doc/source/getting_started/install.rst
.. _install:
{{ header }}
The pandas development team officially distributes pandas for installation through the following methods:
conda-forge <https://anaconda.org/conda-forge/pandas>__ for installation with the conda package manager.PyPI <https://pypi.org/project/pandas/>__ for installation with pip.Github <https://github.com/pandas-dev/pandas>__ 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.
.. _install.version:
See :ref:Python support policy <policies.python_support>.
.. _install.conda:
Installing with Conda
For users working with the `Conda <https://conda.io/en/latest/>`__ package manager,
pandas can be installed from the ``conda-forge`` channel.
.. code-block:: shell
conda install -c conda-forge pandas
To install the Conda package manager on your system, the
`Miniforge distribution <https://github.com/conda-forge/miniforge?tab=readme-ov-file#install>`__
is recommended.
Additionally, it is recommended to install and run pandas from a virtual environment.
.. code-block:: shell
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 <https://pydata.org/>`__ stack such as
`SciPy <https://scipy.org/>`__, `NumPy <https://numpy.org/>`__ and
`Matplotlib <https://matplotlib.org/>`__
is with `Anaconda <https://docs.anaconda.com/anaconda/install/>`__, 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.
.. _install.pip:
Installing with pip
~~~~~~~~~~~~~~~~~~~
For users working with the `pip <https://pip.pypa.io/en/stable/>`__ package manager,
pandas can be installed from `PyPI <https://pypi.org/project/pandas/>`__.
.. code-block:: shell
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.
.. code-block:: shell
pip install "pandas[excel]"
The full list of extras that can be installed can be found in the :ref:`dependency section.<install.optional_dependencies>`
Additionally, it is recommended to install and run pandas from a virtual environment, for example,
using the Python standard library's `venv <https://docs.python.org/3/library/venv.html>`__
.. _install.source:
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.
.. _install.dev:
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.
.. code-block:: shell
pip install --pre --extra-index-url 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.
.. code-block:: shell
pip uninstall pandas -y
Running the test suite
----------------------
If pandas has been installed :ref:`from source <install.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 :func:`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.
.. _install.dependencies:
Dependencies
------------
.. _install.required_dependencies:
Required dependencies
~~~~~~~~~~~~~~~~~~~~~
pandas requires the following dependencies.
================================================================ ==========================
Package Minimum supported version
================================================================ ==========================
`NumPy <https://numpy.org>`__ 1.26.0
`python-dateutil <https://dateutil.readthedocs.io/en/stable/>`__ 2.8.2
`tzdata <https://pypi.org/project/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.
.. _install.optional_dependencies:
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.
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.
.. _install.recommended_dependencies:
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 <https://github.com/pydata/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 <https://github.com/pydata/bottleneck>`__ 1.4.2 performance Accelerates certain types of ``nan`` by using specialized cython routines to achieve large speedup.
`numba <https://github.com/numba/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 <https://github.com/matplotlib/matplotlib>`__ 3.9.3 plot Plotting library
`Jinja2 <https://github.com/pallets/jinja>`__ 3.1.5 output-formatting Conditional formatting with DataFrame.style
`tabulate <https://github.com/astanin/python-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 <https://github.com/scipy/scipy>`__ 1.14.1 computation Miscellaneous statistical functions
`xarray <https://github.com/pydata/xarray>`__ 2024.10.0 computation pandas-like API for N-dimensional data
============================================== ================== =============== =======================================
.. _install.excel_dependencies:
Excel files
^^^^^^^^^^^
Installable with ``pip install "pandas[excel]"``.
================================================================== ================== =============== =============================================================
Dependency Minimum Version pip extra Notes
================================================================== ================== =============== =============================================================
`xlrd <https://github.com/python-excel/xlrd>`__ 2.0.1 excel Reading for xls files
`xlsxwriter <https://github.com/jmcnamara/XlsxWriter>`__ 3.2.0 excel Writing for xlsx files
`openpyxl <https://github.com/theorchard/openpyxl>`__ 3.1.5 excel Reading / writing for Excel 2010 xlsx/xlsm/xltx/xltm files
`pyxlsb <https://github.com/willtrnr/pyxlsb>`__ 1.0.10 excel Reading for xlsb files
`python-calamine <https://github.com/dimastbk/python-calamine>`__ 0.3.0 excel Reading for xls/xlsx/xlsm/xlsb/xla/xlam/ods files
`odfpy <https://github.com/eea/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 <https://github.com/wention/BeautifulSoup4>`__ 4.12.3 html HTML parser for read_html
`html5lib <https://github.com/html5lib/html5lib-python>`__ 1.1 html HTML parser for read_html
`lxml <https://github.com/lxml/lxml>`__ 5.3.0 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:
* `BeautifulSoup4`_ and `html5lib`_
* `BeautifulSoup4`_ and `lxml`_
* `BeautifulSoup4`_ and `html5lib`_ and `lxml`_
* Only `lxml`_, although see :ref:`HTML Table Parsing <io.html.gotchas>`
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.
