# 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')

  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