
... )
>>> df1
      key  lvalue group
0   a       1     a
1   c       2     a
2   e       3     a
3   a       1     b
4   c       2     b
5   e       3     b

>>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
>>> df2
      key  rvalue
0   b       1
1   c       2
2   d       3

>>> merge_ordered(df1, df2, fill_method="ffill", left_by="group")
  key  lvalue group  rvalue
0   a       1     a     NaN
1   b       1     a     1.0
2   c       2     a     2.0
3   d       2     a     3.0
4   e       3     a     3.0
5   a       1     b     NaN
6   b       1     b     1.0
7   c       2     b     2.0
8   d       2     b     3.0
9   e       3     b     3.0