How to Convert a Spark DataFrame Column to a Python List?

To convert a Spark DataFrame column to a Python list, you can use the `collect` method combined with comprehensions or the `toPandas` method to convert the column to a Pandas DataFrame first, then use the `tolist` method. Below are examples using both methods:

Using `collect` Method

The `collect` method retrieves the entire DataFrame or just the specific column’s elements as a list of Rows. You can then extract these values as needed.

Example in PySpark

Let’s assume you have a DataFrame `df` and you want to convert the values in the column “column_name” to a Python list.


from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder.appName("ExampleApp").getOrCreate()

# Sample DataFrame
data = [("Alice", 10), ("Bob", 12), ("Cathy", 14)]
df = spark.createDataFrame(data, ["name", "age"])

# Collect the values of the column 'age'
age_list = df.select("age").rdd.flatMap(lambda x: x).collect()

print(age_list)

[10, 12, 14]

Using `toPandas` Method

The `toPandas` method converts the DataFrame to a Pandas DataFrame. Once you have the Pandas representation, you can easily convert a column to a list using the `tolist` method.

Example in PySpark

Let’s use the same example DataFrame `df` to convert the column “age” to a Python list.


# Convert the DataFrame to Pandas
pandas_df = df.toPandas()

# Extract the 'age' column as a list
age_list = pandas_df["age"].tolist()

print(age_list)

[10, 12, 14]

Conclusion

Both methods are useful, with `collect` being more straightforward for small datasets and simple column extraction. The `toPandas` method is more flexible but can be slower and more memory-intensive for large datasets, so it should be used cautiously based on the size of your data.

About Editorial Team

Our Editorial Team is made up of tech enthusiasts who are highly skilled in Apache Spark, PySpark, and Machine Learning. They are also proficient in Python, Pandas, R, Hive, PostgreSQL, Snowflake, and Databricks. They aren't just experts; they are passionate teachers. They are dedicated to making complex data concepts easy to understand through engaging and simple tutorials with examples.

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