/explore
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pythonspeed.com | ||
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vickiboykis.com
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| | | | | Working with medium-ish data in Pandas | |
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digitalflapjack.com
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| | | | | Why pandas and python make me frustrated: looking at how the promise of simpilicity comes with hidden costs that are expensive to deal with, and what we might do about that. | |
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tomaugspurger.net
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| | | | | This is a status update on some enhancements for pandas. The goal of the work is to store things that are sufficiently array-like in a pandas DataFrame, even if they aren't a regular NumPy array. Pandas already does this in a few places for some blessed types (like Categorical); we'd like to open that up to anybody. A couple months ago, a client came to Anaconda with a problem: they have a bunch of IP Address data that they'd like to work with in pandas. They didn't just want to make a NumPy array of IP addresses for a few reasons: | |
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idiallo.com
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| | | There are many ways to detect if an object is an array. I found a single line of code to rule them all. | ||
