I'm currently having trouble installing scipy via PyCharm's package manager. I have installed numpy successfully and do have the Microsoft Visual Studio C/C++ compiler in the System Variables.
However, when it's time to install scipy in PyCharm, the following error occurs:
Executed Command: pip install scipy
Error occured: numpy.distutils.system_info.NotFoundError: no lapack/blas resources found
I have seen other resources on installing blas / lapack on windows, but I'm unsure if it will work with PyCharm's installations.
If anybody has the solution / resources to redirect me to, please let me know.
As long as you're using the python.org version(s) of Python, the easiest way to install packages is to first check if they are in Christoph Gohlke's Python Extension Packages for Windows repository. There are pre-compiled packages for both numpy and scipy, along with many many others. You'll need to install numpy from there, as it is statically-linked to Intel's MKL, and is a necessary dependency for many of the other packages there, including scipy.
PyCharm uses pip utility so if any error occurs during package installation it means that if you try pip install < package > in the command line you will get the same error.
So in your case the best way is to install pre-compiled package from http://www.lfd.uci.edu/~gohlke/pythonlibs/ for your interpreter in the command line and after that restart PyCharm. Also you can check that now the package is in a package list for your interpreter: Settings| Project| Project interpreter.
The best way to install Python packages for science, math, engineering, data analysis - is using Anaconda.
It's a Python distribution, that comes with the most popular packages (see the list of packages here).
I had the same issue, and downloading Anaconda, and switching the project interpreter in PyCharm to \Anaconda3\python.exe helped solve this.
Good luck!
Install python packages using Anaconda and use interpreter as anaconda/python.exe
when creating new python projects.
It worked well for me without giving above errors.
Refer this:create a project using PyCharm
I am trying to install python and a series of packages onto a 64bit windows 7 desktop. I have installed Python 3.4, have Microsoft Visual Studio C++ installed, and have successfully installed numpy, pandas and a few others. I am getting the following error when trying to install scipy;
numpy.distutils.system_info.NotFoundError: no lapack/blas resources found
I am using pip install offline, the install command I am using is;
pip install --no-index --find-links="S:\python\scipy 0.15.0" scipy
I have read the posts on here about requiring a compiler which if I understand correctly is the VS C++ compiler. I am using the 2010 version as I am using Python 3.4. This has worked for other packages.
Do I have to use the window binary or is there a way I can get pip install to work?
Many thanks for the help
The following link should solve all problems with Windows and SciPy; just choose the appropriate download. I was able to pip install the package with no problems. Every other solution I have tried gave me big headaches.
Source: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
Command:
pip install [Local File Location]\[Your specific file such as scipy-0.16.0-cp27-none-win_amd64.whl]
This assumes you have installed the following already:
Install Visual Studio 2015/2013 with Python Tools
(Is integrated into the setup options on install of 2015)
Install Visual Studio C++ Compiler for Python
Source: http://www.microsoft.com/en-us/download/details.aspx?id=44266
File Name: VCForPython27.msi
Install Python Version of choice
Source: python.org
File Name (e.g.): python-2.7.10.amd64.msi
My python's version is 2.7.10, 64-bits Windows 7.
Download scipy-0.18.0-cp27-cp27m-win_amd64.whl from http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
Open cmd
Make sure scipy-0.18.0-cp27-cp27m-win_amd64.whl is in cmd's current directory, then type pip install scipy-0.18.0-cp27-cp27m-win_amd64.whl.
It will be successful installed.
The solution to the absence of BLAS/LAPACK libraries for SciPy installations on Windows 7 64-bit is described here:
http://www.scipy.org/scipylib/building/windows.html
Installing Anaconda is much easier, but you still don't get Intel MKL or GPU support without paying for it (they are in the MKL Optimizations and Accelerate add-ons for Anaconda - I'm not sure if they use PLASMA and MAGMA either). With MKL optimization, numpy has outperformed IDL on large matrix computations by 10-fold. MATLAB uses the Intel MKL library internally and supports GPU computing, so one might as well use that for the price if they're a student ($50 for MATLAB + $10 for the Parallel Computing Toolbox). If you get the free trial of Intel Parallel Studio, it comes with the MKL library, as well as C++ and FORTRAN compilers that will come in handy if you want to install BLAS and LAPACK from MKL or ATLAS on Windows:
http://icl.cs.utk.edu/lapack-for-windows/lapack/
Parallel Studio also comes with the Intel MPI library, useful for cluster computing applications and their latest Xeon processsors. While the process of building BLAS and LAPACK with MKL optimization is not trivial, the benefits of doing so for Python and R are quite large, as described in this Intel webinar:
https://software.intel.com/en-us/articles/powered-by-mkl-accelerating-numpy-and-scipy-performance-with-intel-mkl-python
Anaconda and Enthought have built businesses out of making this functionality and a few other things easier to deploy. However, it is freely available to those willing to do a little work (and a little learning).
