Common setup for Snowflake Python APIs tutorials¶

Introduction¶

This topic provides instructions for the common setup required for all Snowflake Python APIs tutorials available in this documentation.

from snowflake.snowpark import Session
from snowflake.core import Root

session = Session.builder.configs(connection_params).create()
root = Root(session)

Install the Snowflake Python APIs¶

Before installing the API, you need to activate a Python environment.

In this tutorial, you can use conda or a virtual environment (venv).

1. To create and activate a conda or virtual environment, open a command line terminal and run the following commands:

   condavenv

   conda create -n <env_name> python==3.10
   conda activate <env_name>
   

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2. The Snowflake Python APIs package is available in PyPI.

   To install the API package in the new conda or virtual environment, run the following command:

   pip install snowflake -U
   

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Set up your development environment¶

This tutorial walks through code examples that you can run in a Jupyter notebook. Each step in the tutorial incrementally showcases the capabilities of the Snowflake Python APIs.

You start by setting up your development environment so that you can run the code examples in a notebook.

1. Create a file named $HOME/.snowflake/connections.toml with the following connection parameters, and update it with your real credentials:

   [default]
   account = "<YOUR ACCOUNT NAME>"
   user = "<YOUR ACCOUNT USER>"
   password = "<YOUR ACCOUNT USER PASSWORD>"
   # optional
   # warehouse = "<YOUR COMPUTE WH>"
   # optional
   # database = "<YOUR DATABASE>"
   # optional
   # schema = "<YOUR SCHEMA>"
   

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   Note

   The account parameter does not support account identifiers with underscores. You must specify an account identifier with dashes in place of any underscores. For more information, see Account name in your organization.

   This example specifies these parameters as the default connection to Snowflake in your environment by creating a connection definition named default.

2. Use one of the following methods to open a notebook:

   • Open a new notebook in a code editor that supports Jupyter notebooks (such as Visual Studio Code).

   • To open a notebook in your browser, start a notebook server with the command jupyter notebook.

     To ensure that your environment can run a notebook, run conda install notebook in your terminal before starting the notebook server.

3. In the first cell of the notebook, run the following import statements:

   from datetime import timedelta
   
   from snowflake.snowpark import Session
   from snowflake.snowpark.functions import col
   from snowflake.core import Root, CreateMode
   from snowflake.core.database import Database
   from snowflake.core.schema import Schema
   from snowflake.core.stage import Stage
   from snowflake.core.table import Table, TableColumn, PrimaryKey
   from snowflake.core.task import StoredProcedureCall, Task
   from snowflake.core.task.dagv1 import DAGOperation, DAG, DAGTask
   from snowflake.core.warehouse import Warehouse
   

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   Note

   After running this cell, you might be prompted to set your Python kernel. If you activated a conda environment, select conda as the Python kernel (for example, something similar to: ~/miniconda3/envs/<your conda env>/bin/python).

   In this cell, you import Snowpark and the core APIs that manage Snowflake objects.

4. In the next cell, run the following code:

   connection_params = {
       "connection_name": "default"
   }
   

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   In this cell, you set the Snowflake connection parameters for your session by creating a connection_params dictionary specifying the connection definition named default that you previously configured.

5. To establish a connection to Snowflake, create a Snowpark session and pass connection_params as an argument:

   session = Session.builder.configs(connection_params).create()
   

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6. To create a Root object, pass your session object to the Root constructor:

   root = Root(session)
   

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And that’s it! By running the code in these four cells, you’re now ready to use the Snowflake Python APIs.