Snowpark for Python basics in Snowflake - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Snowpark session.

Snowflake

from snowflake.snowpark import Session

connection_parameters = {
    "account": "my_account",
    "user": "my_user",
    "password": "my_password",
    "role": "SYSADMIN",
    "warehouse": "COMPUTE_WH",
    "database": "MY_DB",
    "schema": "PUBLIC"
}

session = Session.[1](connection_parameters)

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

Aconnect

Bcreate

Cstart

Dopen

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'create' instead of 'connect' causes an AttributeError.

Using 'start' or 'open' are not valid methods for Session.

Use Session.connect() to create a Snowpark session with the given connection parameters.

2fill in blank

medium

Complete the code to create a DataFrame from a table named 'EMPLOYEES'.

Snowflake

df = session.table([1])

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

A"employees"

Bemployees

C"EMPLOYEES"

D'EMPLOYEES'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the table name without quotes causes a NameError.

Using single quotes with uppercase may cause case mismatch.

The table name must be a string with the exact case as in Snowflake, so use double quotes around uppercase 'EMPLOYEES'.

3fill in blank

hard

Fix the error in the code to filter rows where the 'AGE' column is greater than 30.

Snowflake

filtered_df = df.filter(df[[1]] > 30)

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

A"age"

B'AGE'

CAGE

D"AGE"

Attempts:

3 left

💡 Hint

Common Mistakes

Using unquoted column names causes NameError.

Using lowercase 'age' when the column is uppercase causes errors.

Column names in Snowpark DataFrames are case sensitive and should be passed as strings with the exact case, so use double quotes around 'AGE'.

4fill in blank

hard

Fill both blanks to select the 'NAME' column and sort the DataFrame by 'SALARY' descending.

Snowflake

result_df = df.select([1]).order_by([2].desc())

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

A"NAME"

Bdf["SALARY"]

Cdf["NAME"]

D"SALARY"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing column names as strings directly to select causes errors.

Using string 'SALARY' without df[] for order_by causes errors.

Select columns by passing the column expression like df["NAME"]. To sort descending, use df["SALARY"].desc().

5fill in blank

hard

Fill all three blanks to create a new column 'BONUS' as 10% of 'SALARY', filter rows where 'BONUS' is greater than 5000, and show the result.

Snowflake

from snowflake.snowpark.functions import col

result = df.with_column("BONUS", [1]).filter([2] > 5000)
result.[3]()

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

Acol("SALARY") * 0.1

Bcol("BONUS")

Cshow

Dcollect

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'SALARY' as a string instead of col("SALARY") in calculations.

Filtering on 'BONUS' without using col() causes errors.

Using collect() instead of show() to display results.

Create the 'BONUS' column by multiplying the 'SALARY' column by 0.1. Then filter rows where 'BONUS' is greater than 5000 using col("BONUS"). Finally, use show() to display the result.

Practice

1. What is the main purpose of Snowpark for Python in Snowflake?

easy

A. To run Python code directly inside Snowflake for data processing

B. To create visual dashboards from Snowflake data

C. To export Snowflake data to external Python scripts only

D. To replace SQL queries with Java code

Solution

1. Step 1: Understand Snowpark for Python's role

   Snowpark for Python allows running Python code inside Snowflake, enabling fast data processing close to the data.

2. Step 2: Compare options to the main purpose

   Creating visual dashboards, exporting Snowflake data to external Python scripts only, and replacing SQL queries with Java code describe other unrelated tasks or languages, while running Python code directly inside Snowflake for data processing correctly states the main purpose.

3. Final Answer:

   To run Python code directly inside Snowflake for data processing -> Option A

4. Quick Check:

   Snowpark runs Python inside Snowflake = A [OK]

Hint: Snowpark runs Python code inside Snowflake, not outside [OK]

Common Mistakes:

• Confusing Snowpark with visualization tools
• Thinking Snowpark exports data only
• Assuming Snowpark uses Java

2. Which of the following is the correct way to create a Snowpark session in Python?

easy

A. session = SnowflakeSession.connect(connection_parameters)

B. session = snowpark.Session.create(connection_parameters)

C. session = snowflake.snowpark.Session.builder.configs(connection_parameters).create()

D. session = snowflake.connect.snowpark(connection_parameters)

Solution

1. Step 1: Recall Snowpark session creation syntax

   The correct method uses the builder pattern: snowflake.snowpark.Session.builder.configs(...).create()

2. Step 2: Check each option's syntax

   Only session = snowflake.snowpark.Session.builder.configs(connection_parameters).create() matches the official Snowpark Python API syntax; others are incorrect or invalid.

