:orphan: .. _install: Installation ============ Pre-packaged binaries --------------------- To obtain the latest released version of statsmodels using pip:: pip install -U statsmodels Or follow `this link to our PyPI page <https://pypi.python.org/pypi/statsmodels>`__, download the wheel or source and install. Statsmodels is also available in through conda provided by `Anaconda <https://www.continuum.io/downloads>`__. The latest release can be installed using:: conda install -c conda-forge statsmodels For Windows users, unofficial recent binaries (wheels) are occasionally available `here <https://www.lfd.uci.edu/~gohlke/pythonlibs/#statsmodels>`__. Obtaining the Source -------------------- We do not release very often but the master branch of our source code is usually fine for everyday use. You can get the latest source from our `github repository <https://github.com/statsmodels/statsmodels>`__. Or if you have git installed:: git clone git://github.com/statsmodels/statsmodels.git If you want to keep up to date with the source on github just periodically do:: git pull in the statsmodels directory. Installation from Source ------------------------ You will need a C compiler installed to build statsmodels. If you are building from the github source and not a source release, then you will also need Cython. You can follow the instructions below to get a C compiler setup for Windows. If your system is already set up with pip, a compiler, and git, you can try:: pip install git+https://github.com/statsmodels/statsmodels If you do not have pip installed or want to do the installation more manually, you can also type:: .. code-block:: bash python setup.py install Or even more manually .. code-block:: bash python setup.py build python setup.py install statsmodels can also be installed in `develop` mode which installs statsmodels into the current python environment in-place. The advantage of this is that edited modules will immediately be re-interpreted when the python interpreter restarts without having to re-install statsmodels. .. code-block:: bash python setup.py develop Linux ^^^^^ If you are using Linux, we assume that you are savvy enough to install `gcc` on your own. More than likely, its already installed. Windows ^^^^^^^ It is strongly recommended to use 64-bit Python if possible. Getting the right compiler is especially confusing for Windows users. Over time, Python has been built using a variety of different Windows C compilers. `This guide <https://wiki.python.org/moin/WindowsCompilers>`_ should help clarify which version of Python uses which compiler by default. Mac ^^^ Installing statsmodels on MacOS will requires installing `gcc` which provides a suitable C compiler. We recommend installing Xcode and the Command Line Tools. Dependencies ------------ The current minimum dependencies are: * `Python <https://www.python.org>`__ >= 2.7, including Python 3.4+ * `NumPy <http://www.scipy.org/>`__ >= 1.11 * `SciPy <http://www.scipy.org/>`__ >= 0.18 * `Pandas <http://pandas.pydata.org/>`__ >= 0.19 * `Patsy <https://patsy.readthedocs.io/en/latest/>`__ >= 0.4.0 * `Cython <http://cython.org/>`__ >= 0.24 is required to build the code from github but not from a source distribution. Given the long release cycle, Statsmodels follows a loose time-based policy for dependencies: minimal dependencies are lagged about one and a half to two years. Our next planned update of minimum versions in `setup.py` is expected in September 2018, when we will update to reflect Numpy >= 1.12 (released January 2017), Scipy >= 0.19 (released March 2017) and Pandas >= 0.20 (released May 2017). Optional Dependencies --------------------- * `Matplotlib <http://matplotlib.org/>`__ >= 1.5 is needed for plotting functions and running many of the examples. * If installed, `X-12-ARIMA <http://www.census.gov/srd/www/x13as/>`__ or `X-13ARIMA-SEATS <http://www.census.gov/srd/www/x13as/>`__ can be used for time-series analysis. * `pytest <https://docs.pytest.org/en/latest/>`__ is required to run the test suite. * `IPython <http://ipython.org>`__ >= 3.0 is required to build the docs locally or to use the notebooks. * `joblib <http://pythonhosted.org/joblib/>`__ >= 0.9 can be used to accelerate distributed estimation for certain models. * `jupyter <http://jupyter.org/>`__ is needed to run the notebooks.