Type casting and null handling in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to cast the column 'age' to IntegerType.

Apache Spark

df = df.withColumn('age', df['age'].cast([1]))

Drag options to blanks, or click blank then click option'

AIntegerType()

BStringType()

CFloatType()

DBooleanType()

Attempts:

3 left

💡 Hint

Common Mistakes

Using StringType instead of IntegerType causes wrong data type.

Using FloatType when integer is needed.

Forgetting to import IntegerType.

We use IntegerType() to cast the column to integer values.

2fill in blank

medium

Complete the code to replace null values in 'salary' column with 0.

Apache Spark

df = df.fillna({'salary': [1])

Drag options to blanks, or click blank then click option'

A-1

BNone

C''

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using None does not replace nulls.

Using empty string '' for numeric columns causes errors.

Using negative numbers without reason.

We replace nulls in 'salary' with 0 to avoid null errors in calculations.

3fill in blank

hard

Fix the error in casting 'price' column to DoubleType.

Apache Spark

from pyspark.sql.types import [1]
df = df.withColumn('price', df['price'].cast(DoubleType()))

Drag options to blanks, or click blank then click option'

ADoubleType

BStringType

CIntegerType

DFloatType

Attempts:

3 left

💡 Hint

Common Mistakes

Importing FloatType instead of DoubleType.

Forgetting to import the type causes NameError.

Using IntegerType for decimal numbers.

We must import DoubleType to cast to double precision float.

4fill in blank

hard

Complete the code to fill null values in 'age' with 0 and 'name' with 'Unknown' using a dictionary.

Apache Spark

df = df.fillna({'age': [1], 'name': [2])

Drag options to blanks, or click blank then click option'

A0

B'Unknown'

CNone

D''

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string like '0' for the numeric 'age' column.

Forgetting single quotes around 'Unknown'.

Using the same value for both numeric and string columns.

We use a dictionary in fillna() to specify different replacement values for different columns: 0 for numeric 'age' and 'Unknown' for string 'name'.

5fill in blank

hard

Fill all three blanks to conditionally replace nulls in 'salary' with 0 using when and isNull, then cast to DoubleType.

Apache Spark

df = df.withColumn('salary_clean', when(df['salary'].[1](), [2]).otherwise(df['salary']).[3](DoubleType()))

Drag options to blanks, or click blank then click option'

AisNull

Blit(0)

Ccast

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using == 'null' or == None instead of isNull().

Forgetting lit() around the replacement value.

Applying cast() before handling nulls.

Using wrong method like fillna in when clause.

Use isNull() to detect nulls, lit(0) as replacement literal, and cast(DoubleType()) to ensure double type.

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