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The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. The Conda package manager is the recommended installation method for most users.
Instructions for installing :ref:`from source <install.source>`, :ref:`PyPI <install.pypi>`, or a :ref:`development version <install.dev>` are also provided.
Officially Python 3.9, 3.10 and 3.11.
For users that are new to Python, the easiest way to install Python, pandas, and the packages 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 and scientific computing. Installation instructions for Anaconda can be found here.
For users experienced with Python, the recommended way to install pandas with Miniconda. Miniconda allows you to create a minimal, self-contained Python installation compared to Anaconda and use the Conda package manager to install additional packages and create a virtual environment for your installation. Installation instructions for Miniconda can be found here.
The next step is to create a new conda environment. A conda environment is like a virtualenv 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 pandasThis 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 Windows
activate name_of_my_envpandas can be installed via pip from PyPI.
pip install pandasNote
You must have pip>=19.3 to install from PyPI.
Note
It is recommended to install and run pandas from a virtual environment, for example, using the Python standard library's venv
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 :ref:`dependency section.<install.optional_dependencies>`
If you encounter an ImportError, it usually means that Python couldn't find pandas in the list of available
libraries. Python internally has a list of directories it searches through, to find packages. You can
obtain these directories with.
import sys
sys.pathOne way you could be encountering this error is if you have multiple Python installations on your system
and 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're
using. 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 :ref:`in this document <install.miniconda>`.
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 that you might be required to uninstall an existing version of pandas to install the development version.
pip uninstall pandas -ypandas is equipped with an exhaustive set of unit tests. The packages required to run the tests
can be installed with pip install "pandas[test]". To run the tests from a
Python terminal.
>>> import pandas as pd
>>> pd.test()
running: pytest -m "not slow and not network and not db" /home/user/anaconda3/lib/python3.9/site-packages/pandas
============================= test session starts ==============================
platform linux -- Python 3.9.7, pytest-6.2.5, py-1.11.0, pluggy-1.0.0
rootdir: /home/user
plugins: dash-1.19.0, anyio-3.5.0, hypothesis-6.29.3
collected 154975 items / 4 skipped / 154971 selected
........................................................................ [ 0%]
........................................................................ [ 99%]
....................................... [100%]
==================================== ERRORS ====================================
=================================== FAILURES ===================================
=============================== warnings summary ===============================
=========================== short test summary info ============================
= 1 failed, 146194 passed, 7402 skipped, 1367 xfailed, 5 xpassed, 197 warnings, 10 errors in 1090.16s (0:18:10) =Note
This is just an example of what information is shown. Test failures are not necessarily indicative of a broken pandas installation.
pandas requires the following dependencies.
| Package | Minimum supported version |
|---|---|
| NumPy | 1.22.4 |
| python-dateutil | 2.8.2 |
| pytz | 2020.1 |
| 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.
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.
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 Excel |
| xlsxwriter | 3.0.5 | excel | Writing Excel |
| openpyxl | 3.1.0 | excel | Reading / writing for xlsx files |
| pyxlsb | 1.0.10 | excel | Reading for xlsb files |
| python-calamine | 0.1.6 | excel | Reading for xls/xlsx/xlsb/ods 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]". ADBC drivers
must be installed separately.
| 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 | 0.8.0 | ADBC Driver for PostgreSQL | |
| adbc-driver-sqlite | 0.8.0 | 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 | 2022.12.0 | 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 |
| pandas-gbq | 0.19.0 | gcp | Google Big Query access |
| s3fs | 2022.11.0 | aws | Amazon S3 access |
Installable with pip install "pandas[clipboard]".
| Dependency | Minimum Version | pip extra | Notes |
|---|---|---|---|
| PyQt4/PyQt5 | 5.15.8 | 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[consortium-standard]"
| Dependency | Minimum Version | pip extra | Notes |
|---|---|---|---|
| dataframe-api-compat | 0.1.7 | consortium-standard | Consortium Standard-compatible implementation based on pandas |