DataFrame API in Snowpark in Snowflake - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a DataFrame in Snowpark?

A DataFrame in Snowpark is like a table in a database. It holds data in rows and columns and lets you work with that data using simple commands.

beginner

How do you create a DataFrame from a Snowflake table using Snowpark?

You use the session object and call table("table_name"). For example, df = session.table("my_table") creates a DataFrame from the table named 'my_table'.

beginner

What does the filter() method do in a Snowpark DataFrame?

The filter() method selects rows that meet a condition. It’s like picking only the data you want based on rules you set.

intermediate

Explain the difference between select() and filter() in Snowpark DataFrames.

select() chooses which columns to keep, while filter() chooses which rows to keep based on conditions.

beginner

How does Snowpark DataFrame help with processing data in Snowflake?

Snowpark DataFrame lets you write code that runs inside Snowflake, so data stays safe and processing is fast without moving data outside.

Which method creates a DataFrame from a Snowflake table in Snowpark?

A session.createDataFrame()

B session.loadTable()

C session.table("table_name")

D session.newDataFrame()

The correct method to create a DataFrame from an existing table is session.table("table_name").

What does the filter() method do in a Snowpark DataFrame?

A Selects rows based on a condition

B Selects columns

C Joins two DataFrames

D Sorts the DataFrame

filter() selects rows that meet a condition.

Which method would you use to choose specific columns in a Snowpark DataFrame?

A filter()

B select()

C groupBy()

D join()

select() is used to choose specific columns.

Why is processing data with Snowpark DataFrame efficient?

A Because it runs code inside Snowflake without moving data

B Because it uses external servers

C Because it moves data to your local machine

D Because it exports data to CSV first

Snowpark runs code inside Snowflake, keeping data secure and processing fast.

What is the main structure used to hold data in Snowpark?

A Array

B Dictionary

C List

D DataFrame

DataFrame is the main structure to hold and work with data in Snowpark.

Describe how you would create and filter a DataFrame in Snowpark.

Think about starting from a table and then picking rows you want.

Explain the difference between selecting columns and filtering rows in a Snowpark DataFrame.

One picks what data fields to keep, the other picks which records to keep.

Practice

1. What does the filter() method do in Snowpark's DataFrame API?

easy

A. It adds new columns to the DataFrame.

B. It changes the column names of the DataFrame.

C. It selects rows that meet a specified condition.

D. It sorts the DataFrame by a column.

Solution

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

   The filter() method is used to keep only rows that satisfy a condition.

2. Step 2: Compare with other methods

   Changing column names is done by withColumnRenamed(), adding columns by withColumn(), and sorting by orderBy().

3. Final Answer:

   It selects rows that meet a specified condition. -> Option C

4. Quick Check:

   filter() = select rows [OK]

Hint: Filter keeps rows matching condition, like a sieve [OK]

Common Mistakes:

• Confusing filter() with select()
• Thinking filter() changes columns
• Using filter() to sort data

2. Which of the following is the correct way to select columns name and age from a DataFrame df in Snowpark?

easy

A. df.select('name', 'age')

B. df.filter('name', 'age')

C. df.orderBy('name', 'age')

D. df.withColumn('name', 'age')

Solution

1. Step 1: Identify method to pick columns

   The select() method is used to choose specific columns from a DataFrame.

2. Step 2: Check other methods

   filter() is for rows, orderBy() sorts rows, and withColumn() adds or modifies columns.

3. Final Answer:

   df.select('name', 'age') -> Option A

4. Quick Check:

   Select columns = select() [OK]

Hint: Use select() to pick columns, not filter() or orderBy() [OK]

Common Mistakes:

• Using filter() to select columns
• Trying to order columns instead of rows
• Confusing withColumn() with select()

3. Given the following Snowpark code:

df = session.create_dataframe([(1, 'Alice'), (2, 'Bob'), (3, 'Carol')], schema=['id', 'name'])
df_filtered = df.filter(df['id'] > 1).select('name')
df_filtered.show()

What will be the output?

medium

A. Rows with names 'Bob' and 'Carol'

B. Rows with names 'Alice' only

C. Rows with ids 1, 2, and 3

D. Empty DataFrame

Solution

1. Step 1: Understand the filter condition

   The filter keeps rows where id > 1, so rows with id 2 and 3 remain.

2. Step 2: Select the 'name' column

   After filtering, only the 'name' column is selected, so output shows 'Bob' and 'Carol'.

3. Final Answer:

   Rows with names 'Bob' and 'Carol' -> Option A

4. Quick Check:

   Filter id>1 then select name = Bob, Carol [OK]

Hint: Filter first, then select columns to get desired rows [OK]

Common Mistakes:

• Including 'Alice' who has id 1
• Showing all columns instead of just 'name'
• Expecting empty output

4. What is wrong with this Snowpark code snippet?

df = session.create_dataframe([(10, 'X'), (20, 'Y')], schema=['score', 'label'])
df_filtered = df.filter(df['score'] = 20)
df_filtered.show()

medium

A. Using show() instead of collect()

B. Missing parentheses around filter condition

C. Incorrect schema definition

D. Using single equals (=) instead of double equals (==) in filter condition

Solution

1. Step 1: Check filter condition syntax

   The filter condition uses = which is assignment, not comparison. It should be ==.

2. Step 2: Verify other parts

   Schema is correct, parentheses are fine, and show() is valid to display results.

3. Final Answer:

   Using single equals (=) instead of double equals (==) in filter condition -> Option D

4. Quick Check:

   Use == for comparison in filter [OK]

Hint: Use == for conditions, not = [OK]

Common Mistakes:

• Using = instead of == in conditions
• Thinking show() is invalid
• Confusing schema errors with syntax errors

5. You want to create a new column age_group in a Snowpark DataFrame df that labels ages below 18 as 'minor' and 18 or above as 'adult'. Which code snippet correctly achieves this?

hard

A. df.select('age_group', F.when(df['age'] < 18, 'minor').otherwise('adult'))

B. df.withColumn('age_group', F.when(df['age'] < 18, 'minor').otherwise('adult'))

C. df.filter(df['age_group'] == 'minor')

D. df.orderBy('age_group')

Solution

1. Step 1: Understand how to add a conditional column

   The withColumn() method adds or replaces a column. Using F.when().otherwise() sets values conditionally.

2. Step 2: Check other options

   select() chooses columns but does not add new ones, filter() filters rows, and orderBy() sorts rows.

3. Final Answer:

   df.withColumn('age_group', F.when(df['age'] < 18, 'minor').otherwise('adult')) -> Option B

4. Quick Check:

   Add conditional column with withColumn + when [OK]

Hint: Use withColumn and when() to add conditional columns [OK]

Common Mistakes:

• Using select() to add columns
• Filtering instead of adding columns
• Ordering instead of creating new columns