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# Installation

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 from source, PyPI, or a development version are also provided.

## Python version support

Officially Python 3.9, 3.10, 3.11 and 3.12.

## Installing pandas

### Installing with Anaconda

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.

### Installing with Miniconda

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 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 Windows
    activate name_of_my_env

### Installing from PyPI

pandas can be installed via pip from PyPI.

    pip install pandas

Note

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 dependency section.

### Handling ImportErrors

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.path

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

    pip uninstall pandas -y

## Running the test suite

pandas 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.

## Dependencies

### Required dependencies

pandas requires the following dependencies.

Package | Minimum supported version
--- | ---
NumPy | 1.22.4
python-dateutil | 2.8.2
pytz | 2020.1
tzdata | 2022.7

### 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.

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.

#### 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 | 2.8.4 | 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.

#### Visualization

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)

#### Computation

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

#### Excel files

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 | 0.1.7 | excel | Reading for xls/xlsx/xlsb/ods files

#### HTML

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 `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 | 4.9.2 | 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 | 2.0.0 | 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 | postgresql | ADBC Driver for PostgreSQL
adbc-driver-sqlite | 0.8.0 | sql-other | ADBC Driver for SQLite

#### Other data sources

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 `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 | 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

#### Clipboard

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.

#### Compression

Installable with `pip install "pandas[compression]"`

Dependency | Minimum Version | pip extra | Notes
--- | --- | --- | ---
Zstandard | 0.19.0 | compression | Zstandard compression

#### Consortium Standard

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