#358: Understanding Pandas visually with PandasTutor
Pandas is a great library that allows you to accomplish a ton of filtering and processing in condensed syntax. But how well do you understand what's happening? Sam Lau and Philip Guo built a great site to help use visually explore how Pandas is processing your dataset with your specific syntax. It's called PandasTutor, and Sam is here to tell us about it.
25 Maalis 202246min
#357: Python and the James Webb Space Telescope
Telescopes have been fundamental in our understanding of our place in the universe. And when you think about images that have shaped our modern view of space, you probably think about Hubble. But just this year, the JWST or James Web Space Telescope, was launch. JWST will go far beyond what Hubble has discovered. And did you know Python is used extensively in the whole data pipeline of JWST? We have two great guests here to tell us about it: Megan Sosey and Mike Swam.
21 Maalis 20221h 2min
#356: Tips for ML / AI startups
Have you been considering launching a product or even a business based on Python's AI / ML stack? We have a great guest on the episode this week, Dylan Fox, who is the cofounder of AssemblyAI and has been building his startup successfully over the past few years. He has interesting stories of 100s of GPUs in the cloud, evolving ML models, and much more that I know you'll enjoy hearing.
14 Maalis 20221h 6min
#355: EdgeDB - Building a database in Python
What database are you using in your apps these days? If you like most Python people, it's probably PostgreSQL. If you roll with NoSQL like me, you're probably using MongoDB. Maybe you're even using a graph database focused more on relationships. But there's a new Python database in town, and as you learn in during this episode, many critical Python libraries have come into existence because of it. This database is called EdgeDB. EdgeDB is built upon Postgres, implemented mostly in python, and is something of a marriage of a traditional relational database and an ORM.
6 Maalis 20221h 18min
#354: Sphinx, MyST, and Python Docs in 2022
When you think about the power of Python, the clean language or powerful standard library may come to mind. You might certainly point to the external packages too. But what about the relative ease of picking up new libraries or even parts of the standard library? Documentation plays an important role there. And the tools in the Python space for building solid documentation and even publishing articles and books involving live code are huge assets.
24 Helmi 20221h 11min
#353: SQLModel: The New ORM for FastAPI and Beyond
Two frameworks that have taken the Python world by storm are FastAPI and Pydantic. Once you already have your data exchange modeled in Pydantic, you might want to use that code for storing it in the database. And, if you have DB models you might want to somehow use them to power and document the APIs built with FastAPI. But the popular ORMs, such as SQLAlchemy and others, far predate Pydantic. But could they be put together?
18 Helmi 20221h 18min
#352: Running Python in Production
Do we talk about running Python in production enough? I can tell you that the Talk Python infrastructure (courses, podcasts, APIs, etc.) get a fair amount of traffic, but they look nothing like what Google, or Instagram, or insert [BIG TECH NAME] here's deployments do. Yet, mostly, we hear about interesting feats of engineering at massive scale that is impressive but often is also outside of the world most Python devs need for their companies and services.
8 Helmi 20221h
#351: Machine Learning Ethics and Laws Panel
The world of AI is changing fast. And the AI / ML space is a bit out of the ordinary for software developers. Typically in software, we can prove that given a certain situations, the code will always behave the same. We can point to where and why a decision is made. ML isn't like that. We set it up and then it takes on a life of its own.
3 Helmi 20221h 10min