For those who use R, you can now get MKL optimized BLAS and LAPACK for free with R Open from Revolution Analytics.
EDIT: Anaconda Python now ships with MKL optimization, as well as support for a number of other Intel library optimizations through the Intel Python distribution. However, GPU support for Anaconda in the Accelerate library (formerly known as NumbaPro) is still over $10k USD! The best alternatives for that are probably PyCUDA and scikit-cuda, as copperhead (essentially a free version of Anaconda Accelerate) unfortunately ceased development five years ago. It can be found here if anybody wants to pick up where they left off.
Sorry to necro, but this is the first google search result. This is the solution that worked for me:
Download numpy+mkl wheel from
http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy.
Use the version that is the same as your python version (check using python -V). Eg. if your python is 3.5.2, download the wheel which shows cp35
Open command prompt and navigate to the folder where you downloaded the wheel. Run the command: pip install [file name of wheel]
Download the SciPy wheel from: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy (similar to the step above).
As above, pip install [file name of wheel]
This was the order I got everything working. The second point is the most important one. Scipy needs Numpy+MKL, not just vanilla Numpy.
Install python 3.5
pip install "file path" (download Numpy+MKL wheel from here http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy)
pip install scipy
You probably just have too new (unsupported) Python 3.x installed.
This page has overcomplicated solutions to the problem. Most of numpy / scipy users should not need to compile their numpy installations or need to rely on 3rd party "numpy+mkl" wheels.
Downloading a compiler is an anti-pattern, you do not want to build
numpy, only use it. [github.com/numpy]
Solution
Once you have installed supported python version, remove your non-working numpy installation with
pip uninstall numpy
and install scipy with
pip install scipy --only-binary numpy
The --only-binary numpy will force installing binary wheel (.whl) version of numpy. If it fails, you have too new (not yet supported) version of python.
If you have multiple python versions installed, you can ensure that pip is installing the python version you want by
<path_to_python_executable> -m pip install <X>
instead of pip install <X>.
Why this is happening?
Scipy relies on numpy, as can be seen from the setup.py or just by reading the pip install logs.
If you have too new (non-supported) python installation, there are no built wheel (.whl) in the pip repository, but tarballs (.tar.gz), which in this case require the user machine to compile some C++-code during installation. See also: Python packaging: wheels vs tarball (tar.gz)
Appendix
Check the https://pypi.org/project/numpy/ for list of supported Python versions. Currently (2020-11-04) the newest supported python version is Python 3.9. when using numpy 1.19.3 or above, and Python 3.8 for numpy 1.19.2. (For compatibility of older numpy versions, see numpy release notes)
If you are on Windows and see pip trying to install numpy-<x>.tag.gz, you know it probably will not work. Try older version of Python, instead. You want to see pip to installing a binary wheel for numpy for Windows (numpy-<x>.whl). You can check the wheels in pip available for numpy here.
If you are working with Windows and Visual Studio 2015
Install miniconda http://conda.pydata.org/miniconda.html
Change your python environment to python 3.4 (32bit)
click on python environment 3.4 and open cmd
Enter the following commands
"conda install numpy"
"conda install pandas"
"conda install scipy"
Simple and Fast Installation of Scipy in Windows
From http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy download the
correct Scipy package for your Python version (e.g. the correct
package for python 3.5 and Windows x64 is scipy-0.19.1-cp35-cp35m-win_amd64.whl).
Open cmd inside the directory containing the downloaded Scipy
package.
Type pip install <<your-scipy-package-name>> (e.g. pip install
scipy-0.19.1-cp35-cp35m-win_amd64.whl).
My 5 cents; You can just install the entire (pre-compiled) SciPy from
https://github.com/scipy/scipy/releases
Good Luck!
For python27
1、Install numpy + mkl（download link:http://www.lfd.uci.edu/~gohlke/pythonlibs/）
2、install scipy (the same site)
OK!
Intel now provides a Python distribution for Linux / Windows / OS X for free called "Intel distribution for Python".
Its a complete Python distribution (e.g. python.exe is included in the package) which includes some pre-installed modules compiled against Intel's MKL (Math Kernel Library) and thus optimized for faster performance.
The distribution includes the modules NumPy, SciPy, scikit-learn, pandas, matplotlib, Numba, tbb, pyDAAL, Jupyter, and others. The drawback is a bit of lateness in upgrading to more recent versions of Python. For example as of today (1 May 2017) the distribution provides CPython 3.5 while the 3.6 version is already out. But if you don't need the new features they should be perfectly fine.
