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.