# Installation

The easiest way to install pandas is to install it as part of the Anaconda (opens 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, PyPI (opens new window), ActivePython (opens new window), various Linux distributions, or a development version (opens 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 plans (opens 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 statement (opens new window) and the Porting to Python 3 guide (opens 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 NumPy (opens new window) and SciPy (opens 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 SciPy (opens new window) stack (IPython (opens new window), NumPy (opens new window), Matplotlib (opens new window), …) is with Anaconda (opens 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 SciPy (opens new window) stack without needing to install anything else, and without needing to wait for any software to be compiled.

Installation instructions for Anaconda (opens new window) can be found here (opens new window).

A full list of the packages available as part of the Anaconda (opens new window) distribution can be found here (opens 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 Anaconda (opens 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 Miniconda (opens new window) may be a better solution.

Conda (opens new window) is the package manager that the Anaconda (opens 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).

Miniconda (opens new window) allows you to create a minimal self contained Python installation, and then use the Conda (opens new window) command to install additional packages.

First you will need Conda (opens new window) to be installed and downloading and running the Miniconda (opens new window) will do this for you. The installer can be found here (opens 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 Anaconda (opens 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 PyPI (opens new window).

$ pip install pandas

# Installing with ActivePython

Installation instructions for ActivePython (opens new window) can be found here (opens 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.

Distribution Status Download / Repository Link Install method
Debian stable official Debian repository (opens new window) sudo apt-get install python3-pandas
Debian & Ubuntu unstable (latest packages) NeuroDebian (opens new window) sudo apt-get install python3-pandas
Ubuntu stable official Ubuntu repository (opens new window) sudo apt-get install python3-pandas
OpenSuse stable OpenSuse Repository (opens new window) zypper in python3-pandas
Fedora stable official Fedora repository (opens new window) dnf install python3-pandas
Centos/RHEL stable EPEL repository (opens new window) yum 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 pytest (opens new window) >= 4.0.2 and Hypothesis (opens 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

..................................................................S......
........S................................................................
.........................................................................

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

# Dependencies

Package Minimum supported version
setuptools (opens new window) 24.2.0
NumPy (opens new window) 1.13.3
python-dateutil (opens new window) 2.6.1
pytz (opens new window) 2017.2
  • numexpr (opens 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.
  • bottleneck (opens 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.

Note

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.

Dependency Minimum Version Notes
BeautifulSoup4 4.6.0 HTML parser for read_html (see [[note](#optional-html)](#optional-html))
Jinja2 Conditional formatting with DataFrame.style
PyQt4 Clipboard I/O
PyQt5 Clipboard I/O
PyTables 3.4.2 HDF5-based reading / writing
SQLAlchemy 1.1.4 SQL support for databases other than sqlite
SciPy 0.19.0 Miscellaneous statistical functions
XLsxWriter 0.9.8 Excel writing
blosc Compression for msgpack
fastparquet 0.2.1 Parquet reading / writing
gcsfs 0.2.2 Google Cloud Storage access
html5lib HTML parser for read_html (see note)
lxml 3.8.0 HTML parser for read_html (see note)
matplotlib 2.2.2 Visualization
openpyxl 2.4.8 Reading / writing for xlsx files
pandas-gbq 0.8.0 Google Big Query access
psycopg2 PostgreSQL engine for sqlalchemy
pyarrow 0.9.0 Parquet and feather reading / writing
pymysql 0.7.11 MySQL engine for sqlalchemy
pyreadstat SPSS files (.sav) reading
pytables 3.4.2 HDF5 reading / writing
qtpy Clipboard I/O
s3fs 0.0.8 Amazon S3 access
xarray 0.8.2 pandas-like API for N-dimensional data
xclip Clipboard I/O on linux
xlrd 1.1.0 Excel reading
xlwt 1.2.0 Excel writing
xsel Clipboard I/O on linux
zlib Compression 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.

Warning