I was also getting same error while installing scikit-fuzzy. I resolved error as follows:
Install Numpy, a whl file
Install Scipy, again a whl file
choose file according to python version like amd64 for python3 and other win32 file for the python27
then pip install --user skfuzzy
I hope, It will work for you
Solutions:
As specified in many answers, download NumPy and SciPy whl from http://www.lfd.uci.edu/~gohlke/pythonlibs/ and install with
pip install <whl_location>
Building BLAS/LAPACK from source
Using Miniconda.
Refer:
ScikitLearn Installation
Easiest way to install BLAS and LAPACK for scipy?
do this, it solved for me
pip install -U scikit-learn
I got the same error trying to install scipy, having also installed Visual Studio C++, numpy, etc. My problem was that I'd just installed Python 3.9.
I removed version 3.9.0 and downgraded to version 3.8.6 and scipy installed without problems.
Using resources at http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy will solve the problem. However, you should be careful about versions compatibility. After trying for several times, finally I decided to uninstall python and then installed a fresh version of python along with numpy and then installed scipy and this resolved my problem.
install intel's distribution of python https://software.intel.com/en-us/intel-distribution-for-python
better of for distribution of python should contain them initially
I am using Python in Windows. For performance reasons I need certain Python packages built against Intel MKL, most notably numpy. So far I have been installing all packages I use from precompiled installers from http://www.lfd.uci.edu/~gohlke/pythonlibs/. Obviously, manual package management is somewhat inefficient.
I know package managers and distributions exist (pip, Anaconda, Enthought). Is there a way to combine package management for most of the packages with manual install of specific package builds?
So far I have briefly tried pip. I see that after manually updating a package from an exe installer pip freeze still reports the previous version, while Python picks up the new version. So something seems to go at least a bit wrong.
Very related discussions are Anaconda vs. EPD Enthought vs. manual installation of Python and Python packages installation in Windows, but I did not find an answer to my particular question there.
Conda has the ability to convert Golhke installers into conda packages. You'll need to specify the dependencies manually, since the metadata isn't include in the installers. For example, to convert the cvxopt installer to a conda package use:
conda convert cvxopt-1.1.7.win-amd64-py2.7.exe -d 'numpy >=1.8'
I have Python 2.7, and I have distutils installed.
I downloaded the latest version of Scipy for win 32.
For the life of me, I do not understand how to install it.
From the directions on the site, it states:
If you already have Python installed, the easiest way to install Numpy
and Scipy is to download and install the binary distribution from
Download.
I have followed the above directions and downloaded this.
I cannot figure what to do now!
How do I finish getting scipy installed?
It looks like you've downloaded the source distribution, which you would normally install by doing:
python setup.py install
However, without the proper C compiler environment and other libraries, it will probably fail. I'm guessing you really wanted to download the Windows binaries .
You have to drill a little further down in the sourceforge site to find it.
try downloading the windows binary ...
http://sourceforge.net/projects/scipy/files/scipy/0.11.0/scipy-0.11.0-win32-superpack-python2.7.exe/download
You'll be well off installing setuptools. Makes installing almost anything python-related a breeze!
e.g.
easy_install scipy
There's another one called pip.
easy_install pip
pip install scipy
just open windows command prompt and go to the directory you have installed Python. e.g
c:\python34>
Once there, just type python -m pip install scipy and press enter
I tried following the tutorial but after hours of building the ATLAS + LAPACK I got an error in the make install. I tried to download the following 4 libraries and install it still but no dice.
Currently I have installed numpy 1.3 and scipy 0.7.2 from the ubuntu repositories. I need a feature from scipy 0.9 though. Any way (preferably foolproof) I could install it?
ActivePython includes a package manager that allows you to install scipy 0.9 (among numpy, matplotlib, etc.) from PyPM.
pypm install numpy
These packages are built with ATLAS + LAPACK (Linux), veclib (OSX) or Intel MKL / ifortran (Windows).
To install Scipy 0.9, you need to have a newer Numpy installed than 1.3. The oldest Numpy that it will work with is 1.4:
https://github.com/scipy/scipy/blob/maintenance%2F0.9.x/INSTALL.txt
Hopefully upgrading your Numpy install will help!
Otherwise, I second Josh's recommendation for prepackaged distributions. I know several people who use Sage and say it is very easy to get up and running.
Where you able to install the dependencies first?
sudo apt-get install gcc g77 python-dev atlas3-base-dev
And then proceed with the installation of scipy?
I'm a big fan of the Enthought Python Distribution (EPD) to get most of my scientific libraries packaged cleanly in one place:
http://www.enthought.com/products/epd.php
It's free if you're in academia.
There are also other options like Python(x,y) and Sage:
http://code.google.com/p/pythonxy/
http://sagemath.org/
Prepackaged distributions are the closest you'll get to a foolproof way. I have built scipy/numpy from scratch before, but I can't help you without further details.