Help on function merge in module pandas.core.reshape.merge:
merge(left, right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False)
Merge DataFrame objects by performing a database-style join operation by
columns or indexes.
If joining columns on columns, the DataFrame indexes *will be
ignored*. Otherwise if joining indexes on indexes or indexes on a column or
columns, the index will be passed on.
Parameters
----------
left : DataFrame
right : DataFrame
how : {'left', 'right', 'outer', 'inner'}, default 'inner'
* left: use only keys from left frame, similar to a SQL left outer join;
preserve key order只保留左表的所有数据
* right: use only keys from right frame, similar to a SQL right outer join;
preserve key order只保留右表的所有数据
* outer: use union of keys from both frames, similar to a SQL full outer
join; sort keys lexicographically保留两个表的所有信息
* inner: use intersection of keys from both frames, similar to a SQL inner
join; preserve the order of the left keys只保留两个表中公共部分的信息
on : label or list
Field names to join on. Must be found in both DataFrames. If on is
None and not merging on indexes, then it merges on the intersection of
the columns by default.
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_index : boolean, default False
Use the index from the left DataFrame as the join key(s). If it is a
MultiIndex, the number of keys in the other DataFrame (either the index
or a number of columns) must match the number of levels
right_index : boolean, default False
Use the index from the right DataFrame as the join key. Same caveats as
left_index
sort : boolean, default False
Sort the join keys lexicographically in the result DataFrame. If False,
the order of the join keys depends on the join type (how keyword)
suffixes : 2-length sequence (tuple, list, ...)
Suffix to apply to overlapping column names in the left and right
side, respectively
copy : boolean, default True
If False, do not copy data unnecessarily
indicator : boolean or string, default False
If True, adds a column to output DataFrame called "_merge" with
information on the source of each row.
If string, column with information on source of each row will be added to
output DataFrame, and column will be named value of string.
Information column is Categorical-type and takes on a value of "left_only"
for observations whose merge key only appears in 'left' DataFrame,
"right_only" for observations whose merge key only appears in 'right'
DataFrame, and "both" if the observation's merge key is found in both.
.. versionadded:: 0.17.0
Examples
--------
>>> A >>> B
lkey value rkey value
0 foo 1 0 foo 5
1 bar 2 1 bar 6
2 baz 3 2 qux 7
3 foo 4 3 bar 8
>>> A.merge(B, left_on='lkey', right_on='rkey', how='outer')
lkey value_x rkey value_y
0 foo 1 foo 5
1 foo 4 foo 5
2 bar 2 bar 6
3 bar 2 bar 8
4 baz 3 NaN NaN
5 NaN NaN qux 7
Returns
-------
merged : DataFrame
The output type will the be same as 'left', if it is a subclass
of DataFrame.
See also
--------
merge_ordered
merge_asof