The easiest way to install pandas is to install it as part of the Anacondaopen in new window 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, PyPIopen in new window, ActivePythonopen in new window, various Linux distributions, or a development versionopen in new window are also provided.

Plan for dropping Python 2.7

The Python core team plans to stop supporting Python 2.7 on January 1st, 2020. In line with NumPy’s plansopen in new window, all pandas releases through December 31, 2018 will support Python 2.

The 0.24.x feature release will be the last release to support Python 2. The released package will continue to be available on PyPI and through conda.

If there are people interested in continued support for Python 2.7 past December 31, 2018 (either backporting bug fixes or funding) please reach out to the maintainers on the issue tracker.

For more information, see the Python 3 statementopen in new window and the Porting to Python 3 guideopen in new window.

Python version support

Officially Python 2.7, 3.5, 3.6, and 3.7.

Installing pandas

Installing with Anaconda

Installing pandas and the rest of the NumPyopen in new window and SciPyopen in new window 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 SciPyopen in new window stack (IPythonopen in new window, NumPyopen in new window, Matplotlibopen in new window, …) is with Anacondaopen in new window, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing.

After running the installer, the user will have access to pandas and the rest of the SciPyopen in new window stack without needing to install anything else, and without needing to wait for any software to be compiled.

Installation instructions for Anacondaopen in new windowcan be found hereopen in new window.

A full list of the packages available as part of the Anacondaopen in new window distribution can be found hereopen in new window.

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

Installing with Miniconda

The previous section outlined how to get pandas installed as part of the Anacondaopen in new window 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 Minicondaopen in new window may be a better solution.

Condaopen in new window is the package manager that the Anacondaopen in new window 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).

Minicondaopen in new window allows you to create a minimal self contained Python installation, and then use the Condaopen in new window command to install additional packages.

First you will need Condaopen in new window to be installed and downloading and running the Minicondaopen in new window will do this for you. The installer can be found hereopen in new window

The next step is to create a new conda environment. A conda environment is like a virtualenv 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 the following 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 Anacondaopen in new window distribution:

$ conda install anaconda

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

$ conda install pip
$ pip install django

Installing from PyPI

pandas can be installed via pip from PyPIopen in new window.

$ pip install pandas

Installing with ActivePython

Installation instructions for ActivePythonopen in new window can be found hereopen in new window. Versions 2.7 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.

DistributionStatusDownload / Repository LinkInstall method
Debianstableofficial Debian repositoryopen in new windowsudo apt-get install python3-pandas
Debian & Ubuntuunstable (latest packages)NeuroDebianopen in new windowsudo apt-get install python3-pandas
Ubuntustableofficial Ubuntu repositoryopen in new windowsudo apt-get install python3-pandas
OpenSusestableOpenSuse Repositoryopen in new windowzypper in python3-pandas
Fedorastableofficial Fedora repositoryopen in new windowdnf install python3-pandas
Centos/RHELstableEPEL repositoryopen in new windowyum install python3-pandas

However, the packages in the linux package managers are often a few versions behind, so to get the newest version of pandas, it’s recommended to install using the pip or conda methods 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% of the code base as of this writing. To run it on your machine to verify that everything is working (and that you have all of the dependencies, soft and hard, installed), make sure you have pytestopen in new window >= 4.0.2 and Hypothesisopen in new window >= 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


==================== 12130 passed, 12 skipped in 368.339 seconds =====================


PackageMinimum supported version
setuptoolsopen in new window24.2.0
NumPyopen in new window1.13.3
python-dateutilopen in new window2.6.1
pytzopen in new window2017.2
  • numexpropen in new window: 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.
  • bottleneckopen in new window: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.2.1 or higher.


You are highly encouraged to install these libraries, as they provide speed improvements, especially when 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 the optional dependency is not installed, pandas will raise an ImportError when the method requiring that dependency is called.

DependencyMinimum VersionNotes
BeautifulSoup44.6.0HTML parser for read_html (see [[note](#optional-html)](#optional-html))
Jinja2Conditional formatting with
PyQt4Clipboard I/O
PyQt5Clipboard I/O
PyTables3.4.2HDF5-based reading / writing
SQLAlchemy1.1.4SQL support for databases other than sqlite
SciPy0.19.0Miscellaneous statistical functions
XLsxWriter0.9.8Excel writing
bloscCompression for msgpack
fastparquet0.2.1Parquet reading / writing
gcsfs0.2.2Google Cloud Storage access
html5libHTML parser for read_html (see note)
lxml3.8.0HTML parser for read_html (see note)
openpyxl2.4.8Reading / writing for xlsx files
pandas-gbq0.8.0Google Big Query access
psycopg2PostgreSQL engine for sqlalchemy
pyarrow0.9.0Parquet and feather reading / writing
pymysql0.7.11MySQL engine for sqlalchemy
pyreadstatSPSS files (.sav) reading
pytables3.4.2HDF5 reading / writing
qtpyClipboard I/O
s3fs0.0.8Amazon S3 access
xarray0.8.2pandas-like API for N-dimensional data
xclipClipboard I/O on linux
xlrd1.1.0Excel reading
xlwt1.2.0Excel writing
xselClipboard I/O on linux
zlibCompression for msgpack

Optional dependencies for parsing HTML

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

Changed in version 0.23.0.