Pyspark Coding Interview Questions 2025 [Most Asked]

Friends If you’re preparing for a PySpark coding interview, it’s important to understand the basics of Apache Spark and how to use it with Python. PySpark is a powerful tool for handling big data and is widely used in data engineering and machine learning.

In this guide, we will cover some common PySpark interview questions, ranging from basic concepts to advanced coding problems. Even If you’re a beginner or an experienced developer, these questions will help you boost your confidence and crack your interview with ease!

Pyspark Coding Interview Questions

1. What is PySpark?

Answer: PySpark is the Python API for Apache Spark, an open-source distributed computing framework. It allows users to process large datasets efficiently using parallel computing.

🔹 Explanation: PySpark provides a simple way to use Spark’s powerful features with Python. It is widely used for big data processing and analytics.

2. How do you create a SparkSession in PySpark?

Answer:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("InterviewSession").getOrCreate()

🔹 Explanation: A SparkSession is required to use PySpark. It acts as the entry point to interact with Spark functionalities.

3. How do you create a DataFrame from a list in PySpark?

Answer:

data = [("Alice", 25), ("Bob", 30)]
columns = ["Name", "Age"]

df = spark.createDataFrame(data, columns)
df.show()

🔹 Explanation: This code creates a DataFrame from a Python list and defines column names. PySpark DataFrames are similar to Pandas DataFrames but work with distributed data.

4. How do you read a CSV file in PySpark?

Answer:

df = spark.read.csv("data.csv", header=True, inferSchema=True)
df.show()

🔹 Explanation: The read.csv() method loads data from a CSV file. header=True tells PySpark that the first row contains column names, and inferSchema=True automatically detects data types.

5. How do you select specific columns from a DataFrame?

Answer:

df.select("Name", "Age").show()

🔹 Explanation: The select() method is used to pick specific columns from a DataFrame. It helps in reducing the data size when only certain columns are needed.

6. How do you filter data in PySpark?

Answer:

df.filter(df.Age > 25).show()

🔹 Explanation: The filter() method is used to retrieve only the rows that satisfy a given condition, similar to SQL’s WHERE clause.

7. How do you add a new column to a DataFrame?

Answer:

from pyspark.sql.functions import col

df = df.withColumn("New_Age", col("Age") + 5)
df.show()

🔹 Explanation: The withColumn() method allows you to add or modify columns in a DataFrame. Here, we create a new column by adding 5 to the “Age” column.

8. How do you group data and get aggregations in PySpark?

Answer:

df.groupBy("Age").count().show()

🔹 Explanation: The groupBy() method is used to group data based on a column, and then we apply an aggregation function like count().

9. How do you join two DataFrames in PySpark?

Answer:

df1.join(df2, df1.ID == df2.ID, "inner").show()

🔹 Explanation: The join() function allows you to combine two DataFrames using a common column. The "inner" argument specifies an inner join.

10. How do you write a DataFrame to a Parquet file?

Answer:

df.write.parquet("output.parquet")

🔹 Explanation: Parquet is a popular columnar storage format optimized for performance. The write.parquet() function saves the DataFrame as a Parquet file.

10 More PySpark Coding Interview Questions with Answers

11. How do you remove duplicate rows in PySpark?

Answer:

df = df.dropDuplicates()
df.show()

🔹 Explanation: The dropDuplicates() method removes duplicate rows from a DataFrame, keeping only unique records.

12. How do you replace null values in a DataFrame?

Answer:

df = df.fillna({"Age": 0, "Name": "Unknown"})
df.show()

🔹 Explanation: The fillna() method replaces null values with a specified value. This is useful for handling missing data.

13. How do you sort a DataFrame in PySpark?

Answer:

df = df.orderBy("Age", ascending=False)
df.show()

🔹 Explanation: The orderBy() method sorts data based on a column. Setting ascending=False sorts it in descending order.

14. How do you count the number of rows in a DataFrame?

Answer:

count = df.count()
print(count)

🔹 Explanation: The count() method returns the number of rows in the DataFrame.

15. How do you create a new column using a UDF (User-Defined Function)?

Answer:

from pyspark.sql.functions import udf
from pyspark.sql.types import IntegerType

def age_plus_five(age):
    return age + 5

age_udf = udf(age_plus_five, IntegerType())
df = df.withColumn("AgePlusFive", age_udf(df.Age))
df.show()

🔹 Explanation: UDFs allow you to apply custom functions to DataFrame columns. Here, we add 5 to the “Age” column using a UDF.

16. How do you get distinct values from a column?

Answer:

df.select("Age").distinct().show()

🔹 Explanation: The distinct() method retrieves unique values from a specified column.

17. How do you drop a column from a DataFrame?

Answer:

df = df.drop("Age")
df.show()

🔹 Explanation: The drop() method removes a column from the DataFrame. This is useful when certain fields are not needed.

18. How do you perform a left join in PySpark?

Answer:

df1.join(df2, df1.ID == df2.ID, "left").show()

🔹 Explanation: A left join keeps all records from the left DataFrame and only matching records from the right DataFrame.

19. How do you apply a SQL query to a DataFrame?

Answer:

df.createOrReplaceTempView("people")
result = spark.sql("SELECT Name, Age FROM people WHERE Age > 25")
result.show()

🔹 Explanation: The createOrReplaceTempView() method registers a DataFrame as a temporary table, allowing SQL queries to be run on it.

