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A tartalmat a Real Python biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Real Python vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.
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Managing Feature Flags & Comparing Python Visualization Libraries

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Manage episode 508558573 series 2637014
A tartalmat a Real Python biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Real Python vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

What’s a good way to enable or disable code paths without redeploying the software? How can you use feature flags to toggle functionality for specific users of your application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article exploring the use of feature flags. The piece advises targeting specific users, storing schemas, and relying on well-worn code paths. He also discusses the danger of the exponential growth of functionality tests as feature flags are added.

We dig into a comparison of six popular Python visualization libraries. The article provides code examples along with their respective visual output to highlight features and make selecting the optimal library for your work easier.

We also share several other articles and projects from the Python community, including a news roundup, benchmarking MicroPython, simplifying IPs and networks in Python, a “scream” cipher, and a browser-based graphical viewer for the output of Python’s cProfile module.

This episode is sponsored by InfluxData.

Course Spotlight: Exploring Astrophysics in Python With pandas and Matplotlib

This course uses three problems often covered in introductory astrophysics courses to explore in Python. Along the way, you’ll learn some astronomy and how to use a variety of data science libraries like NumPy, Matplotlib, pandas, and pint.

Topics:

  • 00:00:00 – Introduction
  • 00:03:04 – Python 3.14.0rc3 is go!
  • 00:03:14 – Django 6.0 alpha 1 released
  • 00:04:27 – PEP 782: Add PyBytesWriter C API
  • 00:05:06 – PEP 794: Import Name Metadata
  • 00:05:30 – PEP 803: Stable ABI for Free-Threaded Builds
  • 00:05:55 – Announcing the 2025 PSF Board Election Results!
  • 00:06:18 – Top 6 Python Libraries for Visualization: Which One to Use?
  • 00:18:34 – Sponsor: InfluxData
  • 00:19:24 – Feature Flags in Depth
  • 00:22:55 – Benchmarking MicroPython
  • 00:30:02 – Video Course Spotlight
  • 00:31:16 – Simplify IPs, Networks, and Subnets With the ipaddress
  • 00:34:52 – SCREAM CIPHER (“ǠĂȦẶAẦ ĂǍÄẴẶȦ”
  • 00:36:20 – SnakeViz: browser based graphical viewer for the output of Python’s cProfile module
  • 00:40:58 – Thanks and goodbye

News:

Show Links:

  • Top 6 Python Libraries for Visualization: Which One to Use? – The vast number of Python visualization libraries can be overwhelming. This article shows you the pros and cons of some popular libraries, including Matplotlib, seaborn, Plotly, Bokeh, Altair, and Pygal.
  • Feature Flags in Depth – Feature flags are a way to enable or disable blocks of code without needing to redeploy your software. This post shows you several different approaches to feature flags.
  • Benchmarking MicroPython – This post compares the performance of running Python on several microcontroller boards.
  • Simplify IPs, Networks, and Subnets With the ipaddress – Python’s built-in ipaddress module makes handling IP addresses and networks clean and reliable. This article shows how to validate, iterate, and manage addresses and subnets while avoiding common pitfalls of string-based handling.

Projects:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

  continue reading

268 epizódok

Artwork
iconMegosztás
 
Manage episode 508558573 series 2637014
A tartalmat a Real Python biztosítja. Az összes podcast-tartalmat, beleértve az epizódokat, grafikákat és podcast-leírásokat, közvetlenül a Real Python vagy a podcast platform partnere tölti fel és biztosítja. Ha úgy gondolja, hogy valaki az Ön engedélye nélkül használja fel a szerzői joggal védett művét, kövesse az itt leírt folyamatot https://hu.player.fm/legal.

What’s a good way to enable or disable code paths without redeploying the software? How can you use feature flags to toggle functionality for specific users of your application? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.

Christopher shares an article exploring the use of feature flags. The piece advises targeting specific users, storing schemas, and relying on well-worn code paths. He also discusses the danger of the exponential growth of functionality tests as feature flags are added.

We dig into a comparison of six popular Python visualization libraries. The article provides code examples along with their respective visual output to highlight features and make selecting the optimal library for your work easier.

We also share several other articles and projects from the Python community, including a news roundup, benchmarking MicroPython, simplifying IPs and networks in Python, a “scream” cipher, and a browser-based graphical viewer for the output of Python’s cProfile module.

This episode is sponsored by InfluxData.

Course Spotlight: Exploring Astrophysics in Python With pandas and Matplotlib

This course uses three problems often covered in introductory astrophysics courses to explore in Python. Along the way, you’ll learn some astronomy and how to use a variety of data science libraries like NumPy, Matplotlib, pandas, and pint.

Topics:

  • 00:00:00 – Introduction
  • 00:03:04 – Python 3.14.0rc3 is go!
  • 00:03:14 – Django 6.0 alpha 1 released
  • 00:04:27 – PEP 782: Add PyBytesWriter C API
  • 00:05:06 – PEP 794: Import Name Metadata
  • 00:05:30 – PEP 803: Stable ABI for Free-Threaded Builds
  • 00:05:55 – Announcing the 2025 PSF Board Election Results!
  • 00:06:18 – Top 6 Python Libraries for Visualization: Which One to Use?
  • 00:18:34 – Sponsor: InfluxData
  • 00:19:24 – Feature Flags in Depth
  • 00:22:55 – Benchmarking MicroPython
  • 00:30:02 – Video Course Spotlight
  • 00:31:16 – Simplify IPs, Networks, and Subnets With the ipaddress
  • 00:34:52 – SCREAM CIPHER (“ǠĂȦẶAẦ ĂǍÄẴẶȦ”
  • 00:36:20 – SnakeViz: browser based graphical viewer for the output of Python’s cProfile module
  • 00:40:58 – Thanks and goodbye

News:

Show Links:

  • Top 6 Python Libraries for Visualization: Which One to Use? – The vast number of Python visualization libraries can be overwhelming. This article shows you the pros and cons of some popular libraries, including Matplotlib, seaborn, Plotly, Bokeh, Altair, and Pygal.
  • Feature Flags in Depth – Feature flags are a way to enable or disable blocks of code without needing to redeploy your software. This post shows you several different approaches to feature flags.
  • Benchmarking MicroPython – This post compares the performance of running Python on several microcontroller boards.
  • Simplify IPs, Networks, and Subnets With the ipaddress – Python’s built-in ipaddress module makes handling IP addresses and networks clean and reliable. This article shows how to validate, iterate, and manage addresses and subnets while avoiding common pitfalls of string-based handling.

Projects:

Level up your Python skills with our expert-led courses:

Support the podcast & join our community of Pythonistas

  continue reading

268 epizódok

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