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

>>> ordered_merge(A, B, fill_method='ffill', left_by='group')
   key  lvalue group  rvalue
0    a       1     a     NaN
1    b       1     a       1
2    c       2     a       2
3    d       2     a       3
4    e       3     a       3
5    f       3     a       4
6    a       1     b     NaN
7    b       1     b       1
8    c       2     b       2
9    d       2     b       3
10   e       3     b       3
11   f       3     b       4