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Installation

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 3.8, 3.9 and 3.10.

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, macOS, 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.

Note

You must have pip>=19.3 to install from PyPI.

pip install pandas

Installing with ActivePython

Installation instructions for ActivePython can be found here. Versions 2.7, 3.5 and 3.6 include pandas.

Installing using your Linux distribution's package manager.

The commands in this table will install pandas for Python 3 from your distribution.

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.

Handling ImportErrors

If you encounter an ImportError, it usually means that Python couldn't find pandas in the list of available libraries. Python internally has a list of directories it searches through, to find packages. You can obtain these directories with:

import sys
sys.path

One way you could be encountering this error is if you have multiple Python installations on your system and you don't have pandas installed in the Python installation you're currently using. In Linux/Mac you can run which python on your terminal and it will tell you which Python installation you're using. If it's something like "/usr/bin/python", you're using the Python from the system, which is not recommended.

It is highly recommended to use conda, for quick installation and for package and dependency updates. You can find simple installation instructions for pandas in this document: installation instructions </getting_started.html>.

Installing from source

See the :ref:`contributing guide <contributing>` for complete instructions on building from the git source tree. Further, see :ref:`creating a development environment <contributing_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 >= 6.0 and Hypothesis >= 6.13.0, then run:

>>> pd.test()
running: pytest --skip-slow --skip-network --skip-db /home/user/anaconda3/lib/python3.9/site-packages/pandas

============================= test session starts ==============================
platform linux -- Python 3.9.7, pytest-6.2.5, py-1.11.0, pluggy-1.0.0
rootdir: /home/user
plugins: dash-1.19.0, anyio-3.5.0, hypothesis-6.29.3
collected 154975 items / 4 skipped / 154971 selected
........................................................................ [  0%]
........................................................................ [ 99%]
.......................................                                  [100%]

==================================== ERRORS ====================================

=================================== FAILURES ===================================

=============================== warnings summary ===============================

=========================== short test summary info ============================

= 1 failed, 146194 passed, 7402 skipped, 1367 xfailed, 5 xpassed, 197 warnings, 10 errors in 1090.16s (0:18:10) =

This is just an example of what information is shown. You might see a slightly different result as what is shown above.

Dependencies

Package Minimum supported version
NumPy 1.19.5
python-dateutil 2.8.1
pytz 2020.1

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 2.7.3 or higher.
  • bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.3.2 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, :func:`pandas.read_hdf` requires the pytables package, while :meth:`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.

Visualization
Dependency Minimum Version Notes
matplotlib 3.3.2 Plotting library
Jinja2 3.0.0 Conditional formatting with DataFrame.style
tabulate 0.8.9 Printing in Markdown-friendly format (see tabulate)
Computation
Dependency Minimum Version Notes
SciPy 1.7.1 Miscellaneous statistical functions
numba 0.53.1 Alternative execution engine for rolling operations (see :ref:`Enhancing Performance <enhancingperf.numba>`)
xarray 0.19.0 pandas-like API for N-dimensional data
Excel files
Dependency Minimum Version Notes
xlrd 2.0.1 Reading Excel
xlwt 1.3.0 Writing Excel
xlsxwriter 1.4.3 Writing Excel
openpyxl 3.0.7 Reading / writing for xlsx files
pyxlsb 1.0.8 Reading for xlsb files
HTML
Dependency Minimum Version Notes
BeautifulSoup4 4.9.3 HTML parser for read_html
html5lib 1.1 HTML parser for read_html
lxml 4.6.3 HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level :func:`~pandas.read_html` function:

Warning

XML
Dependency Minimum Version Notes
lxml 4.5.0 XML parser for read_xml and tree builder for to_xml
SQL databases
Dependency Minimum Version Notes
SQLAlchemy 1.4.16 SQL support for databases other than sqlite
psycopg2 2.8.6 PostgreSQL engine for sqlalchemy
pymysql 1.0.2 MySQL engine for sqlalchemy
Other data sources
Dependency Minimum Version Notes
PyTables 3.6.1 HDF5-based reading / writing
blosc 1.21.0 Compression for HDF5
zlib   Compression for HDF5
fastparquet 0.4.0 Parquet reading / writing
pyarrow 1.0.1 Parquet, ORC, and feather reading / writing
pyreadstat 1.1.2 SPSS files (.sav) reading

Warning

  • If you want to use :func:`~pandas.read_orc`, it is highly recommended to install pyarrow using conda. The following is a summary of the environment in which :func:`~pandas.read_orc` can work.

    System

    Conda

    PyPI

    Linux

    Successful

    Failed(pyarrow==3.0 Successful)

    macOS

    Successful

    Failed

    Windows

    Failed

    Failed
Access data in the cloud
Dependency Minimum Version Notes
fsspec 2021.5.0 Handling files aside from simple local and HTTP
gcsfs 2021.5.0 Google Cloud Storage access
pandas-gbq 0.15.0 Google Big Query access
s3fs 2021.05.0 Amazon S3 access
Clipboard
Dependency Minimum Version Notes
PyQt4/PyQt5   Clipboard I/O
qtpy   Clipboard I/O
xclip   Clipboard I/O on linux
xsel   Clipboard I/O on linux
Compression
Dependency Minimum Version Notes
brotli 0.7.0 Brotli compression
python-snappy 0.6.0 Snappy compression
Zstandard 0.15.2 Zstandard compression