There are regressions in import time in 3.5 and 3.6.

$ for i in `seq 5`; do ./python -I -m timeit -n1 -r1 "import enum"; done

Python 3.4:
1 loops, best of 1: 3.45 msec per loop
1 loops, best of 1: 3.43 msec per loop
1 loops, best of 1: 3.55 msec per loop
1 loops, best of 1: 3.54 msec per loop
1 loops, best of 1: 3.42 msec per loop

Python 3.5:
1 loops, best of 1: 4.38 msec per loop
1 loops, best of 1: 4.31 msec per loop
1 loops, best of 1: 4.32 msec per loop
1 loops, best of 1: 4.32 msec per loop
1 loops, best of 1: 4.4 msec per loop

Python 3.6:
1 loops, best of 1: 20.2 msec per loop
1 loops, best of 1: 20.2 msec per loop
1 loops, best of 1: 22 msec per loop
1 loops, best of 1: 20.3 msec per loop
1 loops, best of 1: 20.4 msec per loop


$ for i in `seq 5`; do ./python -I -m timeit -n1 -r1 -s "import sys; sys.modules.clear()" -- "import enum"; done

Python 3.4:
1 loops, best of 1: 29.5 msec per loop
1 loops, best of 1: 29.3 msec per loop
1 loops, best of 1: 30 msec per loop
1 loops, best of 1: 28.9 msec per loop
1 loops, best of 1: 29.2 msec per loop

Python 3.5:
1 loops, best of 1: 43.8 msec per loop
1 loops, best of 1: 44 msec per loop
1 loops, best of 1: 43.5 msec per loop
1 loops, best of 1: 43.1 msec per loop
1 loops, best of 1: 43.8 msec per loop

Python 3.6:
1 loops, best of 1: 59.8 msec per loop
1 loops, best of 1: 59.1 msec per loop
1 loops, best of 1: 58.8 msec per loop
1 loops, best of 1: 58.6 msec per loop
1 loops, best of 1: 61.9 msec per loop


And even in importing already imported and cached module there is small regression.
$ for i in `seq 5`; do ./python -I -m timeit "import enum"; done

Python 3.4:
100000 loops, best of 3: 3.04 usec per loop
100000 loops, best of 3: 3.07 usec per loop
100000 loops, best of 3: 3.08 usec per loop
100000 loops, best of 3: 3.11 usec per loop
100000 loops, best of 3: 3.04 usec per loop

Python 3.5:
100000 loops, best of 3: 3.27 usec per loop
100000 loops, best of 3: 3.22 usec per loop
100000 loops, best of 3: 3.18 usec per loop
100000 loops, best of 3: 3.28 usec per loop
100000 loops, best of 3: 3.17 usec per loop

Python 3.6:
100000 loops, best of 3: 3.29 usec per loop
100000 loops, best of 3: 3.26 usec per loop
100000 loops, best of 3: 3.34 usec per loop
100000 loops, best of 3: 3.35 usec per loop
100000 loops, best of 3: 3.35 usec per loop

I don't know how much of this is directly importlib's fault, at least in the 3.5 -> 3.6 case, as very little has changed in the bootstrap code since 3.6 started: https://hg.python.org/cpython/log/default/Lib/importlib/_bootstrap.py and https://hg.python.org/cpython/log/default/Lib/importlib/_bootstrap_external.py and https://hg.python.org/cpython/log/default/Python/import.c .

Even if Python 3.5 startup is slower than Python 3.4 startup, I'm more concerned by Python 3.6! What happens in Python 3.6? Are you sure that all binaries were compiled in release mode?

Why do you import enum? The bare minimum is "pass" :-)

This is not startup time, this is import time. All binaries were compiled in release mode. The regression is reproducible with other modules.

