Visual Studio Code along with GitHub, Codespaces, and Azure Machine Learning have been investing substantially into tools and platforms to make the lives of Python data scientists easier, and we want to share why VS Code is now the #1 tool for Python Data Scientists according to the 2021 Python Software Foundation Developer Survey, and how you can leverage VS Code to take your data science productivity to the next level. This talk will walk through several common Python data science scenarios, showcasing all the productive tooling VS Code has to offer along the way. As a sneak peek, we will be demoing a best in class Jupyter Notebooks experience with VS Code Notebooks, a revolutionary new data cleaning / preparation experience with Data Wrangler in VS Code, collaboration features with GitHub and Codespaces, Azure Machine Learning for deployment, and more! PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=uPRQXDjQhMs https://www.youtube.com/watch?v=eneTYlnYt9A

In this video, I take you through the journey of making a hash table 10 times faster and share performance tips along the way. 00:00 why are hash tables important?
00:31 how hash tables work
02:40 a naïve hash table
04:35 custom hash function
08:52 perfect hash tables
12:03 my perfect hash table
14:20 beating gperf
17:24 beating memcmp
21:46 beating SIMD
26:01 even faster?
30:06 pop quiz answers
31:45 beating cmov
33:09 closing thoughts PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=DMQ_HcNSOAI https://www.youtube.com/watch?v=kuxBOGB_FlM

Key insights 🧠 Developing an AI-based business data analyst using open AI function calling can potentially challenge the role of traditional data analysts. 🧩 The AI-based business data analyst was able to load and inspect the CSV data, generate Python code, and create a chart, showcasing its potential in automating data analysis tasks. 🤷‍ "Overall, I'm not very concerned" – Despite the overestimation, the speaker doesn't seem too worried, indicating a potential disagreement or controversy regarding the significance of the AI's accuracy. 🔄 The use of AI-based tools like the code interpreter raises controversy around the potential replacement of human data analysts. 💡 The potential functions of the chatbot can range from filtering and aggregating data to generating visualizations and even running SQL queries, making it a versatile tool for data analysis. 💡 The AI model can recommend specific functions, such as the plot time series function with the metric sales, to visualize data and provide valuable insights to the user. 💡 Using function calling to develop custom agents that can act as data analysts is a promising approach with several advantages, including full control over generated plots. 🚧 Implementing guardrails and integration tests can help ensure the reliability and accuracy of AI-based business data analysts. PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/@PyDataTV https://www.youtube.com/watch?v=8NfvLDhINdI