Installation — pandas 0.18.1 documentation (original) (raw)

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.

Instructions for installing from source,PyPI, various Linux distributions, or adevelopment version are also provided.

Python version support¶

Officially Python 2.7, 3.4, and 3.5

Installing pandas¶

Trying out pandas, no installation required

The easiest way to start experimenting with pandas doesn’t involve installing pandas at all.

Wakari is a free service that provides a hostedIPython Notebook service in the cloud.

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¶

Installing pandas and the rest of the NumPy andSciPy stack can be a little difficult for inexperienced users.

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 withAnaconda, 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.

Installation instructions for Anaconda can be found here.

A full list of the packages available as part of theAnaconda distributioncan be found here.

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 theAnaconda 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 withMiniconda may be a better solution.

Conda is the package manager that theAnaconda 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).

Miniconda allows you to create a minimal self contained Python installation, and then use theConda command to install additional packages.

First you will need Conda to be installed and downloading and running the Minicondawill do this for you. The installercan be found here

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:

To install a specific pandas version:

conda install pandas=0.13.1

To install other packages, IPython for example:

To install the full Anacondadistribution:

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 fromPyPI.

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 python3-pandas.

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)

Dependencies¶

• setuptools
• NumPy: 1.7.1 or higher
• python-dateutil: 1.5 or higher
• 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. 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 nanevaluations. 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 0.19.1 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:

  • psycopg2: for PostgreSQL
  • pymysql: for MySQL.
  • SQLite: for SQLite, this is included in Python’s standard library by default.

• matplotlib: for plotting
• openpyxl, xlrd/xlwt: Needed for Excel I/O
• XlsxWriter: Alternative Excel writer
• Jinja2: Template engine for conditional HTML formatting.
• boto: necessary for Amazon S3 access.
• blosc: for msgpack compression using blosc
• One of PyQt4, PySide, pygtk, xsel, or xclip: necessary to useread_clipboard(). Most package managers on Linux distributions will have xclip and/or xsel immediately available for installation.
• Google’s python-gflags ,oauth2client ,httplib2and google-api-python-client: Needed for gbq
• 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 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 gotchasfor reasons as to why you should probably not take this approach.
    Warning
  • if you install BeautifulSoup4 you must install eitherlxml 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 Anaconda, or Enthought Canopy may be worth considering.