$ for i in `seq 5`; do ./python -I -m timeit -n1 -r1 "import zipfile"; done

Python 3.4:
1 loops, best of 1: 46 msec per loop
1 loops, best of 1: 45.3 msec per loop
1 loops, best of 1: 45.9 msec per loop
1 loops, best of 1: 45.1 msec per loop
1 loops, best of 1: 45.3 msec per loop

Python 3.5:
1 loops, best of 1: 49.1 msec per loop
1 loops, best of 1: 49.9 msec per loop
1 loops, best of 1: 50 msec per loop
1 loops, best of 1: 49.9 msec per loop
1 loops, best of 1: 53.7 msec per loop

Python 3.6:
1 loops, best of 1: 66.3 msec per loop
1 loops, best of 1: 64.2 msec per loop
1 loops, best of 1: 64.6 msec per loop
1 loops, best of 1: 65.1 msec per loop
1 loops, best of 1: 64.2 msec per loop

It should also be mentioned that startup time from 3.4 to default has not been affected according to Intel's daily benchmark run which is usually the most obvious place you see import performance issues:

normal_startup    0.63661%          0.39540%          0.45931%          5.22018%

Clarify title; regression is not in 'import time'. I am working on timing the import of Lib/*.py on windows.  I already discovered that unix-only crypt is present on windows (but should not be).

I did not find any significant different between 3.4 and 3.5 on Windows running the following:

import os, time
from importlib import import_module

files = os.listdir("C:/Programs/Python34/Lib")
excludes = {'antigravity.py', 'compileall.py', 'crypt.py', 'pty.py',
            'this.py', 'tty.py', '__phello__.foo.py'}
start = time.monotonic()
for name in files:
    if name.endswith('.py') and name not in excludes:
        try:
            import_module(name[:-3])
        except:
            print(name)
print(time.monotonic()-start)

Other than a couple of outliers, the time was consintly about .29 seconds with both 3.4 and 3.5:
  F:\Python\dev>py -3.x  c:/programs/python34/tem.py
The 3.5 run is a bit odd since it runs both 3.4 code and 3.5 code, but there were no extra exceptions once I added the needed exclusions (pyt and tty are also unix only) for 3.4.  The same code runs equally faster with Idle (.17 seconds), but that must be because Idle has already imported so many more /Lib modules (not including idlelib/*) than python itself.

I tested on Linux.

(1) for i in `seq 5`; do ./python -I -m timeit -n1 -r1 -s "import sys; sys.modules.clear()" -- "import enum"; done

(shortest timing)

Python 3.4: 6.93 msec
Python 3.6: 7.05 msec (+2%)


(2) for i in `seq 5`; do ./python -I -m timeit "import enum"; done

Python 3.4: 0.331 usec
Python 3.6: 0.341 usec (+%3)

These numbers are nanoseconds, it's too short to run a real benchmark.


(3) for i in `seq 5`; do ./python -I -m timeit -n1 -r1 "import enum"; done

Python 3.4: 801 usec
Python 3.6: 774 usec (-3%)


Sorry, I don't see major differences like you showed. Can you explain exactly how to reproduce them? Exact Python version? OS?

I used the development branches (branch 3.4 and branch default).

I just have rebuilt my Python's in development brances after made distclean to avoid possible effects of incremental building, and got the same results.

(1) for i in `seq 5`; do ./python -I -m timeit -n1 -r1 -s "import sys; sys.modules.clear()" -- "import enum"; done

Python 3.4: 29 msec
Python 3.5: 43.4 msec (+50%)
Python 3.6: 44.4 msec (+2%, +53%)


(2) for i in `seq 5`; do ./python -I -m timeit "import enum"; done

Python 3.4: 3.02 usec
Python 3.5: 3.14 usec (+4%)
Python 3.6: 3.34 usec (+6%, +10%)


(3) for i in `seq 5`; do ./python -I -m timeit -n1 -r1 "import enum"; done

Python 3.4: 3.49 msec
Python 3.5: 4.19 msec (+20%)
Python 3.6: 4.45 msec (+6%, +28%)


32-bit Ubuntu 14.04, Linux 3.16, gcc 4.8.4, Intel(R) Atom(TM) CPU N570   @ 1.66GH.

Importing a module looks slow to me. Importing a module which is a an *empty* .py file takes ~160 us.

$ python3 -I -m timeit -s "open('slow.py', 'wb').close(); import sys; import slow" "del sys.modules['slow']; import slow"
10000 loops, best of 3: 156 usec per loop

The difference betwen Python 3.4 and 3.5 is that enum.py imports indirectly more modules. The following two imports were added to types.py in Python 3.5:

import functools as _functools         
import collections.abc as _collections_abc

$ 3.4/python -c "import sys; sys.modules.clear(); import enum; print(len(sys.modules))"
18

$ 3.5/python -c "import sys; sys.modules.clear(); import enum; print(len(sys.modules))"
22

Yes, importing 4 additional modules can be a cause of the slowdown. Thanks Victor.