On the float_precision argument to pandas.read_csv

Question

The documentation for the argument in this post's title says:

float_precision : string, default None

Specifies which converter the C engine should use for floating-point values. The options are None for the ordinary converter, high for the high-precision converter, and round_trip for the round-trip converter.

I'd like to learn more about the three algorithms mentioned, preferably without having to dig into the source code¹.

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Q: Do these algorithms have names I can Google for to learn exactly what they do and how they differ?

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(Also, one side question: what exactly is "the C engine" in this context? Is that a Pandas-specific thing, or a Python-wide thing? None of the above?)

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^{1 Not being familiar with the code base in question, I expect it would take me a long time just to locate the relevant source code. But even assuming I manage to find it, my experience with this sort of algorithm is that their implementations are so highly optimized, and at such a low level, that without some high-level description it is really difficult, at least for me, to follow what's going on.}

Answer 1

You asked about the actual algorithms - the closest I can find is: https://github.com/pandas-dev/pandas/blob/master/pandas/_libs/parsers.pyx#L492

This is taken from a related answer, kudos to MaxU (Understanding pandas.read_csv() float parsing)

Ordinary: double_converter_nogil = xstrtod
High: double_converter_nogil = precise_xstrtod
Round-Trip: double_converter_withgil = round_trip

From here, you're in C-land. You also asked why pandas uses C - critical code paths are written in Cython or C.