3. Final Answer:

   session = snowflake.snowpark.Session.builder.configs(connection_parameters).create() -> Option C

4. Quick Check:

   Session builder pattern = B [OK]

Hint: Use Session.builder.configs(...).create() to start Snowpark session [OK]

Common Mistakes:

• Using incorrect method names like connect()
• Wrong module or class names
• Missing builder pattern

3. Given the following Snowpark Python code, what will df.show() display?

df = session.table('EMPLOYEES').filter(col('AGE') > 30).select('NAME', 'AGE')

medium

A. A table showing NAME and AGE of employees older than 30

B. A table showing all columns of EMPLOYEES

C. An error because filter syntax is wrong

D. A table showing only AGE column for employees younger than 30

Solution

1. Step 1: Understand the filter condition

   The filter selects rows where AGE > 30, so only employees older than 30 remain.

2. Step 2: Understand the select columns

   Only NAME and AGE columns are selected to display.

3. Final Answer:

   A table showing NAME and AGE of employees older than 30 -> Option A

4. Quick Check:

   Filter AGE > 30 and select NAME, AGE = A [OK]

Hint: Filter then select columns before show() to see filtered data [OK]

Common Mistakes:

• Confusing filter condition syntax
• Expecting all columns to show
• Thinking filter selects AGE < 30

4. What is wrong with this Snowpark Python code snippet?

df = session.table('SALES').filter(col('AMOUNT') < 1000).select('DATE', 'AMOUNT')
df.show()

medium

A. The filter condition should use > instead of <

B. The table name SALES is invalid

C. The show() method does not exist

D. The col function is not imported or defined

Solution

1. Step 1: Check usage of col function

   The col function must be imported from snowflake.snowpark.functions to be used.

2. Step 2: Verify other parts

   Table name SALES can be valid, filter condition < 1000 is valid, and show() exists on DataFrame.

3. Final Answer:

   The col function is not imported or defined -> Option D

4. Quick Check:

   Missing import for col() causes error = C [OK]

Hint: Always import col() from snowflake.snowpark.functions before filtering [OK]

Common Mistakes:

• Forgetting to import col()
• Assuming show() is missing
• Confusing filter operators

5. You want to create a new DataFrame with employee names in uppercase from the EMPLOYEES table using Snowpark for Python. Which code snippet correctly does this?

hard

A. df = session.table('EMPLOYEES').select(upper('NAME'))

B. df = session.table('EMPLOYEES').select(upper(col('NAME')).alias('NAME_UPPER'))

C. df = session.table('EMPLOYEES').select('NAME').map(str.upper)

D. df = session.table('EMPLOYEES').select(col('NAME').upper())

Solution

1. Step 1: Use upper() function with col()

   To convert a column to uppercase, use upper(col('NAME')) and alias it for clarity.

2. Step 2: Check each option's correctness

   df = session.table('EMPLOYEES').select(upper(col('NAME')).alias('NAME_UPPER')) uses correct syntax with upper() and col(). df = session.table('EMPLOYEES').select(col('NAME').upper()) tries to call upper() on a column object, which is invalid. df = session.table('EMPLOYEES').select('NAME').map(str.upper) uses map on DataFrame, which is not supported. df = session.table('EMPLOYEES').select(upper('NAME')) passes string directly to upper(), which is incorrect.

3. Final Answer:

   df = session.table('EMPLOYEES').select(upper(col('NAME')).alias('NAME_UPPER')) -> Option B

4. Quick Check:

   Use upper(col('NAME')) with alias = D [OK]

Hint: Use upper(col('COLUMN')) and alias() to transform columns [OK]

Common Mistakes:

• Calling upper() as a method on column
• Using map() on DataFrame
• Passing column name as string to upper()