Type casting and null handling in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of type casting with null values in Spark DataFrame

What is the output of the following Spark code snippet?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.getOrCreate()
data = [("1", "100"), ("2", None), (None, "300")]
df = spark.createDataFrame(data, ["id", "value"])
df_cast = df.select(col("id").cast("int").alias("id_int"), col("value").cast("int").alias("value_int"))
df_cast.show()

A

+------+---------+
|id_int|value_int|
+------+---------+
|     1|      100|
|     2|     null|
|  null|      300|
+------+---------+

B

+------+---------+
|id_int|value_int|
+------+---------+
|     1|      100|
|     2|        0|
|     0|      300|
+------+---------+

C RuntimeError: Cannot cast null values to int

D

+------+---------+
|id_int|value_int|
+------+---------+
|     1|     null|
|     2|     null|
|  null|     null|
+------+---------+

💡 Hint

Casting null strings to int results in null values in Spark DataFrame.

When casting string columns to integer in Spark, null string values become null integers. Non-null strings that represent numbers convert correctly.

❓ data_output

intermediate

Count of nulls after type casting in Spark DataFrame

Given the DataFrame below, what is the count of null values in the 'age_int' column after casting 'age' from string to integer?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, isnan, when, count

spark = SparkSession.builder.getOrCreate()
data = [("25",), ("30",), (None,), ("",), ("40",)]
df = spark.createDataFrame(data, ["age"])
df_cast = df.select(col("age").cast("int").alias("age_int"))
null_count = df_cast.filter(col("age_int").isNull()).count()
print(null_count)

A 1

B 2

C 3

D 0

💡 Hint

Empty string and None cast to null integer.

Both None and empty string values become null after casting to integer, so count is 2.

🔧 Debug

advanced

Identify the error in type casting with null handling

What error will this Spark code raise?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder.getOrCreate()
data = [("100",), ("abc",), (None,)]
df = spark.createDataFrame(data, ["score"])
df_cast = df.select(col("score").cast("int").alias("score_int"))
df_cast.show()

A ValueError: invalid literal for int() with base 10: 'abc'

B RuntimeError: Null values not allowed in cast

C TypeError: Cannot cast string to int

D

+---------+
|score_int|
+---------+
|      100|
|     null|
|     null|
+---------+

💡 Hint

Invalid strings become null after casting in Spark, no error is raised.

Spark casts invalid strings to null silently without error during cast to int.

🚀 Application

advanced

Handling nulls after type casting for aggregation

You have a Spark DataFrame with a string column 'price' containing numeric strings and nulls. You want to compute the average price as a float. Which code snippet correctly handles nulls after casting?

A df.selectExpr('avg(cast(price as float)) as avg_price').na.drop().show()

B df.selectExpr('avg(price) as avg_price').show()

C df.selectExpr('avg(cast(price as float)) as avg_price').show()

D df.selectExpr('avg(cast(price as int)) as avg_price').na.fill(0).show()

💡 Hint

Aggregation functions in Spark ignore nulls automatically.

Casting to float and using avg directly works because avg ignores nulls. Filling nulls with 0 or dropping rows is unnecessary and can distort results.

🧠 Conceptual

expert

Effect of type casting on null handling in Spark SQL expressions

In Spark SQL, what happens when you cast a string column containing empty strings and nulls to integer? Choose the most accurate statement.

A Empty strings and nulls both become null integers without error.

B Empty strings cause a runtime error, nulls become zero.

C Empty strings become zero, nulls cause a runtime error.

D Empty strings and nulls both become zero integers silently.

💡 Hint

Spark treats empty strings as nulls during cast to numeric types.

Casting empty strings or nulls to integer results in null values without errors in Spark SQL.

Practice

1. What does the cast() function do in Apache Spark DataFrames?

easy

A. It sorts the DataFrame by a column.

B. It removes null values from a DataFrame.

C. It filters rows based on a condition.

D. It changes the data type of a column to another specified type.

Solution

1. Step 1: Understand the purpose of cast()

   The cast() function is used to convert the data type of a column in a Spark DataFrame to another type, such as from string to integer.

2. Step 2: Compare with other options

   Other options describe different functions: removing nulls, filtering, or sorting, which are not related to type casting.

3. Final Answer:

   It changes the data type of a column to another specified type. -> Option D

4. Quick Check:

   cast() changes type = A [OK]

Hint: Remember: cast() changes column types, not data values [OK]

Common Mistakes:

• Confusing cast() with filtering or sorting functions
• Thinking cast() removes null values
• Assuming cast() modifies data content instead of type

2. Which of the following is the correct syntax to cast a column named age to integer type in Spark DataFrame?

easy

A. df.select(df.age.convert('int'))

B. df.select(df.age.cast('int'))

C. df.select(df.age.toInt())

D. df.select(cast(df.age, 'int'))

Solution

1. Step 1: Recall the correct cast syntax in Spark

   In Spark, to cast a column, you use column.cast('type'). So df.age.cast('int') is correct.

