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Return a new DataFrame containing the union of rows in this and another DataFrame.
Syntax
union(other: "DataFrame")
Parameters
| Parameter | Type | Description |
|---|---|---|
other |
DataFrame | Another DataFrame that needs to be unioned. |
Returns
DataFrame: A new DataFrame containing the combined rows with corresponding columns.
Notes
This method performs a SQL-style set union of the rows from both DataFrame objects, with no automatic deduplication of elements.
Use the distinct() method to perform deduplication of rows.
The method resolves columns by position (not by name), following the standard behavior in SQL.
Examples
df1 = spark.createDataFrame([(1, 'A'), (2, 'B')], ['id', 'value'])
df2 = spark.createDataFrame([(3, 'C'), (4, 'D')], ['id', 'value'])
df3 = df1.union(df2)
df3.show()
# +---+-----+
# | id|value|
# +---+-----+
# | 1| A|
# | 2| B|
# | 3| C|
# | 4| D|
# +---+-----+
df1 = spark.createDataFrame([(1, 'A'), (2, 'B'), (3, 'C')], ['id', 'value'])
df2 = spark.createDataFrame([(3, 'C'), (4, 'D')], ['id', 'value'])
df3 = df1.union(df2).distinct().sort("id")
df3.show()
# +---+-----+
# | id|value|
# +---+-----+
# | 1| A|
# | 2| B|
# | 3| C|
# | 4| D|
# +---+-----+