This post explains how to filter values from a PySpark array column.

It also explains how to filter DataFrames with array columns (i.e. reduce the number of rows in a DataFrame).

Filtering values from an ArrayType column and filtering DataFrame rows are completely different operations of course. The pyspark.sql.DataFrame#filter method and the pyspark.sql.functions#filter function share the same name, but have different functionality. One removes elements from an array and the other removes rows from a DataFrame.

It’s important to understand both. The rest of this post provides clear examples.

filter array column

Suppose you have the following DataFrame with a some_arr column that contains numbers.

df = spark.createDataFrame(
    [([1, 2, 3, 5, 7],), ([2, 4, 9],)], ["some_arr"]
)
df.show()

+---------------+
|       some_arr|
+---------------+
|[1, 2, 3, 5, 7]|
|      [2, 4, 9]|
+---------------+

Use filter to append an arr_evens column that only contains the even numbers from some_arr:

from pyspark.sql.functions import *

is_even = lambda x: x % 2 == 0
res = df.withColumn("arr_evens", filter(col("some_arr"), is_even))
res.show()

+---------------+---------+
|       some_arr|arr_evens|
+---------------+---------+
|[1, 2, 3, 5, 7]|      [2]|
|      [2, 4, 9]|   [2, 4]|
+---------------+---------+

The vanilla filter method in Python works similarly:

list(filter(is_even, [2, 4, 9])) # [2, 4]

The Spark filter function takes is_even as the second argument and the Python filter function takes is_even as the first argument. It’s never easy 😉

Now let’s turn our attention to filtering entire rows.

filter rows if array column contains a value

Suppose you have the following DataFrame.

df = spark.createDataFrame(
    [(["one", "two", "three"],), (["four", "five"],), (["one", "nine"],)], ["some_arr"]
)
df.show()

+-----------------+
|         some_arr|
+-----------------+
|[one, two, three]|
|     [four, five]|
|      [one, nine]|
+-----------------+

Here’s how to filter out all the rows that don’t contain the string one:

res = df.filter(array_contains(col("some_arr"), "one"))
res.show()

+-----------------+
|         some_arr|
+-----------------+
|[one, two, three]|
|      [one, nine]|
+-----------------+

array_contains makes for clean code.

where() is an alias for filter so df.where(array_contains(col("some_arr"), "one")) will return the same result.

filter on if at least one element in an array meets a condition

Create a DataFrame with some words:

df = spark.createDataFrame(
    [(["apple", "pear"],), (["plan", "pipe"],), (["cat", "ant"],)], ["some_words"]
)
df.show()

+-------------+
|   some_words|
+-------------+
|[apple, pear]|
| [plan, pipe]|
|   [cat, ant]|
+-------------+

Filter out all the rows that don’t contain a word that starts with the letter a.

starts_with_a = lambda s: s.startswith("a")
res = df.filter(exists(col("some_words"), starts_with_a))
res.show()

+-------------+
|   some_words|
+-------------+
|[apple, pear]|
|   [cat, ant]|
+-------------+

exists lets you model powerful filtering logic.

See the PySpark exists and forall post for a detailed discussion of exists and the other method we’ll talk about next, forall.

filter if all elements in an array meet a condition

Create a DataFrame with some integers:

df = spark.createDataFrame(
    [([1, 2, 3, 5, 7],), ([2, 4, 9],), ([2, 4, 6],)], ["some_ints"]
)
df.show()

+---------------+
|      some_ints|
+---------------+
|[1, 2, 3, 5, 7]|
|      [2, 4, 9]|
|      [2, 4, 6]|
+---------------+

Filter out all the rows that contain any odd numbers.

is_even = lambda x: x % 2 == 0
res = df.filter(forall(col("some_ints"), is_even))
res.show()

+---------+
|some_ints|
+---------+
|[2, 4, 6]|
+---------+

forall is useful when filtering.

Conclusion

PySpark has a pyspark.sql.DataFrame#filter method and a separate pyspark.sql.functions.filter function. Both are important, but they’re useful in completely different contexts.

The filter function was added in Spark 3.1, whereas the filter method has been around since the early days of Spark (1.3). The filter method is especially powerful when used with multiple conditions or with forall / exsists (methods added in Spark 3.1).

PySpark 3 has added a lot of developer friendly functions and makes big data processing with Python a delight.