Latest episodes
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#426: What's New in PyScript [August 2023]
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#425: Memray: The endgame Python memory profiler
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#424: Shiny for Python
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#423: Solving 10 different simulation problems with Python
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#427: 10 Tips and Ideas for the Beginner to Expert Python Journey
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#422: How data scientists use Python
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#421: Python at Netflix
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#420: Database Consistency & Isolation for Python Devs
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#419: Debugging Python in Production with PyStack
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#418: How To Keep A Secret in Python Apps
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#417: Test-Driven Prompt Engineering for LLMs with Promptimize
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#416: Open Source Sports Analytics with PySport
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#415: Future of Pydantic and FastAPI
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#414: A Stroll Down Startup Lane
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#413: Live from PyCon 2023
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#412: PEP 711 - Distributing Python Binaries
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#411: Things I Wish Someone Had Explained To Me Sooner About Python
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#410: The Intersection of Tabular Data and Generative AI
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#409: Privacy as Code with Fides
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#408: Hatch: A Modern Python Workflow
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#407: pytest tips and tricks for better testing
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#406: Reimagining Python's Packaging Workflows
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#405: Testing in Radio Astronomy with Python and pytest
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#404: Clean Code in Python
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#403: Fusion Ignition Breakthrough and Python
Talk Python,![#402: Polars: A Lightning-fast DataFrame for Python [updated audio]](https://res.cloudinary.com/practicaldev/image/fetch/s--HMYtC324--/c_imagga_scale,f_auto,fl_progressive,h_240,q_auto,w_240/https://dev-to-uploads.s3.amazonaws.com/uploads/podcast/image/21/art.png)
#402: Polars: A Lightning-fast DataFrame for Python [updated audio]
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#402: Polars: A Lightning-fast DataFrame for Python
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#401: Migrating 3.8 Million Lines of Python
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#400: Ruff - The Fast, Rust-based Python Linter
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