:func:`~pandas.read_html` will **not** work with *only*
`BeautifulSoup4`_ installed.
* You are highly encouraged to read :ref:`HTML Table Parsing gotchas <io.html.gotchas>`.
It explains issues surrounding the installation and
usage of the above three libraries.
.. _html5lib: https://github.com/html5lib/html5lib-python
.. _BeautifulSoup4: https://www.crummy.com/software/BeautifulSoup
.. _lxml: https://lxml.de
.. _tabulate: https://github.com/astanin/python-tabulate
XML
^^^
Installable with ``pip install "pandas[xml]"``.
======================================== ================== =============== ====================================================
Dependency Minimum Version pip extra Notes
======================================== ================== =============== ====================================================
`lxml <https://github.com/lxml/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 <https://github.com/sqlalchemy/sqlalchemy>`__ 2.0.36 postgresql, SQL support for databases other than sqlite
mysql,
sql-other
`psycopg2 <https://github.com/psycopg/psycopg2>`__ 2.9.10 postgresql PostgreSQL engine for sqlalchemy
`pymysql <https://github.com/PyMySQL/PyMySQL>`__ 1.1.1 mysql MySQL engine for sqlalchemy
`adbc-driver-postgresql <https://github.com/apache/arrow-adbc>`__ 1.2.0 postgresql ADBC Driver for PostgreSQL
`adbc-driver-sqlite <https://github.com/apache/arrow-adbc>`__ 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 <https://github.com/PyTables/PyTables>`__ 3.10.1 hdf5 HDF5-based reading / writing
`zlib <https://github.com/madler/zlib>`__ hdf5 Compression for HDF5
`fastparquet <https://github.com/dask/fastparquet>`__ 2024.11.0 - Parquet reading / writing (pyarrow is default)
`pyarrow <https://github.com/apache/arrow>`__ 13.0.0 parquet, feather Parquet, ORC, and feather reading / writing
`PyIceberg <https://py.iceberg.apache.org/>`__ 0.8.1 iceberg Apache Iceberg reading / writing
`pyreadstat <https://github.com/Roche/pyreadstat>`__ 1.2.8 spss SPSS files (.sav) reading
`odfpy <https://github.com/eea/odfpy>`__ 1.4.1 excel Open document format (.odf, .ods, .odt) reading / writing
====================================================== ================== ================ ==========================================================
.. _install.warn_orc:
.. warning::
* If you want to use :func:`~pandas.read_orc`, it is highly recommended to install pyarrow using conda.
:func:`~pandas.read_orc` may fail if pyarrow was installed from pypi, and :func:`~pandas.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 <https://github.com/fsspec>`__ 2024.10.0 fss, gcp, aws Handling files aside from simple local and HTTP (required
dependency of s3fs, gcsfs).
`gcsfs <https://github.com/fsspec/gcsfs>`__ 2024.10.0 gcp Google Cloud Storage access
`s3fs <https://github.com/fsspec/s3fs>`__ 2024.10.0 aws Amazon S3 access
============================================ ================== =============== ==========================================================
Clipboard
^^^^^^^^^
Installable with ``pip install "pandas[clipboard]"``.
======================================================================================== ================== =============== ==============
Dependency Minimum Version pip extra Notes
======================================================================================== ================== =============== ==============
`PyQt5 <https://pypi.org/project/PyQt5/>`__ 5.15.9 clipboard Clipboard I/O
`qtpy <https://github.com/spyder-ide/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 <https://github.com/facebook/zstd>`__ 0.23.0 compression Zstandard compression
================================================= ================== =============== ======================
Timezone
^^^^^^^^
Installable with ``pip install "pandas[timezone]"``
========================================== ================== =================== ==============================================
Dependency Minimum Version pip extra Notes
========================================== ================== =================== ==============================================
`pytz <https://github.com/stub42/pytz>`__ 2024.2 timezone Alternative timezone library to ``zoneinfo``.
========================================== ================== =================== ==============================================