Snowpark for Python basics in Snowflake - Step-by-Step Execution

Process Flow - Snowpark for Python basics

Start: Connect to Snowflake

↓

Create Snowpark Session

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Load Data into DataFrame

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Transform Data (e.g., filter, select)

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Show or Collect Results

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End Session

This flow shows how to connect to Snowflake, create a Snowpark session, load data, transform it, and get results.

Execution Sample

Snowflake

from snowflake.snowpark import Session

session = Session.builder.configs({...}).create()
df = session.table('MY_TABLE')
df_filtered = df.filter(df['AGE'] > 30)
df_filtered.show()

Connects to Snowflake, loads a table, filters rows where AGE > 30, and shows the results.

Process Table

Step	Action	Input/Condition	Result/Output
1	Create Session	Configs provided	Session object created
2	Load Table	Table name: MY_TABLE	DataFrame with all rows from MY_TABLE
3	Filter DataFrame	Condition: AGE > 30	New DataFrame with filtered rows
4	Show DataFrame	Call show()	Prints filtered rows to console
5	End	No more actions	Session can be closed or reused

💡 All steps completed; data filtered and displayed successfully.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4
session	None	Session object	Session object	Session object	Session object
df	None	None	DataFrame with all MY_TABLE rows	DataFrame with all MY_TABLE rows	DataFrame with all MY_TABLE rows
df_filtered	None	None	None	DataFrame filtered AGE > 30	DataFrame filtered AGE > 30

Key Moments - 3 Insights

Why do we create a session first before loading data?

The session connects your Python code to Snowflake. Without it, you cannot access tables or run queries. See execution_table step 1.

Does filtering change the original DataFrame?

No, filtering creates a new DataFrame with the filtered data. The original stays unchanged. See execution_table steps 2 and 3.

What does the show() method do?

It prints the DataFrame rows to the console for you to see. It does not return data to Python variables. See execution_table step 4.

Visual Quiz - 3 Questions

Look at the execution_table, what is the state of 'df' after step 3?

A Session object

B DataFrame filtered with AGE > 30

C DataFrame with all rows from MY_TABLE

D None

💡 Hint

Check the variable_tracker for 'df' after step 3.

At which step is the data actually filtered?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Look at the execution_table action descriptions.

If you skip creating the session, what happens?

A You cannot connect to Snowflake

B You can still load tables

C Filtering works but no data shows

D show() prints empty output

💡 Hint

Refer to key_moments about session creation.

Concept Snapshot

Snowpark for Python basics:
- Create a Session to connect to Snowflake
- Load data as a DataFrame from a table
- Use DataFrame methods like filter() to transform data
- Use show() to display results
- Session manages connection lifecycle

Full Transcript

This lesson shows how to use Snowpark for Python to work with data in Snowflake. First, you create a session object that connects your Python code to Snowflake. Then you load a table into a DataFrame. You can transform the data by filtering rows, for example selecting only rows where AGE is greater than 30. Finally, you use the show() method to print the filtered data to the console. The session object remains active until you close it or end your program. This step-by-step process helps beginners see how Snowpark manages data access and transformation in a simple way.