Question

Which of the following pairs of arguments cannot be used in DataFrame.join() to perform an inner Join on two DataFrames, named and aliased with “a” and “b” respectively, to specify two key columns?

A. on = [a.column1 == b.column1, a.column2 == b.column2]
B. on = [col(“column1”), col(“column2”)]
C. on = [col(“a.column1”) == col(“b.column1”), col(“a.column2”) == col(“b.column2”)]
D. All of these options can be used to perform an inner join with two key columns.
E. on = [“column1”, “column2”]

Answer

D. All of these options can be used to perform an inner join with two key columns.

Explanation 1

The correct answer is option C, on = [col(“a.column1”) == col(“b.column1”), col(“a.column2”) == col(“b.column2”)]. This is because the “on” argument in DataFrame.join() is used to specify the columns on which to perform the join, and in this case, we have two DataFrames named and aliased with “a” and “b” respectively. The “on” argument should contain the conditions to join the two DataFrames on two key columns. Option A is a valid way to specify two key columns, but it uses the equality operator twice. Option B uses column expressions, which is also valid. Option D is incorrect because not all options can be used to perform an inner join with two key columns. Option E uses column names, which is also a valid way to specify two key columns.

Explanation 2

df_a.join(df_b, on=[a.column1 == b.column1, a.column2 == b.column2])
df_a.join(df_b, on=[col("column1"), col("column2")])
df_a.join(df_b, on=["column1", "column2"])

df_a.join(df_b, on=[col("a.column1") == col("b.column1"), col("a.column2") == col("b.column2")])

Explanation 3

The pair of arguments that cannot be used in DataFrame.join() to perform an inner join on two DataFrames, named and aliased with “a” and “b” respectively, to specify two key columns is:

B. on = [col(“column1”), col(“column2”)]

Explanation:

When performing an inner join on two DataFrames using the DataFrame.join() operation, the on parameter is used to specify the join expression. The on parameter can be specified as:

• A list of columns or expressions that should be used as the join key.
• A single column or expression that should be used as the join key.
• A dictionary that specifies the join key on a per-DataFrame basis.

Options A, C, and E all specify a list of columns or expressions to be used as the join key, which is a valid way to perform an inner join on two DataFrames. Option B, however, specifies a list of column objects but does not specify the columns to join on. Therefore, option B is not a valid way to perform an inner join on two DataFrames.

Note that options A and C also demonstrate different ways to specify column aliases when joining DataFrames that have been aliased with different names. Option A uses the aliases “a” and “b” to refer to the columns, while option C uses the col() function to specify the DataFrame and column names separately.

Explanation 4

Explanation 5

The correct answer is B. The `on` parameter in the `join()` method is used to specify the columns to join on. In option B, the columns are specified directly using the `col()` function. In option A, the columns are specified using a list of conditions. In option C, the columns are specified using a list of conditions with column aliases. Option D is incorrect because all of the options cannot be used to perform an inner join with two key columns. Option E is incorrect because it specifies the column names as strings instead of using the `col()` function.

Explanation 6

Explanation 7

The correct answer is B. The `on` parameter in the `join()` method is used to specify the join condition. It can be a column name or an expression. In option B, the `on` parameter is set to a list of column names. This is not a valid syntax for specifying join conditions in Spark SQL. The correct syntax for specifying join conditions is to use expressions that compare columns from the two DataFrames. Option A uses expressions to compare columns from the two DataFrames, so it is a valid syntax for specifying join conditions. Option C also uses expressions to compare columns from the two DataFrames, but it uses the `col()` function to reference the columns, which is also a valid syntax. Option D is incorrect because option B cannot be used to perform an inner join with two key columns. Option E is incorrect because it specifies column names instead of expressions that compare columns from the two DataFrames.

Explanation 8

The correct answer is B. The `on` parameter in DataFrame.join() method is used to specify the join condition between the two DataFrames. It can be a column name or a list of column names. In option B, the `on` parameter is a list of column names which is not a valid argument for performing an inner join on two DataFrames with two key columns.

Option A is correct because it specifies two key columns for the join operation. Option C is also correct because it specifies two key columns for the join operation and uses aliases to differentiate between the two DataFrames. Option D is incorrect because all of these options can be used to perform an inner join with two key columns. Option E is incorrect because it specifies only column names and not the DataFrame names.

