The easiest way 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, ActivePython, various Linux distributions, or a development 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 and SciPy 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 with Anaconda, 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 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 the Anaconda distribution can be found here.

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 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).

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

First you will need Conda to be installed and downloading and running the Miniconda will do this for you. The installer can be found here

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 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.

pip install pandas

Installing with ActivePython

Installation instructions for ActivePython can be found here. 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 sudo apt-get install python3-pandas
Debian & Ubuntu unstable (latest packages) NeuroDebian sudo apt-get install python3-pandas
Ubuntu stable official Ubuntu repository sudo apt-get install python3-pandas
OpenSuse stable OpenSuse Repository zypper in python3-pandas
Fedora stable official Fedora repository dnf install python3-pandas
Centos/RHEL stable EPEL repository 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 >= 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


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


Package Minimum supported version
setuptools 24.2.0
NumPy 1.13.3
python-dateutil 2.6.1
pytz 2017.2

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)
Jinja2   Conditional formatting with
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.


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