GroupBy and aggregations in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group the DataFrame by the 'category' column.

Apache Spark

grouped_df = df.[1]('category')

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

AgroupBy

Bselect

Cfilter

DorderBy

Attempts:

3 left

💡 Hint

Common Mistakes

Using select instead of groupBy

Using filter which filters rows, not groups

Using orderBy which sorts data

The groupBy method groups the DataFrame rows by the specified column.

2fill in blank

medium

Complete the code to calculate the average of the 'sales' column after grouping.

Apache Spark

result = df.groupBy('category').[1]('sales')

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

Acount

Bmax

Cavg

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using count which counts rows

Using max or min which find extremes

The avg function calculates the average value of the specified column for each group.

3fill in blank

hard

Fix the error in the code to sum the 'quantity' column after grouping by 'product'.

Apache Spark

total_quantity = df.groupBy('product').[1]('quantity')

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

Asum

Baverage

Csummation

Dtotal

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'average' instead of 'sum'

Using non-existent methods like 'summation' or 'total'

The correct aggregation function to sum values is sum. Other options are invalid method names.

4fill in blank

hard

Fill both blanks to create a dictionary for aggregation: count 'id' and max 'price'.

Apache Spark

agg_result = df.groupBy('store').agg([1]: 'count', [2]: 'max'})

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

A'id'

B'price'

C'quantity'

D'sales'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the columns for count and max

Using columns not present in the DataFrame

The dictionary keys are column names. We count 'id' and find max of 'price'.

5fill in blank

hard

Fill all three blanks to create a dictionary for aggregation: sum 'quantity', avg 'sales', and min 'discount'.

Apache Spark

agg_df = df.groupBy('region').agg([1]: 'sum', [2]: [3], 'discount': 'min'})

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

A'quantity'

B'sales'

C'avg'

D'sum'

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up aggregation functions and column names

Using 'sum' instead of 'avg' for sales

The dictionary keys are column names and values are aggregation functions. We sum 'quantity', average 'sales', and find min 'discount'.

Practice

1. What does the groupBy function do in Apache Spark?

easy

A. It splits the data into groups based on column values.

B. It sorts the data in ascending order.

C. It filters rows based on a condition.

D. It joins two DataFrames together.

Solution

1. Step 1: Understand the purpose of groupBy

   The groupBy function is used to split data into groups based on one or more column values.

2. Step 2: Compare with other functions

   Sorting, filtering, and joining are different operations and do not split data into groups.

3. Final Answer:

   It splits the data into groups based on column values. -> Option A

4. Quick Check:

   groupBy = splits data into groups [OK]

Hint: Remember: groupBy groups rows by column values [OK]

Common Mistakes:

• Confusing groupBy with filter or sort
• Thinking groupBy changes row order
• Assuming groupBy merges DataFrames

2. Which of the following is the correct syntax to calculate the average of column sales after grouping by region in Spark DataFrame df?

easy

A. df.groupBy("region").sum("sales")

B. df.groupBy("region").agg(avg("sales"))

C. df.groupBy("region").filter(avg("sales"))

D. df.groupBy("region").select(avg("sales"))

Solution

1. Step 1: Identify correct aggregation syntax

   To calculate average after grouping, use groupBy followed by agg(avg(column)).

2. Step 2: Check each option

   df.groupBy("region").agg(avg("sales")) uses agg(avg("sales")) correctly. df.groupBy("region").sum("sales") uses sum, not average. df.groupBy("region").filter(avg("sales")) uses filter incorrectly. df.groupBy("region").select(avg("sales")) uses select which is not valid after groupBy for aggregation.

3. Final Answer:

   df.groupBy("region").agg(avg("sales")) -> Option B

4. Quick Check:

   groupBy + agg(avg) = df.groupBy("region").agg(avg("sales")) [OK]

Hint: Use agg() with avg() inside groupBy for averages [OK]

Common Mistakes:

• Using sum instead of avg for average
• Using filter instead of agg
• Using select after groupBy without agg

3. Given the following Spark code, what will be the output schema and result?

from pyspark.sql import SparkSession
from pyspark.sql.functions import sum

spark = SparkSession.builder.getOrCreate()
data = [("East", 100), ("West", 200), ("East", 150)]
df = spark.createDataFrame(data, ["region", "sales"])
result = df.groupBy("region").agg(sum("sales").alias("total_sales"))
result.show()

medium

A. region | total_sales
East | 250
West | 200

B. region | sales
East | 100
West | 200
East | 150

C. region | total_sales
East | 150
West | 200

D. region | total_sales
East | 100
West | 200

Solution

1. Step 1: Understand groupBy and sum aggregation

   The code groups rows by "region" and sums the "sales" values for each group.

2. Step 2: Calculate sums for each region

   For "East": 100 + 150 = 250; For "West": 200.

3. Final Answer:

   region | total_sales East | 250 West | 200 -> Option A

4. Quick Check:

   Sum sales by region = 250 (East), 200 (West) [OK]

Hint: Sum sales per group to get total_sales [OK]

Common Mistakes:

• Not summing all values per group
• Confusing alias name with original column
• Expecting original rows without aggregation

4. Identify the error in the following Spark code snippet:

df.groupBy("category").agg(sum("amount"), avg("amount"))

medium

A. Missing alias for aggregated columns causes error.

B. agg() requires a dictionary, not multiple arguments.

C. groupBy cannot be used with agg.

D. sum and avg functions are not imported.

Solution

1. Step 1: Check function imports

   The code uses sum and avg functions but does not show imports from pyspark.sql.functions.

2. Step 2: Validate other options

   agg() accepts multiple arguments; aliasing is optional; groupBy works with agg.

3. Final Answer:

   sum and avg functions are not imported. -> Option D

4. Quick Check:

   Missing imports cause NameError [OK]

Hint: Always import aggregation functions before use [OK]

Common Mistakes:

• Assuming agg needs dict, but it accepts multiple args
• Forgetting to import sum, avg from pyspark.sql.functions
• Thinking groupBy can't be chained with agg

5. You have a Spark DataFrame sales_df with columns store, product, and revenue. You want to find the maximum revenue per store and the average revenue per product across all stores in a single query. Which code snippet correctly achieves this?

hard

A. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue"))

B. sales_df.agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue"))

C. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).crossJoin(sales_df.groupBy("product").agg(avg("revenue").alias("avg_revenue")))

D. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).groupBy("product").agg(avg("revenue").alias("avg_revenue"))

Solution

1. Step 1: Understand the requirements

   We need max revenue per store and average revenue per product in one result.

2. Step 2: Analyze options

   sales_df.groupBy("store").agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue")) aggregates both by store only, so avg revenue per product is incorrect. sales_df.agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue")) aggregates globally, losing grouping. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).groupBy("product").agg(avg("revenue").alias("avg_revenue")) tries chaining groupBy but overwrites previous aggregation. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).crossJoin(sales_df.groupBy("product").agg(avg("revenue").alias("avg_revenue"))) computes both groupings separately and joins results, correctly combining both summaries.

3. Final Answer:

   sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).crossJoin(sales_df.groupBy("product").agg(avg("revenue").alias("avg_revenue"))) -> Option C

4. Quick Check:

   Separate groupBy + crossJoin combines both aggregations [OK]

Hint: Use separate groupBy then join to combine different aggregations [OK]

Common Mistakes:

• Trying to aggregate different groupings in one groupBy
• Overwriting aggregation by chaining groupBy
• Forgetting to join separate aggregation results