Packages the distutils stdlib module as a standalone package,
```
377K distutils.data
221K distutils.data.br
19K distutils.js
```
to reduce the minimal pyodide size.

Part of #1542 

This also,
 - adds documentation on what's supported or not in the stdlib
 - makes a few changes to `pyodide_build/buildall.py` to be able to add such stdlib packages as a dependency in other packages (which didn't work before). For instance, until there is a release with https://github.com/pypa/packaging/pull/396 micropip has a dependency on distutils.
Unvendor distutils. Packages the distutils stdlib module as a standalone package,
```
377K distutils.data
221K distutils.data.br
19K distutils.js
```
to reduce the minimal pyodide size.

Part of #1542 

This also,
 - adds documentation on what's supported or not in the stdlib
 - makes a few changes to `pyodide_build/buildall.py` to be able to add such stdlib packages as a dependency in other packages (which didn't work before). For instance, until there is a release with https://github.com/pypa/packaging/pull/396 micropip has a dependency on distutils.
# setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1  # noqa
# run: log_likelihood(a, b, c)
# from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
import numpy

# pythran export log_likelihood(float64[], float64, float64)


def log_likelihood(data, mean, sigma):
    s = (data - mean) ** 2 / (2 * (sigma ** 2))
    pdfs = numpy.exp(-s)
    pdfs /= numpy.sqrt(2 * numpy.pi) * sigma
    return numpy.log(pdfs).sum()

# setup: import numpy as np ; N = 100000 ; a = np.random.random(N); b = 0.1; c =1.1  # noqa
# run: log_likelihood(a, b, c)
# from: http://arogozhnikov.github.io/2015/09/08/SpeedBenchmarks.html
import numpy

# pythran export log_likelihood(float64[], float64, float64)


def log_likelihood(data, mean, sigma):
    s = (data - mean) ** 2 / (2 * (sigma ** 2))
    pdfs = numpy.exp(-s)
    pdfs /= numpy.sqrt(2 * numpy.pi) * sigma
    return numpy.log(pdfs).sum()
