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Markdown
322 lines
7.0 KiB
Markdown
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## Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks
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_\-- written by Sebastian Raschka_ on March 13, 2014
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* * *
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#### Sections
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• Anaconda and Miniconda
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• Consider a virtual environment
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• Installing pip
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• Installing NumPy
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• Installing SciPy
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• Installing matplotlib
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• Installing IPython
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• Updating installed packages
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* * *
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## Anaconda and Miniconda
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Alternatively, instead of going through all the manual steps listed in the
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following sections, there is the [Anaconda Python
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distribution](https://store.continuum.io/cshop/anaconda/) for scientific
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computing. Although Anaconda is distributed by Continuum Analytics, it is
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completely free and includes more than 125 packages for science and data
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analysis.
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The installation procedure is nicely summarized here:
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<http://docs.continuum.io/anaconda/install.html>
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If this is too much, the [Miniconda](http://repo.continuum.io/miniconda/)
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might be right for you. Miniconda is basically just a Python distribution with
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the Conda package manager, which let's us install a list of Python packages
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into a specified conda environment.
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$[bash]> conda create -n myenv python=3
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$[bash]> conda install -n myenv numpy scipy matplotlib ipython
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Note: environments will be created in `ROOT_DIR/envs` by default, you can use
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the `-p` instead of the `-n` flag in the conda commands above in order to
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specify a custom path.
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If you we decided pro Anaconda or Miniconda, we are basically done at this
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point. The following sections are explaining a more (semi)-manual approach to
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install the packages individually using `pip`.
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## Consider a virtual environment
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In order to not mess around with our system packages, we should consider
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setting up a virtual environment when we want to install the additional
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scientific packages.
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To set up a new virtual environment, we can use the following command
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$[bash]> python3 -m venv /path_to/my_virtual_env
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and activate it via
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$[bash]> source /path_to/my_virtual_env/bin/activate
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## Installing pip
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`pip` is a tool for installing and managing Python packages. It makes the
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installation process for Python packages a lot easier, since they don't have
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to be downloaded manually.
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If you haven't installed the `pip` package for your version of Python, yet,
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I'd suggest to download it from <https://pypi.python.org/pypi/pip>, unzip it,
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and install it from the unzipped directory via
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$[bash]> python3 setup.py install
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## Installing NumPy
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Installing NumPy should be straight forward now using `pip`
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$[bash]> python3 -m pip install numpy
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The installation will probably take a few minutes due to the source files that
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have to be compiled for your machine. Once it is installed, `NumPy` should be
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available in Python via
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>> import numpy
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If you want to see a few examples of how to operate with NumPy arrays, you can
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check out my [Matrix Cheatsheet for Moving from MATLAB matrices to NumPy
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arrays](http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html)
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## Installing SciPy
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While the `clang` compiler worked fine for compiling the C source code for
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`numpy`, we now need an additional Fortran compiler in order to install
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`scipy`.
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#### Installing a Fortran Compiler
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Unfortunately, MacOS 10.9 Mavericks doesn't come with a Fortran compiler, but
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it is pretty easy to download and install one.
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For example, `gfortran` for MacOS 10.9 can be downloaded from
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<http://coudert.name/software/gfortran-4.8.2-Mavericks.dmg>
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Just double-click on the downloaded .DMG container and follow the familiar
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MacOS X installation procedure. Once it is installed, the `gfortran` compiler
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should be available from the command line,. We can test it by typing
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$[bash]> gfortran -v
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Among other information, we will see the current version, e.g.,
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gcc version 4.8.2 (GCC)
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#### Installing SciPy
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Now, we should be good to go to install `SciPy` using `pip`.
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$[bash]> python3 -m pip install scipy
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After it was successfully installed - might also take a couple of minutes due
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to the source code compilation - it should be available in Python via
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>> import scipy
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## Installing matplotlib
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The installation process for matplotlib should go very smoothly using `pip`, I
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haven't encountered any hurdles here.
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$[bash]> python3 -m pip install matplotlib
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After successful installation, it can be imported in Python via
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>> import matplotlib
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The `matplotlib` library has become my favorite data plotting tool recently,
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you can check out some examples in my little matplotlib-gallery on GitHub:
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<https://github.com/rasbt/matplotlib_gallery>
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## Installing IPython
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#### Installing pyzmq
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The IPython kernel requires the `pyzmq` package to run, `pyzmq` contains
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Python bindings for ØMQ, which is a lightweight and fast messaging
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implementation. It can be installed via `pip`.
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$[bash]> python3 -m pip install pyzmq
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#### Installing pyside
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When I was trying to install the `pyside` package, I had it complaining about
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the missing `cmake`. It can be downloaded from:
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<http://www.cmake.org/files/v2.8/cmake-2.8.12.2-Darwin64-universal.dmg>
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Just as we did with `gfortran` in the Installing SciPy section, double-click
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on the downloaded .DMG container and follow the familiar MacOS X installation
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procedure.
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We can confirm that it was successfully installed by typing
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$[bash]> cmake --version
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into the command line where it would print something like
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cmake version 2.8.12.2
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#### Installing IPython
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Now, we should finally be able to install IPython with all its further
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dependencies (pygments, Sphinx, jinja2, docutils, markupsafe) via
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$[bash]> python3 -m pip install ipython[all]
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By doing this, we would install IPython to a custom location, e.g.,
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`/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-
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packages/IPython`.
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You can find the path to this location by importing IPython in Python and then
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print its path:
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>> import IPython
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>> IPython.__path__
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Finally, we can set an `alias` in our `.bash_profile` or `.bash_rc` file to
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conviniently run IPython from the console. E.g.,
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alias ipython3="python3 /Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/IPython/terminal/ipapp.py"
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(Don't forget to `source` the `.bash_rc` or `.bash_profile` file afterwards)
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Now we can run
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$[bash]> ipython3
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from you shell terminal to launch the interactive IPython shell, and
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$[bash]> ipython3 notebook
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to bring up the awesome IPython notebook in our browser, respectively.
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## Updating installed packages
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Finally, if we want to keep our freshly installed packages up to date, we'd
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run `pip` with the `\--upgrade` flag, for example
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$[bash]> python3 -m pip install numpy --upgrade
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