Joins¶
DataFusion supports the following join variants via the method DataFrame.join()
Inner Join
Left Join
Right Join
Full Join
Left Semi Join
Left Anti Join
For the examples in this section we’ll use the following two DataFrames
In [1]: from datafusion import SessionContext
In [2]: ctx = SessionContext()
In [3]: left = ctx.from_pydict(
...: {
...: "customer_id": [1, 2, 3],
...: "customer": ["Alice", "Bob", "Charlie"],
...: }
...: )
...:
In [4]: right = ctx.from_pylist([
...: {"id": 1, "name": "CityCabs"},
...: {"id": 2, "name": "MetroRide"},
...: {"id": 5, "name": "UrbanGo"},
...: ])
...:
Inner Join¶
When using an inner join, only rows containing the common values between the two join columns present in both DataFrames will be included in the resulting DataFrame.
In [5]: left.join(right, join_keys=(["customer_id"], ["id"]), how="inner")
Out[5]:
DataFrame()
+-------------+----------+----+-----------+
| customer_id | customer | id | name |
+-------------+----------+----+-----------+
| 1 | Alice | 1 | CityCabs |
| 2 | Bob | 2 | MetroRide |
+-------------+----------+----+-----------+
The parameter join_keys specifies the columns from the left DataFrame and right DataFrame that contains the values
that should match.
Left Join¶
A left join combines rows from two DataFrames using the key columns. It returns all rows from the left DataFrame and matching rows from the right DataFrame. If there’s no match in the right DataFrame, it returns null values for the corresponding columns.
In [6]: left.join(right, join_keys=(["customer_id"], ["id"]), how="left")
Out[6]:
DataFrame()
+-------------+----------+----+-----------+
| customer_id | customer | id | name |
+-------------+----------+----+-----------+
| 1 | Alice | 1 | CityCabs |
| 2 | Bob | 2 | MetroRide |
| 3 | Charlie | | |
+-------------+----------+----+-----------+
Full Join¶
A full join merges rows from two tables based on a related column, returning all rows from both tables, even if there is no match. Unmatched rows will have null values.
In [7]: left.join(right, join_keys=(["customer_id"], ["id"]), how="full")
Out[7]:
DataFrame()
+-------------+----------+----+-----------+
| customer_id | customer | id | name |
+-------------+----------+----+-----------+
| | | 5 | UrbanGo |
| 1 | Alice | 1 | CityCabs |
| 2 | Bob | 2 | MetroRide |
| 3 | Charlie | | |
+-------------+----------+----+-----------+
Left Semi Join¶
A left semi join retrieves matching rows from the left table while omitting duplicates with multiple matches in the right table.
In [8]: left.join(right, join_keys=(["customer_id"], ["id"]), how="semi")
Out[8]:
DataFrame()
+-------------+----------+
| customer_id | customer |
+-------------+----------+
| 2 | Bob |
| 1 | Alice |
+-------------+----------+
Left Anti Join¶
A left anti join shows all rows from the left table without any matching rows in the right table, based on a the specified matching columns. It excludes rows from the left table that have at least one matching row in the right table.
In [9]: left.join(right, join_keys=(["customer_id"], ["id"]), how="anti")
Out[9]:
DataFrame()
+-------------+----------+
| customer_id | customer |
+-------------+----------+
| 3 | Charlie |
+-------------+----------+