2. Step 2: Check other options for syntax errors

   df.select(cast(df.age, 'int')) uses cast as a function incorrectly. df.select(df.age.toInt()) uses a method toInt() which does not exist in Spark DataFrame columns. df.select(df.age.convert('int')) uses convert() which is not a Spark method.

3. Final Answer:

   df.select(df.age.cast('int')) -> Option B

4. Quick Check:

   Use column.cast('type') syntax = D [OK]

Hint: Use column.cast('type') to cast columns [OK]

Common Mistakes:

• Using Python native methods like toInt() on Spark columns
• Trying to use cast as a standalone function
• Using non-existent methods like convert()

3. Given the following Spark code:

from pyspark.sql.functions import col

df = spark.createDataFrame([("25",), ("30",), (None,)], ["age"])
df2 = df.select(col("age").cast("int").alias("age_int"))
df2.show()

What will be the output?

medium

A. +-------+
|age_int|
+-------+
| 25|
| 30|
| null|
+-------+

B. +-------+
|age_int|
+-------+
| 25|
| 30|
| 0|
+-------+

C. RuntimeError due to casting None to int

D. SyntaxError in code

Solution

1. Step 1: Understand casting behavior with nulls

   Casting a string column with None values to integer results in integer values for valid strings and null for None values, not zero or error.

2. Step 2: Check the output of show()

   The DataFrame will display 25 and 30 as integers, and null for the None entry.

3. Final Answer:

   +-------+ |age_int| +-------+ | 25| | 30| | null| +-------+ -> Option A

4. Quick Check:

   Nulls remain null after cast = B [OK]

Hint: Casting None keeps null, does not convert to zero [OK]

Common Mistakes:

• Expecting None to become 0 after cast
• Thinking casting None causes error
• Confusing syntax errors with runtime behavior

4. Identify the error in this Spark code snippet that tries to replace nulls in column salary with 0:

df = df.withColumn("salary", df["salary"].cast("int").fillna(0))

medium

A. withColumn() requires a function, not a column.

B. cast() cannot convert salary to int.

C. fillna() cannot be called on a Column object; it should be called on DataFrame.

D. The syntax is correct; no error.

Solution

1. Step 1: Understand method chaining on Column vs DataFrame

   In Spark, fillna() is a DataFrame method, not a Column method. Calling fillna() on a Column object causes an error.

2. Step 2: Correct usage

   To replace nulls after casting, use df = df.withColumn('salary', df['salary'].cast('int')) then df = df.fillna({'salary': 0}).

3. Final Answer:

   fillna() cannot be called on a Column object; it should be called on DataFrame. -> Option C

4. Quick Check:

   fillna() on DataFrame, not Column = C [OK]

Hint: Remember: fillna() is for DataFrame, not Column [OK]

Common Mistakes:

• Calling fillna() on a Column instead of DataFrame
• Assuming cast() changes null values automatically
• Misusing withColumn() syntax

5. You have a DataFrame with a string column score containing numeric strings and some nulls. You want to create a new integer column score_int where nulls become -1 after casting. Which code correctly does this?

hard

A. df.withColumn('score_int', when(col('score').isNull(), -1).otherwise(col('score').cast('int')))

B. df.withColumn('score_int', col('score').cast('int').fillna(-1))

C. df.withColumn('score_int', col('score').cast('int').fillna({'score_int': -1}))

D. df.withColumn('score_int', col('score').cast('int').na.fill(-1))

Solution

1. Step 1: Understand null handling with casting

   Casting null strings to int results in null integers. To replace nulls with -1 in the new column, use when() to check nulls before casting.

2. Step 2: Analyze options

   df.withColumn('score_int', when(col('score').isNull(), -1).otherwise(col('score').cast('int'))) uses when() to assign -1 if null, else cast. Options B, C, and D try to fill nulls after casting, but fillna() or na.fill() on columns or DataFrames after casting may not replace nulls correctly or require separate steps.

3. Final Answer:

   df.withColumn('score_int', when(col('score').isNull(), -1).otherwise(col('score').cast('int'))) -> Option A

4. Quick Check:

   Use when() for conditional null replacement before cast = A [OK]

Hint: Use when() to replace nulls before casting [OK]

Common Mistakes:

• Using fillna() on columns instead of DataFrame
• Expecting fillna() to replace nulls after cast automatically
• Not handling nulls before casting