Snowpark Pandas API: Bridging the Gap Between Pandas and Snowflake

In the fast-paced world of data science, efficiency and integration are key. Snowflake, a leader in cloud data platforms, has recently taken a forward step with the introduction of the Snowpark Pandas API. This integration bridges the familiar and powerful capabilities of the Pandas library with the scalability and performance of Snowflake. Whether you’re a seasoned data scientist or a data engineer, the Snowpark Pandas API promises to revolutionize the way you handle data in the cloud. In this post, we’ll explore the features, benefits, and limitation.

Lets understand what is this snowpark Pandas API and how it differs from existing snowpark dataframe API.

The Snowpark pandas API lets you run your pandas code directly on your data in Snowflake. Just by changing the import statement and a few lines of code, you can get the same pandas-native experience you know and love with the scalability and security benefits of Snowflake.

With this API, you can handle much larger datasets, eliminating the need to port your Pandas pipelines to other big data frameworks or invest in larger, more expensive machines.

Benefits of Using snowpark Pandas API

1. Python Developer friendly -Providing a pandas-compatible layer
2. Scalable-This API solves the convenience of pandas with the scalability infrastructure
3. Security & Governance -This API runs within snowflake secure environment.
4. No additional Cost for infrastructure -Snowpark Pandas API leverages the snowflake compute engine. So no need to setup additional/different Infra.

But in snowflake we are having already snowpark Dataframe API!!. What is the difference and advantage over Pandas API!!.

Lets deep dive on to this….

Differences:

We can able to convert the snowpark Dataframe to pandas api data frame and vice versa with below commands — to_snowpark_pandas ,to_snowpark

How Snowpark pandas compares to Native pandas

Snowpark pandas and native pandas have similar DataFrame APIs with matching signatures and similar semantics. Snowpark pandas provides the same API signature as native pandas and provides scalable computation with Snowflake.

We can convert native pandas data frame to snowpark pandas dataframe and vice versa . to_pandas, pd.DataFrame(…)

Limitations

1. Snowpark pandas provides no guarantee of compatibility with OSS third-party libraries.Limited compartability with Numpy .Snowflake recommends to convert them to native pands dataframe before calling 3rd party library.
2. Snowpark pandas Dataframe not integrated with snowpark ML. In that sceario convert them into snowpark dataframe .
3. Snowpark pandas requires a specific pandas version. Snowpark pandas requires pandas 2.2.1, and only provides compatibility with pandas 2.2.1.
4. Snowpark pandas provides fast and zero copy clone capability while creating DataFrames from Snowflake tables. However, several table types do not support zero copy clone capability and will materialize the data, which might be slow for large tables. Some examples of such tables include Hybrid tables, Iceberg tables, External tables, and tables from shared databases.

Summary

The Snowpark Pandas API is a powerful integration that combines the flexibility and ease of use of the Pandas library with the scalability and performance of Snowflake’s cloud data platform. This API allows data scientists and engineers to handle much larger datasets without the need to port Pandas pipelines to other big data frameworks or invest in more expensive hardware. It facilitates seamless conversion between native Pandas DataFrames and Snowpark Pandas DataFrames, making it easier to leverage Snowflake’s robust data processing capabilities. In this post, we’ll explore how the Snowpark Pandas API can streamline your data workflows, enhance efficiency, and open up new possibilities for data analysis in the cloud.