Installation

You have the option to install an official release or to build the development version. If you choose to install from source and are running Windows, you will have to ensure that you have a compatible C compiler (MinGW or Visual Studio) installed. How-to install MinGW on Windows

Python version support

Officially Python 2.6, 2.7, 3.2, 3.3, and 3.4.

Binary installers

All platforms

Stable installers available on PyPI

Preliminary builds and installers on the pandas download page .

Overview

Platform Distribution Status Download / Repository Link Install method
Windows all stable All platforms pip install pandas
Mac all stable All platforms pip install pandas
Linux Debian stable official Debian repository sudo apt-get install python-pandas
Linux Debian & Ubuntu unstable (latest packages) NeuroDebian sudo apt-get install python-pandas
Linux Ubuntu stable official Ubuntu repository sudo apt-get install python-pandas
Linux Ubuntu unstable (daily builds) PythonXY PPA; activate by: sudo add-apt-repository ppa:pythonxy/pythonxy-devel && sudo apt-get update sudo apt-get install python-pandas
Linux OpenSuse & Fedora stable OpenSuse Repository zypper in  python-pandas

Dependencies

Optional Dependencies

  • Cython: Only necessary to build development version. Version 0.17.1 or higher.

  • SciPy: miscellaneous statistical functions

  • PyTables: necessary for HDF5-based storage

  • SQLAlchemy: for SQL database support. Version 0.8.1 or higher recommended.

  • matplotlib: for plotting

  • statsmodels
    • Needed for parts of pandas.stats
  • openpyxl, xlrd/xlwt
    • openpyxl version 1.6.1 or higher, but lower than 2.0.0
    • Needed for Excel I/O
  • XlsxWriter
    • Alternative Excel writer.
  • boto: necessary for Amazon S3 access.

  • One of PyQt4, PySide, pygtk, xsel, or xclip: necessary to use read_clipboard(). Most package managers on Linux distributions will have xclip and/or xsel immediately available for installation.

  • Google’s python-gflags and google-api-python-client * Needed for gbq

  • httplib2 * Needed for gbq

  • One of the following combinations of libraries is needed to use the top-level read_html() function:

    Warning

    Note

    • if you’re on a system with apt-get you can do

      sudo apt-get build-dep python-lxml
      

      to get the necessary dependencies for installation of lxml. This will prevent further headaches down the line.

Note

Without the optional dependencies, many useful features will not work. Hence, it is highly recommended that you install these. A packaged distribution like Enthought Canopy may be worth considering.

Installing from source

Note

Installing from the git repository requires a recent installation of Cython as the cythonized C sources are no longer checked into source control. Released source distributions will contain the built C files. I recommend installing the latest Cython via easy_install -U Cython

The source code is hosted at http://github.com/pydata/pandas, it can be checked out using git and compiled / installed like so:

git clone git://github.com/pydata/pandas.git
cd pandas
python setup.py install

Make sure you have Cython installed when installing from the repository, rather then a tarball or pypi.

On Windows, I suggest installing the MinGW compiler suite following the directions linked to above. Once configured property, run the following on the command line:

python setup.py build --compiler=mingw32
python setup.py install

Note that you will not be able to import pandas if you open an interpreter in the source directory unless you build the C extensions in place:

python setup.py build_ext --inplace

The most recent version of MinGW (any installer dated after 2011-08-03) has removed the ‘-mno-cygwin’ option but Distutils has not yet been updated to reflect that. Thus, you may run into an error like “unrecognized command line option ‘-mno-cygwin’”. Until the bug is fixed in Distutils, you may need to install a slightly older version of MinGW (2011-08-02 installer).

Running the test suite

pandas is equipped with an exhaustive set of unit tests covering about 97% of the codebase as of this writing. To run it on your machine to verify that everything is working (and you have all of the dependencies, soft and hard, installed), make sure you have nose and run:

$ nosetests pandas
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Ran 818 tests in 21.631s

OK (SKIP=2)