The Pandas method to_sql is used to send data from a DataFrame to a SQL database. Common use cases for this method are sending data back from batch machine learning predictions, tracking metrics, and storing prepared datasets.
Connecting to Snowflake in Python by using the Snowflake Connector allows you to write data to and from tables and execute SQL commands as if you were working within the UI.
Connecting to Snowflake from Python by using SQLAlchemy allows you to write data to and from tables and execute SQL commands as if you were working within the Snowflake UI.
Connecting to Snowflake from Python allows you to write data to and from tables and execute SQL commands as if you were working within the Snowflake UI.
One common task that developers have is to create a Snowflake table using a Pandas DataFrame that they’ve created in Python. There are several ways to achieve this, but in this post I will run through how it can be done by using only the Snowflake Python connector.
