Installation — pandas 3.0.0.dev0+2096.g8045c2d59f documentation (original) (raw)
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#
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, theMiniforge distributionis 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 asSciPy, NumPy andMatplotlibis 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.
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.
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.
Optional dependencies#
pandas has many optional dependencies that are only used for specific methods. For example, pandas.read_hdf() requires the pytables package, whileDataFrame.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.
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]"
Visualization#
Installable with pip install "pandas[plot, output-formatting]".
Computation#
Installable with pip install "pandas[computation]".
Excel files#
Installable with pip install "pandas[excel]".
HTML#
Installable with pip install "pandas[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 Parsingfor reasons as to why you should probably not take this approach.
Warning
- if you install BeautifulSoup4 you must install eitherlxml 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]".
SQL databases#
Traditional drivers are installable with pip install "pandas[postgresql, mysql, sql-other]"
Other data sources#
Installable with pip install "pandas[hdf5, parquet, feather, spss, excel]"
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]"
Clipboard#
Installable with pip install "pandas[clipboard]".
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]"
Timezone#
Installable with pip install "pandas[timezone]"