Asynchronous I/O, OAuth authentication, expanded SQL standards support, and new extension capabilities give developers faster performance, stronger security, and greater flexibility.
How-To Geek on MSN
How to Use pandas DataFrames in Python to Analyze and Manipulate Data
pandas is a Python module that's popular in data science and data analysis. It's offers a way to organize data into ...
The Register on MSN
Google fuses SQL, Python, and Spark in Colab Enterprise push
Move comes as Snowflake and Databricks chase the same all-in-one analytics dream Google is promising a single notebook environment for machine learning and data analytics, integrating SQL, Python, and ...
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