Installation#

The pandas development team officially distributes pandas for installation through the following methods:

  • Available on conda-forge for installation with the conda package manager.

  • Available on PyPI for installation with pip.

  • Available on Github for installation from source.

Note

pandas may be installable from other sources besides the ones listed above, but they are not managed by the pandas development team.

Python version support#

See Python support policy.

Installing pandas#

Installing with Conda#

For users working with the Conda package manager, pandas can be installed from the conda-forge channel.

conda install -c conda-forge pandas

To install the Conda package manager on your system, the Miniforge distribution is recommended.

Additionally, it is recommended to install and run pandas from a virtual environment.

conda create -c conda-forge -n name_of_my_env python pandas
# On Linux or MacOS
source activate name_of_my_env
# On Windows
activate name_of_my_env

Tip

For users that are new to Python, the easiest way to install Python, pandas, and the packages that make up the PyData stack such as SciPy, NumPy and Matplotlib is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing.

However, pandas from Anaconda is not officially managed by the pandas development team.

Installing with pip#

For users working with the pip package manager, pandas can be installed 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 dependency section.

Additionally, it is recommended to install and run pandas from a virtual environment, for example, using the Python standard library’s venv

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.

Installing the development version of pandas#

Installing the development version is the quickest way to:

  • Try a new feature that will be shipped in the next release (that is, a feature from a pull-request that was recently merged to the main branch).

  • Check whether a bug you encountered has been fixed since the last release.

The development version is usually uploaded daily to the scientific-python-nightly-wheels index from the PyPI registry of anaconda.org. You can install it by running.

pip install --pre --extra-index https://pypi.anaconda.org/scientific-python-nightly-wheels/simple pandas

Note

You might be required to uninstall an existing version of pandas to install the development version.

pip uninstall pandas -y

Running the test suite#

If pandas has been installed from source, running pytest pandas will run all of pandas unit tests.

The unit tests can also be run from the pandas module itself with the test() function. The packages required to run the tests can be installed with pip install "pandas[test]".

Note

Test failures are not necessarily indicative of a broken pandas installation.

Dependencies#

Required dependencies#

pandas requires the following dependencies.

Package

Minimum supported version

NumPy

1.23.5

python-dateutil

2.8.2

tzdata

2022.7

Optional dependencies#

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.

With 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]). All optional dependencies can be installed with pandas[all], and specific sets of dependencies are listed in the sections below.

Visualization#

Installable with pip install "pandas[plot, output-formatting]".

Dependency

Minimum Version

pip extra

Notes

matplotlib

3.6.3

plot

Plotting library

Jinja2

3.1.2

output-formatting

Conditional formatting with DataFrame.style

tabulate

0.9.0

output-formatting

Printing in Markdown-friendly format (see tabulate)

Computation#

Installable with pip install "pandas[computation]".

Dependency

Minimum Version

pip extra

Notes

SciPy

1.10.0

computation

Miscellaneous statistical functions

xarray

2022.12.0

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 for xls files

xlsxwriter

3.0.5

excel

Writing for xlsx files

openpyxl

3.1.0

excel

Reading / writing for Excel 2010 xlsx/xlsm/xltx/xltm files

pyxlsb

1.0.10

excel

Reading for xlsb files

python-calamine

0.1.7

excel

Reading for xls/xlsx/xlsm/xlsb/xla/xlam/ods files

odfpy

1.4.1

excel

Reading / writing for OpenDocument 1.2 files

HTML#

Installable with pip install "pandas[html]".

Dependency

Minimum Version

pip extra

Notes

BeautifulSoup4

4.11.2

html

HTML parser for read_html

html5lib

1.1

html

HTML parser for read_html

lxml

4.9.2

html

HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

XML#

Installable with pip install "pandas[xml]".

Dependency

Minimum Version

pip extra

Notes

lxml

4.9.2

xml

XML parser for read_xml and tree builder for to_xml

SQL databases#

Traditional drivers are installable with pip install "pandas[postgresql, mysql, sql-other]"

Dependency

Minimum Version

pip extra

Notes

SQLAlchemy

2.0.0

postgresql, mysql, sql-other

SQL support for databases other than sqlite

psycopg2

2.9.6

postgresql

PostgreSQL engine for sqlalchemy

pymysql

1.0.2

mysql

MySQL engine for sqlalchemy

adbc-driver-postgresql

0.10.0

postgresql

ADBC Driver for PostgreSQL

adbc-driver-sqlite

0.8.0

sql-other

ADBC Driver for SQLite

Other data sources#

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

Dependency

Minimum Version

pip extra

Notes

PyTables

3.8.0

hdf5

HDF5-based reading / writing

blosc

1.21.3

hdf5

Compression for HDF5; only available on conda

zlib

hdf5

Compression for HDF5

fastparquet

2023.10.0

Parquet reading / writing (pyarrow is default)

pyarrow

10.0.1

parquet, feather

Parquet, ORC, and feather reading / writing

pyreadstat

1.2.0

spss

SPSS files (.sav) reading

odfpy

1.4.1

excel

Open document format (.odf, .ods, .odt) reading / writing

Warning

  • If you want to use read_orc(), it is highly recommended to install pyarrow using conda. read_orc() may fail if pyarrow was installed from pypi, and read_orc() is not compatible with Windows OS.

Access data in the cloud#

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

Dependency

Minimum Version

pip extra

Notes

fsspec

2022.11.0

fss, gcp, aws

Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs).

gcsfs

2022.11.0

gcp

Google Cloud Storage access

s3fs

2022.11.0

aws

Amazon S3 access

Clipboard#

Installable with pip install "pandas[clipboard]".

Dependency

Minimum Version

pip extra

Notes

PyQt4/PyQt5

5.15.9

clipboard

Clipboard I/O

qtpy

2.3.0

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

Zstandard

0.19.0

compression

Zstandard compression

Timezone#

Installable with pip install "pandas[timezone]"

Dependency

Minimum Version

pip extra

Notes

pytz

2023.4

timezone

Alternative timezone library to zoneinfo.