Installation — Pandas 1.4.3 Documentation

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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.

condainstall-cconda-forgepandas

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.

condacreate-cconda-forge-nname_of_my_envpythonpandas # On Linux or MacOS sourceactivatename_of_my_env # On Windows activatename_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.

pipinstallpandas

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.

pipinstall"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.

pipinstall--pre--extra-index-urlhttps://pypi.anaconda.org/scientific-python-nightly-wheels/simplepandas

Note

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

pipuninstallpandas-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	1.26.0
python-dateutil	2.8.2
tzdata *	/

* tzdata is only required on Windows and Pyodide (Emscripten).

Generally, the minimum supported version is ~2 years old from the release date of a major or minor pandas version.

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.

Generally, the minimum supported version is ~1 years old from the release date of a major or minor pandas version. Older versions of optional dependencies may still work, but they are not tested or considered supported.

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	2.10.2	performance	Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups
bottleneck	1.4.2	performance	Accelerates certain types of nan by using specialized cython routines to achieve large speedup.
numba	0.60.0	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.

Visualization#

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

Dependency	Minimum Version	pip extra	Notes
matplotlib	3.8.3	plot	Plotting library
Jinja2	3.1.3	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	1.14.1	computation	Miscellaneous statistical functions
xarray	2024.10.0	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	3.2.0	excel	Writing for xlsx files
openpyxl	3.1.5	excel	Reading / writing for Excel 2010 xlsx/xlsm/xltx/xltm files
pyxlsb	1.0.10	excel	Reading for xlsb files
python-calamine	0.3.0	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	4.12.3	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:

• 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.

Warning

• if you install BeautifulSoup4 you must install either lxml or html5lib or both. read_html() will not work with only BeautifulSoup4 installed.

• You are highly encouraged to read HTML Table Parsing gotchas. It explains issues surrounding the installation and usage of the above three libraries.

XML#

Installable with pip install "pandas[xml]".

Dependency	Minimum Version	pip extra	Notes
lxml	5.3.0	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	2.0.36	postgresql, mysql, sql-other	SQL support for databases other than sqlite
psycopg2	2.9.10	postgresql	PostgreSQL engine for sqlalchemy
pymysql	1.1.1	mysql	MySQL engine for sqlalchemy
adbc-driver-postgresql	1.2.0	postgresql	ADBC Driver for PostgreSQL
adbc-driver-sqlite	1.2.0	sql-other	ADBC Driver for SQLite

Other data sources#

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

Dependency	Minimum Version	pip extra	Notes
PyTables	3.10.1	hdf5	HDF5-based reading / writing
zlib	hdf5	Compression for HDF5
fastparquet	2024.11.0	Parquet reading / writing (pyarrow is default)
pyarrow	13.0.0	parquet, feather	Parquet, ORC, and feather reading / writing
PyIceberg	0.8.1	iceberg	Apache Iceberg reading / writing
pyreadstat	1.2.8	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	2024.10.0	fss, gcp, aws	Handling files aside from simple local and HTTP (required dependency of s3fs, gcsfs).
gcsfs	2024.10.0	gcp	Google Cloud Storage access
s3fs	2024.10.0	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	2.4.2	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	2024.2	timezone	Alternative timezone library to zoneinfo.

On this page

• Python version support
• Installing pandas
  • Installing with Conda
  • Installing with pip
  • Installing from source
  • Installing the development version of pandas
• Running the test suite
• Dependencies
  • Required dependencies
  • Optional dependencies
    • Performance dependencies (recommended)
    • Visualization
    • Computation
    • Excel files
    • HTML
    • XML
    • SQL databases
    • Other data sources
    • Access data in the cloud
    • Clipboard
    • Compression
    • Timezone