Installation

The easiest way to install pandas is to install itas part of the Anaconda distribution, across platform distribution for data analysis and scientific computing.This is the recommended installation method for most users.

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

Python version support

Officially Python 3.5.3 and above, 3.6, 3.7, and 3.8.

Installing pandas

Installing with Anaconda

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

The simplest way to install not only pandas, but Python and the most popularpackages that make up the SciPy stack(IPython, NumPy,Matplotlib, …) is withAnaconda, a cross-platform(Linux, Mac OS X, Windows) Python distribution for data analytics andscientific computing.

After running the installer, the user will have access to pandas and therest of the SciPy stack without needing to installanything else, and without needing to wait for any software to be compiled.

Installation instructions for Anacondacan be found here.

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

Another advantage to installing Anaconda is that you don’t needadmin rights to install it. Anaconda can install in the user’s home directory,which makes it trivial to delete Anaconda if you decide (just deletethat folder).

Installing 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 packagesand 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 internetbandwidth, 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 aminimal self contained Python installation, and then use theConda command to install additional packages.

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

The next step is to create a new conda environment. A conda environment is like avirtualenv that allows you to specify a specific version of Python and set of libraries.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:

activate name_of_my_env

The final step required is to install pandas. This can be done with thefollowing command:

conda install pandas

To install a specific pandas version:

conda install pandas=0.20.3

To install other packages, IPython for example:

conda install ipython

To install the full Anacondadistribution:

conda install anaconda

If you need packages that are available to pip but not conda, theninstall pip, and then use pip to install those packages:

conda install pip
pip install django

Installing from PyPI

pandas can be installed via pip fromPyPI.

pip install pandas

Installing with ActivePython

Installation instructions forActivePython can be foundhere. Versions2.7 and 3.5 include pandas.

Installing using your Linux distribution’s package manager.

The commands in this table will install pandas for Python 3 from your distribution.To install pandas for Python 2, you may need to use the python-pandas package.

However, the packages in the linux package managers are often a few versions behind, soto get the newest version of pandas, it’s recommended to install using the pip or condamethods described above.

Installing from source

See the contributing guide 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% ofthe code base as of this writing. To run it on your machine to verify thateverything is working (and that you have all of the dependencies, soft and hard,installed), make sure you have pytest >= 4.0.2 and Hypothesis >= 3.58, then run:

>>> pd.test()
running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site-packages\pandas
============================= test session starts =============================
platform win32 -- Python 3.6.2, pytest-3.6.0, py-1.4.34, pluggy-0.4.0
rootdir: C:\Users\TP\Documents\Python\pandasdev\pandas, inifile: setup.cfg
collected 12145 items / 3 skipped
 
..................................................................S......
........S................................................................
.........................................................................
 
==================== 12130 passed, 12 skipped in 368.339 seconds =====================

Dependencies

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 2.6.2 or higher.
• bottleneck: for accelerating certain types of nanevaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed,must be Version 1.2.1 or higher.

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especiallywhen working with large data sets.

Optional dependencies

Pandas has many optional dependencies that are only used for specific methods.For example, pandas.read_hdf() requires the pytables package. If theoptional dependency is not installed, pandas will raise an ImportError whenthe method requiring that dependency is called.

Optional dependencies for parsing HTML

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

Changed in version 0.23.0.

• BeautifulSoup4 and html5lib
• BeautifulSoup4 and lxml
• BeautifulSoup4 and html5lib and lxml
• Only lxml, although see HTML Table Parsingfor 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 onlyBeautifulSoup4 installed.
• You are highly encouraged to read HTML Table Parsing gotchas.It explains issues surrounding the installation andusage of the above three libraries.