static-frame VS vim-jumpsuite

This is inadequate, as it ignores the types contained within the container. A DataFrame might have string column labels and three columns of integer, string, and floating-point values; these characteristics define the type. A function argument with such type hints provides developers, static analyzers, and runtime checkers with all the information needed to understand the expectations of the interface. StaticFrame 2 now permits this:

StaticFrame is an alternative DataFrame library that offers efficient solutions to this problem, both for in-memory and disk-based memoization.

A NumPy array is a Python object that stores data in a contiguous C-array buffer. The excellent performance of these arrays comes not only from this compact representation, but also from the ability of arrays to share "views" of that buffer among many arrays. NumPy makes frequent use of "no-copy" array operations, producing derived arrays without copying underling data buffers. By taking full advantage of NumPy's efficiency, the StaticFrame DataFrame library offers orders-of-magnitude better performance than Pandas for many common operations.

static-frame. An immutable alternative to pandas.

StaticFrame is an immutable DataFrame library that offers solutions to such problems. In StaticFrame, alternative fill value representations can be used to preserve columnar types in reindexing, shifting, and many other operations that require fill_value arguments. For operations on heterogeneously typed columnar data, one fill value is simply not enough.

For me, one example of high cpu usage is when i visit links like this one (https://github.com/InvestmentSystems/static-frame/blob/master/static_frame/performance/core.py) on GitHub. Safari is extremely laggy when i do nothing more than just scrolling around. Do you have sth like this?

Oh, you're in luck because I've actually written this exact plugin, it's called vim-jumpsuite. It is personally one of my special weapons that's becoming completely indispensable for me.

vim-jumpsuite: parses python tracebacks and identifies the most "interesting" part of the stack to create a jump list; despite vim being in the name, the python part of the plug-in is usable with any editors that supports parsing grep/quickfix-style output

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vim-jumpsuite for creating a quickfix/loclist jumps out of unittest tracebacks

vim-test with lieryan/vim-jumpsuite

For more elaborate cases, I wrote a plugin that summarises python traceback into the quickfix list. vim-jumpsuite is designed to be used when writing unittest/pytest; for each failing test, it'll try to pick the three most important locations that you'll want to jump to. You can also configure certain files/functions to never be picked by vim-jumpsuite.

Plug: one of the most valuable plugin for me are the vim plugin that I wrote myself: lieryan/vim-jumpsuite. It's a plugin to quickly jump to "interesting" line of code from your test suite by converting unittest reports to a Quickfix jumplist. The plugin uses some customizable heuristics to find the lines from tracebacks that are most likely to be most useful to your code.