20. How do you check the schema of a DataFrame?

Answer:

df.printSchema()

🔹 Explanation: The printSchema() method displays the column names, data types, and nullability information of a DataFrame.

21. How do you convert a PySpark DataFrame to a Pandas DataFrame?

Answer:

pandas_df = df.toPandas()
print(pandas_df.head())

🔹 Explanation: The toPandas() method converts a PySpark DataFrame into a Pandas DataFrame, allowing you to use Pandas-specific functions. However, it should be used only for small datasets due to memory limitations.

22. How do you broadcast a variable in PySpark?

Answer:

from pyspark.sql import SparkSession
from pyspark.sql.functions import broadcast

broadcasted_df = broadcast(df)
broadcasted_df.show()

🔹 Explanation: Broadcasting is used to optimize joins by sending a small DataFrame to all nodes in the cluster, reducing data shuffling.

23. How do you check for null values in a column?

Answer:

from pyspark.sql.functions import col

df.filter(col("Age").isNull()).show()

🔹 Explanation: The isNull() function helps find rows where a column contains null values.

24. How do you drop rows with null values?

Answer:

df = df.dropna()
df.show()

🔹 Explanation: The dropna() method removes rows containing null values. This is useful for cleaning data before processing.

25. How do you replace values in a PySpark DataFrame?

Answer:

df = df.replace({"Alice": "Alicia", "Bob": "Robert"})
df.show()

🔹 Explanation: The replace() method allows replacing specific values in a DataFrame.

26. How do you check the number of partitions in a DataFrame?

Answer:

print(df.rdd.getNumPartitions())

🔹 Explanation: The getNumPartitions() method shows how many partitions a DataFrame is split into. Partitions help in parallel processing.

27. How do you repartition a DataFrame?

Answer:

df = df.repartition(4)
print(df.rdd.getNumPartitions())

🔹 Explanation: The repartition() method changes the number of partitions in a DataFrame to improve performance in distributed environments.

28. How do you cache a DataFrame in memory?

Answer:

df.cache()
df.show()

🔹 Explanation: Caching stores the DataFrame in memory, reducing computation time for repeated operations.

29. How do you explode a column containing lists?

Answer:

from pyspark.sql.functions import explode

df = df.withColumn("ExplodedColumn", explode(df.ListColumn))
df.show()

🔹 Explanation: The explode() function splits an array or list column into multiple rows, one for each element.

30. How do you write a DataFrame to a JSON file?

Answer:

df.write.json("output.json")

🔹 Explanation: The write.json() function saves the DataFrame in JSON format, preserving its structure.

31. How do you convert a DataFrame column to lowercase?

Answer:

from pyspark.sql.functions import lower

df = df.withColumn("Lower_Name", lower(df.Name))
df.show()

🔹 Explanation: The lower() function converts text to lowercase, which is useful for normalization.

32. How do you get the first N rows from a DataFrame?

Answer:

df.show(5)

🔹 Explanation: The show(n) method displays the first n rows of a DataFrame.

33. What is the difference between repartition() and coalesce()?

Answer:

• repartition(n): Increases or decreases partitions and performs a full shuffle.
• coalesce(n): Reduces partitions without a full shuffle, making it more efficient for downsizing.

🔹 Explanation: Use coalesce() for reducing partitions efficiently and repartition() when increasing or distributing partitions evenly.

34. Which method is used to register a DataFrame as a SQL table?

A) registerTable()
B) createOrReplaceTempView()
C) toSQL()
D) sqlTable()

✅ Answer: B) createOrReplaceTempView()

🔹 Explanation: This method registers a DataFrame as a temporary SQL table so that SQL queries can be performed on it.

35. Which function is used to read a Parquet file in PySpark?

A) spark.loadParquet("file.parquet")
B) spark.read.parquet("file.parquet")
C) spark.parquet("file.parquet")
D) spark.read.format("parquet").load("file.parquet")

✅ Answer: B) spark.read.parquet(“file.parquet”)

🔹 Explanation: read.parquet() is the simplest and most efficient way to load Parquet files in PySpark.

36. How do you get the column names of a DataFrame?

Answer:

print(df.columns)

🔹 Explanation: The columns attribute returns a list of column names in a DataFrame.

37. Which PySpark function is used for window functions?

A) window()
B) groupBy()
C) partitionBy()
D) over()

✅ Answer: D) over()

🔹 Explanation: The over() function is used with window functions like row_number() or rank() to perform operations over a specific window of rows.

38. How do you apply a filter using multiple conditions?

Answer:

df.filter((df.Age > 25) & (df.Salary > 50000)).show()

🔹 Explanation: The & operator is used to apply multiple conditions in a filter() function.

39. Which PySpark operation is used for mapping transformations?

A) map()
B) apply()
C) transform()
D) lambda()

✅ Answer: A) map()

🔹 Explanation: The map() function applies a transformation to each row in an RDD.

40. How do you union two DataFrames in PySpark?

Answer:

df3 = df1.union(df2)
df3.show()

🔹 Explanation: The union() function combines two DataFrames with the same schema into one.

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