Running quicker for Numpy and Pandas( installed via conda) than via pip?











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I have two Python environments (3.6).



I installed packages in one environment via conda, and in another environment via pip.



Then I found that conda-provided numpy and pandas run quicker than pip version (for most of the scenario).



Just wonder why this happens. Is that because they use different configuration during compilation?






python pandas numpy anaconda







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    Most likely it is due to a different BLAS linked to numpy - e.g., see markus-beuckelmann.de/blog/boosting-numpy-blas.html
    – chrisb
    Nov 7 at 21:21















up vote
3
down vote

favorite
1












I have two Python environments (3.6).



I installed packages in one environment via conda, and in another environment via pip.



Then I found that conda-provided numpy and pandas run quicker than pip version (for most of the scenario).



Just wonder why this happens. Is that because they use different configuration during compilation?






python pandas numpy anaconda







share|improve this question


















  • 1




    Most likely it is due to a different BLAS linked to numpy - e.g., see markus-beuckelmann.de/blog/boosting-numpy-blas.html
    – chrisb
    Nov 7 at 21:21













up vote
3
down vote

favorite
1









up vote
3
down vote

favorite
1






1





I have two Python environments (3.6).



I installed packages in one environment via conda, and in another environment via pip.



Then I found that conda-provided numpy and pandas run quicker than pip version (for most of the scenario).



Just wonder why this happens. Is that because they use different configuration during compilation?






python pandas numpy anaconda







share|improve this question













I have two Python environments (3.6).



I installed packages in one environment via conda, and in another environment via pip.



Then I found that conda-provided numpy and pandas run quicker than pip version (for most of the scenario).



Just wonder why this happens. Is that because they use different configuration during compilation?






python pandas numpy anaconda




python pandas numpy anaconda






share|improve this question













share|improve this question











share|improve this question




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asked Nov 7 at 20:29









MTANG

1368




1368








  • 1




    Most likely it is due to a different BLAS linked to numpy - e.g., see markus-beuckelmann.de/blog/boosting-numpy-blas.html
    – chrisb
    Nov 7 at 21:21














  • 1




    Most likely it is due to a different BLAS linked to numpy - e.g., see markus-beuckelmann.de/blog/boosting-numpy-blas.html
    – chrisb
    Nov 7 at 21:21








1




1




Most likely it is due to a different BLAS linked to numpy - e.g., see markus-beuckelmann.de/blog/boosting-numpy-blas.html
– chrisb
Nov 7 at 21:21




Most likely it is due to a different BLAS linked to numpy - e.g., see markus-beuckelmann.de/blog/boosting-numpy-blas.html
– chrisb
Nov 7 at 21:21












1 Answer
1






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2
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Yes, most likely this difference is due to different compilation configurations. Numpy makes use of the low-level fortran libraries BLAS and LAPACK. The numpy that you get from pip is compiled with different implementations of BLAS and LAPACK than the numpy that you get from conda. The latter uses the MKL implementation which is optimized specifically for Intel processors.



You can check the implementation that numpy is using in a particular environment with:



import numpy as np

np.show_config()


Some comparison benchmarks can be found here (as already noted by crisb).






share|improve this answer























  • Thanks. I think it is exactly the case. I'm doing some heavy linear algebra calculation via numpy.
    – MTANG
    Nov 7 at 22:35











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up vote
2
down vote













Yes, most likely this difference is due to different compilation configurations. Numpy makes use of the low-level fortran libraries BLAS and LAPACK. The numpy that you get from pip is compiled with different implementations of BLAS and LAPACK than the numpy that you get from conda. The latter uses the MKL implementation which is optimized specifically for Intel processors.



You can check the implementation that numpy is using in a particular environment with:



import numpy as np

np.show_config()


Some comparison benchmarks can be found here (as already noted by crisb).






share|improve this answer























  • Thanks. I think it is exactly the case. I'm doing some heavy linear algebra calculation via numpy.
    – MTANG
    Nov 7 at 22:35















up vote
2
down vote













Yes, most likely this difference is due to different compilation configurations. Numpy makes use of the low-level fortran libraries BLAS and LAPACK. The numpy that you get from pip is compiled with different implementations of BLAS and LAPACK than the numpy that you get from conda. The latter uses the MKL implementation which is optimized specifically for Intel processors.



You can check the implementation that numpy is using in a particular environment with:



import numpy as np

np.show_config()


Some comparison benchmarks can be found here (as already noted by crisb).






share|improve this answer























  • Thanks. I think it is exactly the case. I'm doing some heavy linear algebra calculation via numpy.
    – MTANG
    Nov 7 at 22:35













up vote
2
down vote










up vote
2
down vote









Yes, most likely this difference is due to different compilation configurations. Numpy makes use of the low-level fortran libraries BLAS and LAPACK. The numpy that you get from pip is compiled with different implementations of BLAS and LAPACK than the numpy that you get from conda. The latter uses the MKL implementation which is optimized specifically for Intel processors.



You can check the implementation that numpy is using in a particular environment with:



import numpy as np

np.show_config()


Some comparison benchmarks can be found here (as already noted by crisb).






share|improve this answer














Yes, most likely this difference is due to different compilation configurations. Numpy makes use of the low-level fortran libraries BLAS and LAPACK. The numpy that you get from pip is compiled with different implementations of BLAS and LAPACK than the numpy that you get from conda. The latter uses the MKL implementation which is optimized specifically for Intel processors.



You can check the implementation that numpy is using in a particular environment with:



import numpy as np

np.show_config()


Some comparison benchmarks can be found here (as already noted by crisb).







share|improve this answer














share|improve this answer



share|improve this answer








edited Nov 7 at 21:47

























answered Nov 7 at 21:39









Xukrao

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1,6181622












  • Thanks. I think it is exactly the case. I'm doing some heavy linear algebra calculation via numpy.
    – MTANG
    Nov 7 at 22:35


















  • Thanks. I think it is exactly the case. I'm doing some heavy linear algebra calculation via numpy.
    – MTANG
    Nov 7 at 22:35
















Thanks. I think it is exactly the case. I'm doing some heavy linear algebra calculation via numpy.
– MTANG
Nov 7 at 22:35




Thanks. I think it is exactly the case. I'm doing some heavy linear algebra calculation via numpy.
– MTANG
Nov 7 at 22:35


















 

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