How to Filter a PySpark DataFrame Using SQL-like IN Clause?

Filtering a DataFrame using an SQL-like IN clause is a common requirement when working with PySpark. You can achieve this in multiple ways, such as using the `filter()` or `where()` methods, leveraging the DataFrame DSL, or employing a SQL query. Below, I will provide a comprehensive explanation along with examples to illustrate these approaches.

Approach 1: Using filter() Method

The `filter()` method in PySpark allows filtering rows based on a condition. You can use the `isin()` function to apply an SQL-like IN clause.

Here’s an example:

from pyspark.sql import SparkSession

# Initialize Spark Session
spark = SparkSession.builder.appName("Filter IN Clause").getOrCreate()

# Create a sample DataFrame
data = [(1, "Alice"), (2, "Bob"), (3, "Catherine"), (4, "David")]
df = spark.createDataFrame(data, ["id", "name"])

# Filter using IS IN clause
filtered_df = df.filter(df.name.isin("Alice", "David"))
filtered_df.show()

+---+-----+
| id| name|
+---+-----+
|  1|Alice|
|  4|David|
+---+-----+

Approach 2: Using where() Method

The `where()` method serves the same purpose as `filter()`, and you can also use the `isin()` function with this method.

Here’s an example:

# Filter using WHERE clause
filtered_df = df.where(df.name.isin("Alice", "David"))
filtered_df.show()

+---+-----+
| id| name|
+---+-----+
|  1|Alice|
|  4|David|
+---+-----+

Approach 3: Using SQL Query

You can register the DataFrame as a temporary table and then execute an SQL query that includes the IN clause.

Here’s an example:

# Register the DataFrame as a SQL temporary view
df.createOrReplaceTempView("people")

# Use spark.sql to execute an SQL query
filtered_df = spark.sql("SELECT * FROM people WHERE name IN ('Alice', 'David')")
filtered_df.show()

+---+-----+
| id| name|
+---+-----+
|  1|Alice|
|  4|David|
+---+-----+

Comparison

Here is a simple comparison of the three approaches:

All these methods are equally valid and can be used based on your preference or the specific requirements of your application. The `filter()` and `where()` methods are more suited for programmatic chaining, whereas the SQL approach can be more intuitive for those familiar with SQL syntax.