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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.
See :ref:`Python support policy <policies.python_support>`.
For users working with the Conda package manager,
pandas can be installed from the conda-forge channel.
conda install -c conda-forge pandasTo 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_envTip
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.
For users working with the pip package manager, pandas can be installed from PyPI.
pip install pandaspandas 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>`
Additionally, it is recommended to install and run pandas from a virtual environment, for example, using the Python standard library's venv
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.
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 pandasNote
You might be required to uninstall an existing version of pandas to install the development version.
pip uninstall pandas -yIf 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.
pandas requires the following dependencies.
| Package | Minimum supported version |
|---|---|
| NumPy | 1.23.5 |
| python-dateutil | 2.8.2 |
| tzdata | 2022.7 |
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.
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 | 2.8.4 | performance | Accelerates certain numerical operations by using multiple cores as well as smart chunking and caching to achieve large speedups |
| bottleneck | 1.3.6 | performance | Accelerates certain types of nan by using specialized cython routines to achieve large speedup. |
| numba | 0.56.4 | 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. |
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) |
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 |
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 |
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 :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.
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 |
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 |
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 :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.
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 |
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.
Installable with pip install "pandas[compression]"
| Dependency | Minimum Version | pip extra | Notes |
|---|---|---|---|
| Zstandard | 0.19.0 | compression | Zstandard compression |
Installable with pip install "pandas[timezone]"
| Dependency | Minimum Version | pip extra | Notes |
|---|---|---|---|
| pytz | 2023.4 | timezone | Alternative timezone library to zoneinfo. |