Intermediate Data Transformations in Snowpark for Python | Snowflake Tutorial

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Ready to learn more about data transformations in Snowpark for Python?

In this video, we'll explore how to perform various data transformations using Snowpark's Python API, including how to get to and from Pandas, User-Defined Functions (UDFs), semi-structured data in Snowpark for Python, and Window functions.

We'll start by discussing how to get to and from Pandas, one of the most popular data analysis libraries in Python. With Snowpark, you can easily convert data between Pandas DataFrames and Snowpark Tables, allowing you to take advantage of the powerful data manipulation capabilities of both libraries.

Next, we'll dive into User-Defined Functions (UDFs), which are custom functions that you can define and use in your Snowpark queries. 

 We'll also discuss semi-structured data in Snowpark for Python, including how to work with JSON data, and other semi-structured data formats. You'll learn how to work with Table Functions, by the FLATTEN() table function demo. 

Finally, we'll simplify Window functions in Snowpark, which are a powerful tool for performing complex calculations over sets of rows in your data. We'll demonstrate how to define the window, use the window object or function once found. 

Whether you're new to Snowpark or an experienced user looking to expand your data transformation capabilities, this video has something for you. 

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Download links:
1. https://drive.google.com/file/d/1Easg...

2. https://repo.anaconda.com/pkgs/snowfl...

3. https://docs.snowflake.com/en/sql-ref...

00:00 Introduction
00:23 Background
00:42 What will be covered
01:36 To and From PANDAS
07:01 User Defined Functions (UDFs)
08:24 How to use Temporary UDFs in our Transformations
12:48 Semi-Structure Data
13:57 Flatten() Demo
19:35 Window Functions
20:42 Window Functions Demo
24:28 Conclusion


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