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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.

    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-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.

    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 | 1.26.0
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`.