I found one operation leading to the speed decrement. When inputs are a list
of dictionaries, pandas will firstly convert them to a series of numpy.ndarray.
This conversion might be slow when using PyPy.
I made a small example inside the micro benchmark project like this:
====================
def bench_build_np():
with Timer('np.array'):
for i in xrange(N//10):
myarray = np.array([i])
====================
On PyPy, it took about 2.3s, and on CPython it took about 0.22s.
I think I should try to find out why PyPy is slow on such condition. But
though I have read the docs for several times, I am not familiar with the
details of PyPy project. Could you please give me some advice on why
dynamically creating Numpy arrays is slow, based on how PyPy works?
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