The easiest way for the majority of users to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method for most users.
Officially Python 2.7, 3.4, 3.5, and 3.6
The easiest way to start experimenting with pandas doesn’t involve installing pandas at all.
Simply create an account, and have access to pandas from within your brower via an IPython Notebook in a few minutes.
The simplest way to install not only pandas, but Python and the most popular packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing.
After running a simple installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software to be compiled.
An additional advantage of installing with Anaconda is that you don’t require admin rights to install it, it will install in the user’s home directory, and this also makes it trivial to delete Anaconda at a later date (just delete that folder).
The previous section outlined how to get pandas installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size.
If you want to have more control on which packages, or have a limited internet bandwidth, then installing pandas with Miniconda may be a better solution.
Conda is the package manager that the Anaconda distribution is built upon. It is a package manager that is both cross-platform and language agnostic (it can play a similar role to a pip and virtualenv combination).
The next step is to create a new conda environment (these are analogous to a virtualenv but they also allow you to specify precisely which Python version to install also). Run the following commands from a terminal window:
conda create -n name_of_my_env python
This will create a minimal environment with only Python installed in it. To put your self inside this environment run:
source activate name_of_my_env
On Windows the command is:
The final step required is to install pandas. This can be done with the following command:
conda install pandas
To install a specific pandas version:
conda install pandas=0.13.1
To install other packages, IPython for example:
conda install ipython
To install the full Anaconda distribution:
conda install anaconda
If you require any packages that are available to pip but not conda, simply install pip, and use pip to install these packages:
conda install pip pip install django
pandas can be installed via pip from PyPI.
pip install pandas
This will likely require the installation of a number of dependencies, including NumPy, will require a compiler to compile required bits of code, and can take a few minutes to complete.
The commands in this table will install pandas for Python 2 from your distribution. To install pandas for Python 3 you may need to use the package
|Distribution||Status||Download / Repository Link||Install method|
|Debian||stable||official Debian repository|| |
|Debian & Ubuntu||unstable (latest packages)||NeuroDebian|| |
|Ubuntu||stable||official Ubuntu repository|| |
|Ubuntu||unstable (daily builds)||
PythonXY PPA; activate by: || |
|OpenSuse||stable||OpenSuse Repository|| |
|Fedora||stable||official Fedora repository|| |
|Centos/RHEL||stable||EPEL repository|| |
See the contributing documentation for complete instructions on building from the git source tree. Further, see creating a development environment if you wish to create a pandas development environment.
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:
>>> import pandas as pd >>> pd.test() Running unit tests for pandas pandas version 0.18.0 numpy version 1.10.2 pandas is installed in pandas Python version 2.7.11 |Continuum Analytics, Inc.| (default, Dec 6 2015, 18:57:58) [GCC 4.2.1 (Apple Inc. build 5577)] nose version 1.3.7 ..................................................................S...... ........S................................................................ ......................................................................... ---------------------------------------------------------------------- Ran 9252 tests in 368.339s OK (SKIP=117)
numexpruses multiple cores as well as smart chunking and caching to achieve large speedups. If installed, must be Version 2.1 or higher (excluding a buggy 2.4.4). Version 2.4.6 or higher is highly recommended.
bottleneckuses specialized cython routines to achieve large speedups.
You are highly encouraged to install these libraries, as they provide large speedups, especially if working with large data sets.
SQLAlchemy: for SQL database support. Version 0.8.1 or higher recommended. Besides SQLAlchemy, you also need a database specific driver. You can find an overview of supported drivers for each SQL dialect in the SQLAlchemy docs. Some common drivers are:
For Excel I/O:
read_clipboard(). Most package managers on Linux distributions will have
xselimmediately available for installation.
One of the following combinations of libraries is needed to use the top-level
read_html()will not work with only BeautifulSoup4 installed.
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.
Without the optional dependencies, many useful features will not work. Hence, it is highly recommended that you install these. A packaged distribution like Anaconda, or Enthought Canopy may be worth considering.
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© 2008–2014 the pandas development team
Licensed under the 3-clause BSD License.