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 a merge for ordered data with optional filling/interpolation.
Designed for ordered data like time series data. Optionally perform group-wise merge (see examples).
- Parameters
- leftDataFrame
- rightDataFrame
- onlabel or list
Field names to join on. Must be found in both DataFrames.
- left_onlabel 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_onlabel or list, or array-like
Field names to join on in right DataFrame or vector/list of vectors per left_on docs.
- left_bycolumn name or list of column names
Group left DataFrame by group columns and merge piece by piece with right DataFrame.
- right_bycolumn 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.
- suffixeslist-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