I found out that it\'s impossible to install NumPy/SciPy via installers on Windows 64-bit, that\'s only possible on 32-bit. Because I need more memory than a 32-bit installa
Look into python wheels to solve your problem. The best part of python wheels is that they let you install C extensions with no compilers. I just installed numpy and scipy using pip in a clean python install and they both worked fine.
You can now pip install numpy on Windows!
"Note: this page has only historical relevance, you can now pip-install for windows" Source: https://github.com/numpy/numpy/wiki/Whats-with-Windows-builds
Follow these steps:
cd..
cd..
cd Program Files\Python38\Scripts
Python38\Scripts
folder.pip install packagename.whl
You can write your python version instead of "38"
Installing with pip
You can install the numpy and scipy wheels on Windows with pip
in one step if you use the appropriate link from Gohlke's Unofficial Windows Binaries (mentioned by sebix) and run the Windows command prompt as Administrator. For example, in Python 3.5, you would simply use something like this:
# numpy-1.9.3+mkl for Python 3.5 on Win AMD64
pip3.5 install http://www.lfd.uci.edu/~gohlke/pythonlibs/xmshzit7/numpy-1.9.3+mkl-cp35-none-win_amd64.whl
# scipy-0.16.1 for Python 3.5 on Win AMD64
pip3.5 install http://www.lfd.uci.edu/~gohlke/pythonlibs/xmshzit7/scipy-0.16.1-cp35-none-win_amd64.whl
Hey I had the same issue.
You can find all the packages in the link below:
http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-learn
And choose the package you need for your version of windows and python.
You have to download the file with whl
extension. After that, you will copy the file into your python directory then run the following command:
py -3.6 -m pip install matplotlib-2.1.0-cp36-cp36m-win_amd64.whl
Here is an example when I wanted to install matplolib
for my python 3.6 https://www.youtube.com/watch?v=MzV4N4XUvYc
and this is the video I followed.
Intel provides pre-compiled Python modules for free in their "Intel Distribution for Python". The modules are compiled against Intel's MKL (Math Kernel Library) and thus optimized for faster performance. The package includes NumPy, SciPy, scikit-learn, pandas, matplotlib, Numba, tbb, pyDAAL, Jupyter, and others. Find more information and the download link here