Getting Started with Python Integration to SAS® Viya® - Index

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CAS Actions and Action Sets - a brief intro - A quick introduction about the distributed CAS server in SAS® Viya®.

Index of articles on Getting Started with Python Integration to SAS Viya.

  1. Making a Connection - An introduction to SAS Viya and the massively parallel processing CAS engine, and how to make a connection to CAS using the Python SWAT package.
  2. Working with CAS Actions and CASResults Objects - Learn about CAS actions and action sets, how to execute actions in the distributed CAS server, and how to work with the client-side results.
  3. Loading a CAS Action Set - Learn how to explore the available CAS action sets in your environment, and load an action set if it's not loaded.
  4. Exploring Caslibs - Get an overview of how data is organized in the distributed CAS server and how to use CAS actions to explore the environment.
  5. Loading Server-Side Files into Memory - Learn how to load data into memory in the CAS server to begin processing your data in a massively parallel processing environment.
  6. Descriptive Statistics - Learn how to use the SWAT describe method and the summary CAS action to obtain descriptive statistics of your CAS table.
  7. Filtering CAS Tables - Learn how to filter CAS tables using a variety of methods like the traditional Pandas bracket notation, the SWAT query and isin method, and the CAS table where parameter.
  8. Creating Calculated Columns - Learn how to create calculated columns in a CAS table using a variety of methods like the traditional Pandas bracket notation, the swat eval method, and the CAS table computedVarsProgram parameter.
  9. Summarize Columns - Learn how to perform simple summarizations on a distributed CAS table using a variety of Pandas API methods through SWAT package like sum, mean, max and nlargest.
  10. Group and Aggregate CAS Tables - Learn how to group and aggregate distributed CAS tables using the familiar Pandas groupby method in the SWAT package, and the CAS table groupby parameter.
  11. Rename Columns - Learn how to rename CAS table columns using familiar Python techniques and the alterTable CAS action.
  12. Count of Unique Values - Learn how to get the count of unique values in a CAS table column
  13. Loading a Client-Side File into Memory - Learn how to load a client-side csv file into the distributed CAS server.
  14. CAS Table to DataFrame - Learn how to transfer a CAS table from the CAS server to your Python client as a DataFrame.
  15. Count Missing Values in a CAS Table - Learn how to identify missing values in a CAS table.
  16. Execute SQL - Learn how to execute SQL in the distributed CAS server.
  17. Saving CAS tables - Learn how to save CAS tables to a caslib's data source as a file such as CSV, parquet and sashdat.
  18. Update rows in CAS tables - Learn how to update rows in a distributed CAS table in place.
  19. Read multiple CSV files into a CAS table - Learn how to load multiple CSV files into memory as a single CAS table using the loadTable action.
  20. Remove Duplicate Rows - Learn how to remove duplicate rows from a distributed CAS table using both the Pandas API in the SWAT package and the native CAS action.
  21. Impute Missing Values - Learn how to impute missing values in a distributed CAS table using the fillna method from the Pandas API in the SWAT package and the impute CAS action.
  22. Create User Defined Functions (UDFs) - Learn how to create user defined functions (UDFs) for the distributed CAS server using the SWAT package.
  23. Executing SQL on Snowflake - Learn how to effortlessly connect Snowflake to the massively parallel processing CAS server in SAS Viya with the Python SWAT package.

 

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About Author

Peter Styliadis

Technical Training Consultant

Peter Styliadis is a Technical Training Consultant at SAS on the Foundations team in the Education division. The team focuses on course development and customer training. Currently Peter spends his time working with SAS Programming, Structured Query Language (SQL), SAS Visual Analytics, Programming in Viya and Python. Peter is from the Rochester/Buffalo New York area and does not miss the cold weather up north. After work, Peter can be found spending time with his wife, child and three dogs, as well as hiking and spending time at home learning something new. Prior to joining SAS, Peter worked at his family's restaurant (think My Big fat Greek Wedding), worked in customer service, then became a High School Math and Business teacher. To connect with Peter, feel free to connect on LinkedIn.

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