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.
Python version support¶
Officially Python 2.7, 3.4, 3.5, and 3.6
Trying out pandas, no installation required!¶
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.
Installing pandas with Anaconda¶
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).
Installing pandas with Miniconda¶
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
Installing from PyPI¶
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.
Installing using your Linux distribution’s package manager.¶
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:
|Fedora||stable||official Fedora repository||
Installing from source¶
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.
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:
>>> 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)
- numexpr: for accelerating certain numerical operations.
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.
- bottleneck: for accelerating certain types of
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.
Cython: Only necessary to build development version. Version 0.19.1 or higher.
SciPy: miscellaneous statistical functions
xarray: pandas like handling for > 2 dims, needed for converting Panels to xarray objects. Version 0.7.0 or higher is recommended.
PyTables: necessary for HDF5-based storage. Version 3.0.0 or higher required, Version 3.2.1 or higher highly recommended.
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:
matplotlib: for plotting
For Excel I/O:
Jinja2: Template engine for conditional HTML formatting.
boto: necessary for Amazon S3 access.
blosc: for msgpack compression using
Google’s `python-gflags <<https://github.com/google/python-gflags/>`__ , oauth2client , httplib2 and google-api-python-client : Needed for
Backports.lzma: Only for Python 2, for writing to and/or reading from an xz compressed DataFrame in CSV; Python 3 support is built into the standard library.
One of the following combinations of libraries is needed to use the top-level
- 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.
- 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
if you’re on a system with
apt-getyou 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.