Load and query sample data using Snowpark Python¶

Introduction¶

This tutorial uses a fictitious food truck brand named Tasty Bytes to show you how to load and query data in Snowflake using Snowpark Python. You use a pre-loaded Python worksheet in Snowsight to complete these tasks.

The following illustration provides an overview of Tasty Bytes.

Prerequisites¶

This tutorial assumes the following:

• You have a supported browser.

• You have a trial account. If you do not have a trial account yet, you can sign up for a free trial. You can choose any Snowflake Cloud Region.

• Your user is the account administrator and is granted the ACCOUNTADMIN system role. For more information, see Using the ACCOUNTADMIN Role.

Step 1. Sign in using Snowsight¶

To access Snowsight over the public Internet, do the following:

1. In a supported web browser, navigate to https://app.snowflake.com.

2. Provide your account identifier or account URL. If you’ve previously signed in to Snowsight, you might see an account name that you can select.

3. Sign in using your Snowflake account credentials.

Step 2. Open the Python worksheet¶

You can use Python worksheets to write and run Python code. Your trial account has access to a pre-loaded Python worksheet for this tutorial. The worksheet has the Python code that you will run to create a database, load data into it, and query the data. For more information about Python worksheets, see Writing Snowpark Code in Python Worksheets.

To open the pre-loaded tutorial Python worksheet:

1. Select Projects » Worksheets to open the list of worksheets.

2. Open [Tutorial] Using Python to load and query sample data.

   Your worksheet looks similar to the following image.

This pre-loaded Python worksheet automatically uses the ACCOUNTADMIN system role so that you can view and manage objects in your account. For more information, see Using the ACCOUNTADMIN Role.

The worksheet also uses the COMPUTE_WH virtual warehouse. A warehouse provides the required resources to create and manage objects and run SQL commands. These resources include CPU, memory, and temporary storage. For more information, see Virtual warehouses.

Step 3. Learn how to use Python worksheets¶

Python worksheets let you use Snowpark Python in Snowsight to run SQL statements. This step in this tutorial describes the code in each step in the Python worksheet. When you use a Python worksheet, you cannot run individual blocks of code separately. You must run the whole worksheet. Before you select Run in the worksheet, review the following steps so that you better understand the Python code.

1. In the open Python worksheet, this step includes the following code:

   import snowflake.snowpark as snowpark
   from snowflake.snowpark.functions import col
   from snowflake.snowpark.types import StructField, StructType, IntegerType, StringType, VariantType
   

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   This tutorial imports the snowpark package and selected classes and functions so that they are available to your code.

2. This step in the worksheet includes the following code:

   def main(session: snowpark.Session):
   

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   This line defines the default main handler function. The handler function contains the code you will run in this tutorial. This line passes in a Session object that you can use to execute SQL statements in Snowflake.

   # Use SQL to create our Tasty Bytes Database
   session.sql('CREATE OR REPLACE DATABASE tasty_bytes_sample_data;').collect()
   

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   This line creates a database named tasty_bytes_sample_data. This database stores data in tables that you can manage and query. For more information, see Databases, Tables and Views - Overview.

   The code uses the sql method to create a DataFrame that represents the results of the SQL statement. In Snowpark, you can query and process data with a DataFrame. The code also uses the collect method to run the SQL statement represented by the DataFrame. The other lines of code in this step in the worksheet also use these methods.

   # Use SQL to create our Raw POS (Point-of-Sale) Schema
   session.sql('CREATE OR REPLACE SCHEMA tasty_bytes_sample_data.raw_pos;').collect()
   

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   This line creates a schema named raw_pos in the tasty_bytes_sample_data database. A schema is a logical grouping of database objects, such as tables and views. For example, a schema might contain the database objects required for a specific application.

   # Use SQL to create our Blob Stage
   session.sql('CREATE OR REPLACE STAGE tasty_bytes_sample_data.public.blob_stage url = "s3://sfquickstarts/tastybytes/" file_format = (type = csv);').collect()
   

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   This line creates a stage named blob_stage. A stage is a location that holds data files to load into a Snowflake database. This tutorial creates a stage that loads data from an Amazon S3 bucket. The tutorial uses an existing bucket with a CSV file that contains the data. It loads the data from this CSV file into the table that is created later in this tutorial. For more information, see Bulk loading from Amazon S3.

3. This step in the worksheet includes the following code:

   # Define our Menu Schema
   menu_schema = StructType([StructField("menu_id",IntegerType()),\
                        StructField("menu_type_id",IntegerType()),\
                        StructField("menu_type",StringType()),\
                        StructField("truck_brand_name",StringType()),\
                        StructField("menu_item_id",IntegerType()),\
                        StructField("menu_item_name",StringType()),\
                        StructField("item_category",StringType()),\
                        StructField("item_subcategory",StringType()),\
                        StructField("cost_of_goods_usd",IntegerType()),\
                        StructField("sale_price_usd",IntegerType()),\
                        StructField("menu_item_health_metrics_obj",VariantType())])
   

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   This code creates a StructType object named menu_schema. This object consists of a list of StructField objects that describe the fields in the CSV file in the stage. For more information, see Working With Files in a Stage.

4. This step in the worksheet includes the following code:

   # Create a Dataframe from our Menu file from our Blob Stage
   df_blob_stage_read = session.read.schema(menu_schema).csv('@tasty_bytes_sample_data.public.blob_stage/raw_pos/menu/')
   

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   This line creates the df_blob_stage_read DataFrame. This DataFrame is configured to read data from the CSV file located in the specified stage, using the specified menu_schema schema. The schema contains information about the types and names of the columns of data.

   # Save our Dataframe as a Menu table in our Tasty Bytes Database and Raw POS Schema
   df_blob_stage_read.write.mode("overwrite").save_as_table("tasty_bytes_sample_data.raw_pos.menu")
   

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   This code uses the save_as_table method to create the menu table and load the data from the stage into it.

5. This step in the worksheet includes the following code:

   # Create a new Dataframe reading from our Menu table and filtering for the Freezing Point brand
   df_menu_freezing_point = session.table("tasty_bytes_sample_data.raw_pos.menu").filter(col("truck_brand_name") == 'Freezing Point')
   

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   This line creates the df_menu_freezing_point DataFrame and configures it to query the menu table. The filter method prepares the SQL for execution with a conditional expression. The conditional expression filters the rows in the menu table to return the rows where the truck_brand_name column equals Freezing Point (similar to a WHERE clause).

   # return our Dataframe
   return df_menu_freezing_point
   

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   This line returns the df_menu_freezing_point DataFrame so that the query is ready for execution. DataFrames are lazily evaluated, which means that this line does not send the query to the server for execution.

When you are ready, select Run to run the code and view the output. When you select Run, the Python worksheet executes the Python code, which generates and executes the SQL statements. The query for the returned DataFrame is executed, and the results are displayed in the worksheet.

Your output looks similar to the following image.

Step 4. Clean up, summary, and additional resources¶

Congratulations! You have successfully completed this tutorial for trial accounts.

Take a few minutes to review a short summary and the key points covered in this tutorial. Consider cleaning up by dropping any objects you created in this tutorial. Learn more by reviewing other topics in the Snowflake Documentation.

DROP DATABASE IF EXISTS tasty_bytes_sample_data;