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# 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

> - NumPy: 1.6.1 or higher

> - python-dateutil 1.5

> - pytz
>     - Needed for time zone support

## Recommended Dependencies

> - numexpr: for accelerating certain numerical operations. `numexpr` uses multiple cores as well as smart chunking and caching to achieve large speedups.
> - bottleneck: for accelerating certain types of `nan` evaluations. `bottleneck` uses specialized cython routines to achieve large speedups.

Note

You are highly encouraged to install these libraries, as they provide large speedups, especially if working with large data sets.

## 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:

>   - BeautifulSoup4 and html5lib (Any recent version of html5lib is okay.)
>   - BeautifulSoup4 and lxml
>   - BeautifulSoup4 and html5lib and lxml
>   - Only lxml, although see HTML reading gotchas for reasons as to why you should probably not take this approach.

>   Warning

>   - if you install BeautifulSoup4 you must install either lxml or html5lib or both. `read_html()` will not work with only BeautifulSoup4 installed.

>   - You are highly encouraged to read HTML reading gotchas. It explains issues surrounding the installation and usage of the above three libraries

>   - You may need to install an older version of BeautifulSoup4:
>       - Versions 4.2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and 32-bit Ubuntu/Debian

>   - Additionally, if you’re using Anaconda you should definitely read the gotchas about HTML parsing libraries

>   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
    ..........................................................................
    .......................S..................................................
    ..........................................................................
    ..........................................................................
    ..........................................................................
    ..........................................................................
    ..........................................................................
    ..........................................................................
    ..........................................................................
    ..........................................................................
    .................S........................................................
    ....
    ----------------------------------------------------------------------
    Ran 818 tests in 21.631s

    OK (SKIP=2)

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