pandas.DataFrame.merge — pandas 2.2.2 documentation (2024)

Merge DataFrame or named Series objects with a database-style join.

A named Series object is treated as a DataFrame with a single named column.

The join is done on columns or indexes. If joining columns oncolumns, the DataFrame indexes will be ignored. Otherwise if joining indexeson indexes or indexes on a column or columns, the index will be passed on.When performing a cross merge, no column specifications to merge on areallowed.

Parameters:

rightDataFrame or named Series

Object to merge with.

how{‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}, default ‘inner’

Type of merge to be performed.

• left: use only keys from left frame, similar to a SQL left outer join;preserve key order.

• right: use only keys from right frame, similar to a SQL right outer join;preserve key order.

• outer: use union of keys from both frames, similar to a SQL full outerjoin; sort keys lexicographically.

• inner: use intersection of keys from both frames, similar to a SQL innerjoin; preserve the order of the left keys.

• cross: creates the cartesian product from both frames, preserves the orderof the left keys.

onlabel or list

Column or index level names to join on. These must be found in bothDataFrames. If on is None and not merging on indexes then this defaultsto the intersection of the columns in both DataFrames.

left_onlabel or list, or array-like

Column or index level names to join on in the left DataFrame. Can alsobe an array or list of arrays of the length of the left DataFrame.These arrays are treated as if they are columns.

See Also

pandas.merge — pandas 2.2.2 documentationMerge, join, concatenate and compare — pandas 2.2.2 documentationCombining Data in pandas With merge(), .join(), and concat() – Real PythonCombine Data in Pandas with merge, join, and concat • datagy

right_onlabel or list, or array-like

Column or index level names to join on in the right DataFrame. Can alsobe an array or list of arrays of the length of the right DataFrame.These arrays are treated as if they are columns.

left_indexbool, default False

Use the index from the left DataFrame as the join key(s). If it is aMultiIndex, the number of keys in the other DataFrame (either the indexor a number of columns) must match the number of levels.

right_indexbool, default False

Use the index from the right DataFrame as the join key. Same caveats asleft_index.

sortbool, 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).

suffixeslist-like, default is (“_x”, “_y”)

A length-2 sequence where each element is optionally a stringindicating the suffix to add to overlapping column names inleft and right respectively. Pass a value of None insteadof a string to indicate that the column name from left orright should be left as-is, with no suffix. At least one of thevalues must not be None.

copybool, default True

If False, avoid copy if possible.

Note

The copy keyword will change behavior in pandas 3.0.Copy-on-Writewill be enabled by default, which means that all methods with acopy keyword will use a lazy copy mechanism to defer the copy andignore the copy keyword. The copy keyword will be removed in afuture version of pandas.

You can already get the future behavior and improvements throughenabling copy on write pd.options.mode.copy_on_write = True

indicatorbool or str, default False

If True, adds a column to the output DataFrame called “_merge” withinformation on the source of each row. The column can be given a differentname by providing a string argument. The column will have a Categoricaltype with the value of “left_only” for observations whose merge key onlyappears in the left DataFrame, “right_only” for observationswhose merge key only appears in the right DataFrame, and “both”if the observation’s merge key is found in both DataFrames.

validatestr, optional

If specified, checks if merge is of specified type.

• “one_to_one” or “1:1”: check if merge keys are unique in bothleft and right datasets.

• “one_to_many” or “1:m”: check if merge keys are unique in leftdataset.

  See Also

  Python | Pandas Merging, Joining, and Concatenating - GeeksforGeeks

• “many_to_one” or “m:1”: check if merge keys are unique in rightdataset.

• “many_to_many” or “m:m”: allowed, but does not result in checks.

Returns:

DataFrame

A DataFrame of the two merged objects.

Examples

>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],...  'value': [1, 2, 3, 5]})>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],...  'value': [5, 6, 7, 8]})>>> df1 lkey value0 foo 11 bar 22 baz 33 foo 5>>> df2 rkey value0 foo 51 bar 62 baz 73 foo 8

Merge df1 and df2 on the lkey and rkey columns. The value columns havethe default suffixes, _x and _y, appended.

>>> df1.merge(df2, left_on='lkey', right_on='rkey') lkey value_x rkey value_y0 foo 1 foo 51 foo 1 foo 82 bar 2 bar 63 baz 3 baz 74 foo 5 foo 55 foo 5 foo 8

Merge DataFrames df1 and df2 with specified left and right suffixesappended to any overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey',...  suffixes=('_left', '_right')) lkey value_left rkey value_right0 foo 1 foo 51 foo 1 foo 82 bar 2 bar 63 baz 3 baz 74 foo 5 foo 55 foo 5 foo 8

Merge DataFrames df1 and df2, but raise an exception if the DataFrames haveany overlapping columns.

>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))Traceback (most recent call last):...ValueError: columns overlap but no suffix specified: Index(['value'], dtype='object')

>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})>>> df1 a b0 foo 11 bar 2>>> df2 a c0 foo 31 baz 4

>>> df1.merge(df2, how='inner', on='a') a b c0 foo 1 3

>>> df1.merge(df2, how='left', on='a') a b c0 foo 1 3.01 bar 2 NaN

>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})>>> df2 = pd.DataFrame({'right': [7, 8]})>>> df1 left0 foo1 bar>>> df2 right0 71 8

>>> df1.merge(df2, how='cross') left right0 foo 71 foo 82 bar 73 bar 8