Explanation 9

The correct answer is B. The key column storeID needs to be in a list like [“storeID”]. Here is a detailed explanation:

B. The key column storeID needs to be in a list like [“storeID”].

This is true because the on argument of the join() operation can accept either a single column name or a list of column names, but not a Column object. The col() operation returns a Column object that can be used for expressions or conditions, but not for specifying join keys. The code block shown in the question will cause an error because it passes a Column object as the on argument. The correct code block should be:

a.join(b, on=[“storeID”], how=”inner”)

A. on = [a.column1 == b.column1, a.column2 == b.column2] This is false because this form of expression can be used to perform an inner join on two DataFrames with two key columns. The expression specifies the join condition as a list of boolean expressions that compare the columns from both DataFrames.

C. on = [col(“a.column1”) == col(“b.column1”), col(“a.column2”) == col(“b.column2”)] This is false because this form of expression can also be used to perform an inner join on two DataFrames with two key columns. The expression specifies the join condition as a list of boolean expressions that compare the columns from both DataFrames using the col() operation.

D. All of these options can be used to perform an inner join with two key columns. This is false because option B cannot be used to perform an inner join with two key columns, as explained above. E. on = [“column1”, “column2”] This is false because this form of expression can be used to perform an inner join on two DataFrames with two key columns, assuming that both DataFrames have the same column names. The expression specifies the join keys as a list of column names.

Explanation 10

The correct answer is B. on = [col(“column1”), col(“column2”)]

Explanation:

Option A: on = [a.column1 == b.column1, a.column2 == b.column2]
This option is valid for joining two DataFrames on multiple columns using the join condition with the specified aliases “a” and “b”.

Option B: on = [col(“column1”), col(“column2”)]
This option is incorrect because it only lists the column names without specifying the join condition between the two DataFrames. It should use a comparison operator (==) to compare the columns from both DataFrames.

Option C: on = [col(“a.column1”) == col(“b.column1”), col(“a.column2”) == col(“b.column2”)]
This option is valid for joining two DataFrames on multiple columns using the join condition with the specified aliases “a” and “b” and the col() function.

Option D: All of these options can be used to perform an inner join with two key columns.
This option is incorrect because option B is not a valid way to perform an inner join with two key columns.

Option E: on = [“column1”, “column2”]
This option is valid for joining two DataFrames on multiple columns when the column names are the same in both DataFrames.

Explanation 11

The correct answer is D. All of these options can be used to perform an inner join with two key columns.

In Apache Spark, DataFrame.join() provides multiple ways to specify the join condition. The join condition can be expressed using column expressions, column names, or a combination of both.

Let’s go through each option:

A. on = [a.column1 == b.column1, a.column2 == b.column2]
This option uses column expressions to specify the join condition. It compares column1 from DataFrame “a” with column1 from DataFrame “b” and column2 from DataFrame “a” with column2 from DataFrame “b”. This is a valid way to specify the join condition for an inner join on two key columns.

B. on = [col(“column1”), col(“column2”)]
This option uses col() function to specify the join condition. It selects column1 and column2 from both DataFrames. This is also a valid way to specify the join condition for an inner join on two key columns.

C. on = [col(“a.column1”) == col(“b.column1”), col(“a.column2”) == col(“b.column2”)]
This option combines column expressions and aliases to specify the join condition. It compares column1 from DataFrame “a” with column1 from DataFrame “b” using aliases, and column2 from DataFrame “a” with column2 from DataFrame “b” using aliases. This is another valid way to specify the join condition for an inner join on two key columns.

E. on = [“column1”, “column2”]
This option directly uses column names to specify the join condition. It selects column1 and column2 from both DataFrames. This is also a valid way to specify the join condition for an inner join on two key columns.

All of the given options are valid and can be used to perform an inner join with two key columns. Therefore, the correct answer is D. All of these options can be used to perform an inner join with two key columns.

Reference

• pandas.DataFrame.join — pandas 2.0.2 documentation (pydata.org)
• pandas.DataFrame.merge — pandas 2.0.2 documentation (pydata.org)
• Combining Data in pandas With merge(), .join(), and concat() – Real Python
• Tutorial: Work with PySpark DataFrames on Databricks | Databricks on AWS
• Tutorial: Work with PySpark DataFrames on Azure Databricks – Azure Databricks | Microsoft Learn
• PySpark Join Multiple Columns – Spark By {Examples} (sparkbyexamples.com)
• python – How to join on multiple columns in Pyspark? – Stack Overflow
• How to join on multiple columns in Pyspark? – GeeksforGeeks
• PySpark Join on Multiple Columns | Join Two or Multiple Dataframes (educba.com)
• How to join multiple columns in PySpark Azure Databricks? (azurelib.com)
• Solved: Is there a better method to join two dataframes an… – Databricks – 30559

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