Snowpark for Python basics in Snowflake - Mini Project: Build & Apply

Snowpark for Python basics

📖 Scenario: You are working with Snowflake's Snowpark for Python to process data inside the cloud database. You want to create a simple Snowpark session, load some sample data into a DataFrame, and perform a basic transformation.

🎯 Goal: Build a Snowpark Python script that creates a session, loads a small dataset into a DataFrame, filters the data, and shows the result.

📋 What You'll Learn

Create a Snowpark session with exact parameters

Create a DataFrame from a list of tuples with specific data

Filter the DataFrame using a condition on a column

Show the filtered DataFrame content

💡 Why This Matters

🌍 Real World

Snowpark for Python lets you run Python code close to your data inside Snowflake, reducing data movement and improving performance.

💼 Career

Data engineers and analysts use Snowpark to build scalable data pipelines and transformations directly in Snowflake using Python.

1

Create a Snowpark session

Write code to create a Snowpark session called session using Session.builder.configs() with the exact dictionary {'account': 'xy12345', 'user': 'user1', 'password': 'pass123', 'role': 'SYSADMIN', 'warehouse': 'COMPUTE_WH', 'database': 'TEST_DB', 'schema': 'PUBLIC'} and then call .create().

Snowflake

# Your code here to create the Snowpark session named session

Hint

Use Session.builder.configs() with the exact dictionary and then call .create() to make the session.

2

Create a DataFrame with sample data

Create a variable called data that is a list of tuples with these exact entries: (1, 'Apple'), (2, 'Banana'), (3, 'Cherry'). Then create a Snowpark DataFrame called df from data using session.create_dataframe(data, schema=['id', 'fruit']).

Snowflake

from snowflake.snowpark import Session

session = Session.builder.configs({'account': 'xy12345', 'user': 'user1', 'password': 'pass123', 'role': 'SYSADMIN', 'warehouse': 'COMPUTE_WH', 'database': 'TEST_DB', 'schema': 'PUBLIC'}).create()

# Create the data list and DataFrame df below

Hint

Make a list called data with the exact tuples. Then use session.create_dataframe() with data and the schema list ['id', 'fruit'].

3

Filter the DataFrame for fruits with id greater than 1

Create a new DataFrame called filtered_df by filtering df where the id column is greater than 1 using df.filter(df['id'] > 1).

Snowflake

from snowflake.snowpark import Session

session = Session.builder.configs({'account': 'xy12345', 'user': 'user1', 'password': 'pass123', 'role': 'SYSADMIN', 'warehouse': 'COMPUTE_WH', 'database': 'TEST_DB', 'schema': 'PUBLIC'}).create()

data = [(1, 'Apple'), (2, 'Banana'), (3, 'Cherry')]
df = session.create_dataframe(data, schema=['id', 'fruit'])

# Filter df where id > 1 and assign to filtered_df

Hint

Use df.filter(df['id'] > 1) to create filtered_df.

4

Show the filtered DataFrame content

Call filtered_df.show() to display the filtered data.

Snowflake

from snowflake.snowpark import Session

session = Session.builder.configs({'account': 'xy12345', 'user': 'user1', 'password': 'pass123', 'role': 'SYSADMIN', 'warehouse': 'COMPUTE_WH', 'database': 'TEST_DB', 'schema': 'PUBLIC'}).create()

data = [(1, 'Apple'), (2, 'Banana'), (3, 'Cherry')]
df = session.create_dataframe(data, schema=['id', 'fruit'])

filtered_df = df.filter(df['id'] > 1)

# Show the filtered DataFrame

Hint

Call filtered_df.show() to display the filtered rows.

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()