pandas.DataFrame.join#
Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list.
Parameters other DataFrame, Series, or a list containing any combination of them
Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame.
on str, list of str, or array-like, optional
Column or index level name(s) in the caller to join on the index in other , otherwise joins index-on-index. If multiple values given, the other DataFrame must have a MultiIndex. Can pass an array as the join key if it is not already contained in the calling DataFrame. Like an Excel VLOOKUP operation.
How to handle the operation of the two objects.
- left: use calling frame’s index (or column if on is specified)
- right: use other ’s index.
- outer: form union of calling frame’s index (or column if on is specified) with other ’s index, and sort it. lexicographically.
- inner: form intersection of calling frame’s index (or column if on is specified) with other ’s index, preserving the order of the calling’s one.
- cross: creates the cartesian product from both frames, preserves the order of the left keys.
Suffix to use from left frame’s overlapping columns.
rsuffix str, default ‘’
Suffix to use from right frame’s overlapping columns.
sort bool, default False
Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword).
validate str, optional
If specified, checks if join is of specified type. * “one_to_one” or “1:1”: check if join keys are unique in both left and right datasets. * “one_to_many” or “1:m”: check if join keys are unique in left dataset. * “many_to_one” or “m:1”: check if join keys are unique in right dataset. * “many_to_many” or “m:m”: allowed, but does not result in checks. .. versionadded:: 1.5.0
A dataframe containing columns from both the caller and other .
For column(s)-on-column(s) operations.
Parameters on , lsuffix , and rsuffix are not supported when passing a list of DataFrame objects.
Support for specifying index levels as the on parameter was added in version 0.23.0.
>>> df = pd.DataFrame('key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'], . 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']>)
>>> df key A 0 K0 A0 1 K1 A1 2 K2 A2 3 K3 A3 4 K4 A4 5 K5 A5
>>> other = pd.DataFrame('key': ['K0', 'K1', 'K2'], . 'B': ['B0', 'B1', 'B2']>)
>>> other key B 0 K0 B0 1 K1 B1 2 K2 B2
Join DataFrames using their indexes.
>>> df.join(other, lsuffix='_caller', rsuffix='_other') key_caller A key_other B 0 K0 A0 K0 B0 1 K1 A1 K1 B1 2 K2 A2 K2 B2 3 K3 A3 NaN NaN 4 K4 A4 NaN NaN 5 K5 A5 NaN NaN
If we want to join using the key columns, we need to set key to be the index in both df and other . The joined DataFrame will have key as its index.
>>> df.set_index('key').join(other.set_index('key')) A B key K0 A0 B0 K1 A1 B1 K2 A2 B2 K3 A3 NaN K4 A4 NaN K5 A5 NaN
Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other ’s index but we can use any column in df . This method preserves the original DataFrame’s index in the result.
>>> df.join(other.set_index('key'), on='key') key A B 0 K0 A0 B0 1 K1 A1 B1 2 K2 A2 B2 3 K3 A3 NaN 4 K4 A4 NaN 5 K5 A5 NaN
Using non-unique key values shows how they are matched.
>>> df = pd.DataFrame('key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'], . 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']>)
>>> df key A 0 K0 A0 1 K1 A1 2 K1 A2 3 K3 A3 4 K0 A4 5 K1 A5
>>> df.join(other.set_index('key'), on='key', validate='m:1') key A B 0 K0 A0 B0 1 K1 A1 B1 2 K1 A2 B1 3 K3 A3 NaN 4 K0 A4 B0 5 K1 A5 B1
pandas.merge#
pandas. 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 = None , indicator = False , validate = None ) [source] #
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 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. When performing a cross merge, no column specifications to merge on are allowed.
If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.
Parameters left DataFrame or named Series right DataFrame or named Series
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 outer join; sort keys lexicographically.
- inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys.
- cross: creates the cartesian product from both frames, preserves the order of the left keys.
Column or index level names to join on. These must be found in both DataFrames. If on is None and not merging on indexes then this defaults to the intersection of the columns in both DataFrames.
left_on label or list, or array-like
Column or index level names to join on in the left DataFrame. Can also be an array or list of arrays of the length of the left DataFrame. These arrays are treated as if they are columns.
right_on label or list, or array-like
Column or index level names to join on in the right DataFrame. Can also be an array or list of arrays of the length of the right DataFrame. These arrays are treated as if they are columns.
left_index bool, 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 bool, default False
Use the index from the right DataFrame as the join key. Same caveats as left_index.
sort bool, 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 list-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.
copy bool, default True
If False, avoid copy if possible.
indicator bool or str, default False
If True, adds a column to the output DataFrame called “_merge” with information on the source of each row. The column can be given a different name by providing a string argument. The column will have a Categorical type with the value of “left_only” for observations whose merge key only appears in the left DataFrame, “right_only” for observations whose merge key only appears in the right DataFrame, and “both” if the observation’s merge key is found in both DataFrames.
validate str, optional
If specified, checks if merge is of specified type.
- “one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
- “one_to_many” or “1:m”: check if merge keys are unique in left dataset.
- “many_to_one” or “m:1”: check if merge keys are unique in right dataset.
- “many_to_many” or “m:m”: allowed, but does not result in checks.
A DataFrame of the two merged objects.
Merge with optional filling/interpolation.
Similar method using indices.
Support for specifying index levels as the on , left_on , and right_on parameters was added in version 0.23.0 Support for merging named Series objects was added in version 0.24.0
>>> 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 value 0 foo 1 1 bar 2 2 baz 3 3 foo 5 >>> df2 rkey value 0 foo 5 1 bar 6 2 baz 7 3 foo 8
Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.
>>> df1.merge(df2, left_on='lkey', right_on='rkey') lkey value_x rkey value_y 0 foo 1 foo 5 1 foo 1 foo 8 2 foo 5 foo 5 3 foo 5 foo 8 4 bar 2 bar 6 5 baz 3 baz 7
Merge DataFrames df1 and df2 with specified left and right suffixes appended to any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey', . suffixes=('_left', '_right')) lkey value_left rkey value_right 0 foo 1 foo 5 1 foo 1 foo 8 2 foo 5 foo 5 3 foo 5 foo 8 4 bar 2 bar 6 5 baz 3 baz 7
Merge DataFrames df1 and df2, but raise an exception if the DataFrames have any 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 b 0 foo 1 1 bar 2 >>> df2 a c 0 foo 3 1 baz 4
>>> df1.merge(df2, how='inner', on='a') a b c 0 foo 1 3
>>> df1.merge(df2, how='left', on='a') a b c 0 foo 1 3.0 1 bar 2 NaN
>>> df1 = pd.DataFrame('left': ['foo', 'bar']>) >>> df2 = pd.DataFrame('right': [7, 8]>) >>> df1 left 0 foo 1 bar >>> df2 right 0 7 1 8
>>> df1.merge(df2, how='cross') left right 0 foo 7 1 foo 8 2 bar 7 3 bar 8