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.
Officially Python 3.6.1 and above, 3.7, and 3.8.
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
Matplotlib, …) is with
Anaconda, a cross-platform
(Linux, Mac OS X, Windows) Python distribution for data analytics and
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
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
The previous section outlined how to get pandas installed as part of the
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:
The final step required is to install pandas. This can be done with the
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
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
pandas can be installed via pip from
pip install pandas
Installation instructions for
ActivePython can be found
2.7, 3.5 and 3.6 include pandas.
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.
Download / Repository Link
official Debian repository
sudo apt-get install python3-pandas
Debian & Ubuntu
unstable (latest packages)
official Ubuntu repository
zypper in python3-pandas
official Fedora repository
dnf install python3-pandas
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.
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.
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 >= 5.0.1 and Hypothesis >= 3.58, then run:
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 =====================
Minimum supported version
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 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.
Pandas has many optional dependencies that are only used for specific methods.
For example, pandas.read_hdf() requires the pytables package, while
DataFrame.to_markdown() requires the tabulate package. If the
optional dependency is not installed, pandas will raise an ImportError when
the method requiring that dependency is called.
HTML parser for read_html (see note)
Conditional formatting with DataFrame.style
HDF5-based reading / writing
SQL support for databases other than sqlite
Miscellaneous statistical functions
Compression for HDF5
Parquet reading / writing
Google Cloud Storage access
Alternative execution engine for rolling operations
Reading / writing for xlsx files
Google Big Query access
PostgreSQL engine for sqlalchemy
Parquet, ORC (requires 0.13.0), and feather reading / writing
MySQL engine for sqlalchemy
SPSS files (.sav) reading
HDF5 reading / writing
Reading for xlsb files
Amazon S3 access
Printing in Markdown-friendly format (see tabulate)
pandas-like API for N-dimensional data
Clipboard I/O on linux
One of the following combinations of libraries is needed to use the
top-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 Parsing
for reasons as to why you should probably not take this approach.
if you install BeautifulSoup4 you must install either
lxml or html5lib or both.
read_html() will not work with only
You are highly encouraged to read HTML Table Parsing gotchas.
It explains issues surrounding the installation and
usage of the above three libraries.