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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, 3.10 and 3.11.

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

pandas can also be installed with sets of optional dependencies to enable certain functionality. For example, to install pandas with the optional dependencies to read Excel files.

pip install "pandas[excel]"

The full list of extras that can be installed can be found in the :ref:`dependency section.<install.optional_dependencies>`

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 >= 7.0 and Hypothesis >= 6.34.2, 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

Required dependencies

pandas requires the following dependencies.

Package Minimum supported version
NumPy 1.20.3
python-dateutil 2.8.2
pytz 2020.1

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.

If using pip, optional pandas dependencies can be installed or managed in a file (e.g. requirements.txt or pyproject.toml) as optional extras (e.g.,``pandas[performance, aws]>=1.5.0``). All optional dependencies can be installed with pandas[all], and specific sets of dependencies are listed in the sections below.

Performance dependencies (recommended)

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.

Installable with pip install "pandas[performance]"

Dependency Minimum Version pip extra Notes
numexpr 2.7.3 performance Accelerates certain numerical operations by using uses multiple cores as well as smart chunking and caching to achieve large speedups
bottleneck 1.3.2 performance Accelerates certain types of nan by using specialized cython routines to achieve large speedup.
numba 0.53.1 performance Alternative execution engine for operations that accept engine="numba" using a JIT compiler that translates Python functions to optimized machine code using the LLVM compiler.
Timezones

Installable with pip install "pandas[timezone]"

Dependency Minimum Version pip extra Notes
tzdata 2022.1(pypi)/ 2022a(for system tzdata) timezone

Allows the use of zoneinfo timezones with pandas. Note: You only need to install the pypi package if your system does not already provide the IANA tz database. However, the minimum tzdata version still applies, even if it is not enforced through an error.

If you would like to keep your system tzdata version updated, it is recommended to use the tzdata package from conda-forge.
Visualization

Installable with pip install "pandas[plot, output_formatting]".

Dependency Minimum Version pip extra Notes
matplotlib 3.6.1 plot Plotting library
Jinja2 3.0.0 output_formatting Conditional formatting with DataFrame.style
tabulate 0.8.9 output_formatting Printing in Markdown-friendly format (see tabulate)
Computation

Installable with pip install "pandas[computation]".

Dependency Minimum Version pip extra Notes
SciPy 1.7.1 computation Miscellaneous statistical functions
xarray 0.21.0 computation pandas-like API for N-dimensional data
Excel files

Installable with pip install "pandas[excel]".

Dependency Minimum Version pip extra Notes
xlrd 2.0.1 excel Reading Excel
xlsxwriter 1.4.3 excel Writing Excel
openpyxl 3.0.7 excel Reading / writing for xlsx files
pyxlsb 1.0.8 excel Reading for xlsb files
HTML

Installable with pip install "pandas[html]".

Dependency Minimum Version pip extra Notes
BeautifulSoup4 4.9.3 html HTML parser for read_html
html5lib 1.1 html HTML parser for read_html
lxml 4.6.3 html 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

Installable with pip install "pandas[xml]".

Dependency Minimum Version pip extra Notes
lxml 4.6.3 xml XML parser for read_xml and tree builder for to_xml
SQL databases

Installable with pip install "pandas[postgresql, mysql, sql-other]".

Dependency Minimum Version pip extra Notes
SQLAlchemy 1.4.16 postgresql, mysql, sql-other SQL support for databases other than sqlite
psycopg2 2.8.6 postgresql PostgreSQL engine for sqlalchemy
pymysql 1.0.2 mysql MySQL engine for sqlalchemy
Other data sources

Installable with pip install "pandas[hdf5, parquet, feather, spss, excel]"

Dependency Minimum Version pip extra Notes
PyTables 3.6.1 hdf5 HDF5-based reading / writing
blosc 1.21.0 hdf5 Compression for HDF5; only available on conda
zlib   hdf5 Compression for HDF5
fastparquet 0.6.3
Parquet reading / writing (pyarrow is default)
pyarrow 6.0.0 parquet, feather Parquet, ORC, and feather reading / writing
pyreadstat 1.1.2 spss SPSS files (.sav) reading
odfpy 1.4.1 excel Open document format (.odf, .ods, .odt) reading / writing

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

    macOS

    Successful

    Failed

    Windows

    Failed

    Failed
Access data in the cloud

Installable with pip install "pandas[fss, aws, gcp]"

Dependency Minimum Version pip extra Notes
fsspec 2021.7.0 fss, gcp, aws Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs).
gcsfs 2021.7.0 gcp Google Cloud Storage access
pandas-gbq 0.15.0 gcp Google Big Query access
s3fs 2021.08.0 aws Amazon S3 access
Clipboard

Installable with pip install "pandas[clipboard]".

Dependency Minimum Version pip extra Notes
PyQt4/PyQt5 5.15.1 clipboard Clipboard I/O
qtpy 2.2.0 clipboard Clipboard I/O

Note

Depending on operating system, system-level packages may need to installed. For clipboard to operate on Linux one of the CLI tools xclip or xsel must be installed on your system.

Compression

Installable with pip install "pandas[compression]"

Dependency Minimum Version pip extra Notes
brotli 0.7.0 compression Brotli compression
python-snappy 0.6.0 compression Snappy compression
Zstandard 0.15.2 compression Zstandard compression