Comments (4)
In order to help, you need to give a code example what you do and where it takes to much time.
from distfit.
I rechecked if that was a problem with my machine, but it doesn't look so.
import numpy
from distfit import distfit
from datetime import datetime
dist = distfit(todf=True, method='discrete')
dist.fit_transform(np.random.binomial(1, 0.48, 250))
start_time = datetime.now()
dist.plot()
# Next jupyter cell
print('Time elapsed (hh:mm:ss.ms) {}'.format(datetime.now() - start_time))
Out: Time elapsed (hh:mm:ss.ms) 0:02:50.604459
Python implementation: CPython
Python version : 3.9.2
IPython version : 7.22.0
numpy : 1.19.2
Compiler : MSC v.1916 64 bit (AMD64)
OS : Windows
Release : 10
Machine : AMD64
Processor : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
CPU cores : 8
Architecture: 64bit
from distfit.
Okay, I guess it was problem with my machine after all, restarting kernel reduced these times. Sorry.
Great library!
from distfit.
Great! Well you can speedup the proces by setting the parameter todf=False. Pandas is really a bottleneck. The results are also stored in a dict for further usage!
from distfit.
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from distfit.