merge_ordered(left: 'DataFrame', right: 'DataFrame', on: 'IndexLabel | None' = None, left_on: 'IndexLabel | None' = None, right_on: 'IndexLabel | None' = None, left_by=None, right_by=None, fill_method: 'str | None' = None, suffixes: 'Suffixes' = ('_x', '_y'), how: 'str' = 'outer') -> 'DataFrame'
Designed for ordered data like time series data. Optionally perform group-wise merge (see examples).
Field names to join on. Must be found in both DataFrames.
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.
Field names to join on in right DataFrame or vector/list of vectors per left_on docs.
Group left DataFrame by group columns and merge piece by piece with right DataFrame.
Group right DataFrame by group columns and merge piece by piece with left DataFrame.
Interpolation method for data.
A length-2 sequence where each element is optionally a string indicating the suffix to add to overlapping column names in :None:None:`left` and :None:None:`right` respectively. Pass a value of :None:None:`None` instead of a string to indicate that the column name from :None:None:`left` or :None:None:`right` should be left as-is, with no suffix. At least one of the values must not be None.
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).
The merged DataFrame output type will the be same as 'left', if it is a subclass of DataFrame.
Perform a merge for ordered data with optional filling/interpolation.
merge
Merge with a database-style join.
merge_asof
Merge on nearest keys.
>>> df1 = pd.DataFrame(This example is valid syntax, but we were not able to check execution
... {
... "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]})This example is valid syntax, but we were not able to check execution
... 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 3.0See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.reshape.merge.merge_asof
pandas.core.reshape.merge.merge
pandas.core.reshape.merge.merge_ordered
pandas.core.frame.DataFrame.merge
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