>> I'm not talking about in place installs, I'm talking about e.g. building a
>> wheel and then tweaking one file and rebuilding -- traditionally build
>> systems go to some effort to keep track of intermediate artifacts and reuse
>> them across builds when possible, but if you always copy the sour
I was more aiming to point out a situation where the NumPy's text file
reader was significantly better than the Pandas version, so we would want
to make sure that we properly benchmark any significant changes to NumPy's
text reading code. Who knows where else NumPy beats Pandas?
Indeed. For this ex
Correct, there were entries that would sometimes take up their entire
width. The delimited text readers could not read this particular dataset.
The dataset I am referring to is the processed ISD data:
https://www.ncdc.noaa.gov/isd
As for fromstring() not being able to help there, I didn't mean to
This came up in [a matplotlib
issue](https://github.com/matplotlib/matplotlib/issues/5221):
>>> np.histogram(np.arange(10), range=(0.0, np.inf))
(array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),
array([ nan, inf, inf, inf, inf, inf, inf, inf, inf, inf, inf]))
>>> np.histogram(np.arange(10), ran
