Do you write data science code? Do you struggle loading large amounts of data or wonder what parts of your code use the maximum amount of memory?

Maybe you just want to require smaller compute resources, servers, RAM, and so on.

If so, this episode is for you.

We have Idemar Turner Trowing, reader of the Python Data Science Memory Profiler Phil, here to talk about memory usage and data science. This is Talk Python to Me,

episode 274 recorded July 8th, 2020. Welcome to Talk Python to Me, a weekly podcast on Python, the language, the libraries, the ecosystem, and the personalities.

This is your host, Michael Kennedy. Follow me on Twitter where I'm at M. Kennedy. Keep up with a show and listen to past episodes at TalkPython.fm.

And follow the show on Twitter via At TalkPython.

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Good tomorrow. Welcome to Talk Python to me. Hi. Great to be here. Yeah, it's great to have you here.

I'm excited to talk about Python and memory. Yeah, me too. Yeah, I think it's something that doesn't

really get as much coverage as I think it deserves in the Python space.

You know, if you're a Java developer or a dot net developer, people go on and on and on about optimizing the GC and tweaking this thing or that thing or your code or algorithms for memory management.

...