Installation — pandas 2.2.2 documentation (2024)

Installing with Anaconda#

For users that are new to Python, the easiest way to install Python, pandas, and thepackages that make up the PyData stack(SciPy, NumPy,Matplotlib, and more)is with Anaconda, a cross-platform(Linux, macOS, Windows) Python distribution for data analytics andscientific computing. Installation instructions for Anacondacan be found here.

Installing with Miniconda#

For users experienced with Python, the recommended way to install pandas withMiniconda.Miniconda allows you to create a minimal, self-contained Python installation compared to Anaconda and use theConda package manager to install additional packagesand create a virtual environment for your installation. Installation instructions for Minicondacan be found here.

The next step is to create a new conda environment. A conda environment is like avirtualenv that allows you to specify a specific version of Python and set of libraries.Run the following commands from a terminal window.

conda create -c conda-forge -n name_of_my_env python pandas

This will create a minimal environment with only Python and pandas installed.To put your self inside this environment run.

source activate name_of_my_env# On Windowsactivate name_of_my_env

Installing from PyPI#

pandas can be installed via pip fromPyPI.

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.

Handling ImportErrors#

If you encounter an ImportError, it usually means that Python couldn’t find pandas in the list of availablelibraries. Python internally has a list of directories it searches through, to find packages. You canobtain these directories with.

import syssys.path

One way you could be encountering this error is if you have multiple Python installations on your systemand you don’t have pandas installed in the Python installation you’re currently using.In Linux/Mac you can run which python on your terminal and it will tell you which Python installation you’reusing. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended.

It is highly recommended to use conda, for quick installation and for package and dependency updates.You can find simple installation instructions for pandas in this document.

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 createa 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-wheelsindex 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 that you might be required to uninstall an existing version of pandas to install the development version.

pip uninstall pandas -y

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 theoptional dependency is not installed, pandas will raise an ImportError whenthe method requiring that dependency is called.

If using 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.