![]() Of NumPy, Scipy and Matplotlib means that these packages are difficult to Unfortunately, the way Apple currently installs its own copies In order to fully remove an installed Matplotlib:ĭelete the caches from your Matplotlib configuration directory.ĭelete any Matplotlib directories or eggs from your installationĪpple ships OSX with its own Python, in /usr/bin/python, and its own copy Occasionally, problems with Matplotlib can be solved with a clean If you are still having trouble, see Getting help. Instructions within and the concept of the Matplotlib backend. Process, governed by the matplotlibrc configuration file which contains Sure you understand Matplotlib's configuration This will give you additional information about which backends Matplotlib is Python - c "from pylab import * set_loglevel('debug') plot() show()" Open up a UNIX shell or a DOS command prompt and run, for Rather than working interactively from a python shell or an integratedĭevelopment environment such as IDLE which add additionalĬomplexities. If not, the best way to test your install is by running a script, The first thing to try is a clean install and see if Matplotlib compiled fine, but nothing shows up when I use it # Matplotlib compiled fine, but nothing shows up when I use it See Setting up Matplotlib for development. That any DLLs that you copy into the source tree will be packaged too. ![]() If you are building your own Matplotlib wheels (or sdists) on Windows, note Thisįile will be particularly useful to those packaging Matplotlib. Optional libraries that Matplotlib ships with are installed, and so on. For example, which default backend to use, whether some of the We provide a mplsetup.cfg file which you can use to customize the build If you would like to build from a tarball, grab the latest tar.gz release May result in your build producing unexpected behavior and/or causing Proceed with caution because these instructions The following instructions in this section are for very custom The easiest way to get the latest development version to start contributing Required to build matplotlib from source. They should be selected by default under the "Optional" subheading, but are Windows SDK compatible with your version of Windows are selected and installed. Selected, and that the latest MSVC, "C CMake tools for Windows," and a Will need xcode on Windows, you will need Visual Studio 2015 or later.įor those using Visual Studio, make sure "Desktop development with C " is Should be installed using your distribution's package manager on macOS, you Typically, on Linux, you will need gcc, which Yourself, it is not difficult to build Matplotlib from source.įirst you need to install the Dependencies.Ī C compiler is required. Running the latest source code, or just like to build everything If you are interested in contributing to Matplotlib development, However, i am looking for a general solution for such problems.Python -m pip install \ -upgrade \ -pre \ -index-url \ -extra-index-url \ matplotlib conda install -c anaconda scikit-learn) the installation seems to have no problem and both packages work fine (though the installed scikit-learn isn't the latest version). I have already (in another machine) installed scikit-learn (with pip) and then tensor-flow and they both worked fine.Īlso when i am using anaconda as channel parameter (i.e. The documentation for install says "To prevent existing packages from updating, use the -freeze-installed option." however, it doesn't work and already existing packages still change. And finally the most import one: how can I prevent this? how cant i force conda not to change the already existing packages and find the version of scikit-learn compatible with the current environment.Why some already existing packages with similar version numbers are also downloaded again?.Answer: it maybe due to the fact that tensor-flow is installed with pip not conda If full compatibility cannot be assured, an error is reported and the environment is not changed". Shouldn't conda automatically prevent this? because the documentation for install says: " installs a set of packages consistent with those specifications and compatible with the underlying environment.There are other packages that are also updated and some of them still downloading the same version like certifi-2023.5.7! Numpy 1.23.5 -> is updated to numpy-1.24.3 (and tensor-flow have problems with this version)Ĭharset-normalizer is also changed to charset-normalizer-2.0.4 (which also causes issues) the problem is that some packages are updated, which are inconsistent with the current version of tensorflow (for instance) : ![]() after a while, i wanted to install scikit-learn (with conda in the same environment), however tensorflow stopped working. I have installed tensorflow (the last version), and it works perfectly.
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