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. This is the recommended installation method for most users.

Instructions for installing from source, PyPI, ActivePython, various Linux distributions, or a development version are also provided.

Python version support#

Officially Python 3.8, 3.9 and 3.10.

Installing pandas#

Installing with Anaconda#

Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users.

The simplest way to install not only pandas, but Python and the most popular packages that make up the SciPy stack (IPython, NumPy, Matplotlib, …) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing.

After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software to be compiled.

Installation instructions for Anaconda can be found here.

A full list of the packages available as part of the Anaconda distribution can be found here.

Another advantage to installing Anaconda is that you don’t need admin rights to install it. Anaconda can install in the user’s home directory, which makes it trivial to delete Anaconda if you decide (just delete that folder).

Installing with Miniconda#

The previous section outlined how to get pandas installed as part of the Anaconda distribution. However this approach means you will install well over one hundred packages and involves downloading the installer which is a few hundred megabytes in size.

If you want to have more control on which packages, or have a limited internet bandwidth, then installing pandas with Miniconda may be a better solution.

Conda is the package manager that the Anaconda distribution is built upon. It is a package manager that is both cross-platform and language agnostic (it can play a similar role to a pip and virtualenv combination).

Miniconda allows you to create a minimal self contained Python installation, and then use the Conda command to install additional packages.

First you will need Conda to be installed and downloading and running the Miniconda will do this for you. The installer 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 -n name_of_my_env python

This will create a minimal environment with only Python installed in it. To put your self inside this environment run:

source activate name_of_my_env

On Windows the command is:

activate name_of_my_env

The final step required is to install pandas. This can be done with the following command:

conda install pandas

To install a specific pandas version:

conda install pandas=0.20.3

To install other packages, IPython for example:

conda install ipython

To install the full Anaconda distribution:

conda install anaconda

If you need packages that are available to pip but not conda, then install pip, and then use pip to install those packages:

conda install pip
pip install django

Installing from PyPI#

pandas can be installed via pip from PyPI.

Note

You must have pip>=19.3 to install from PyPI.

pip install pandas

Installing with ActivePython#

Installation instructions for ActivePython can be found here. Versions 2.7, 3.5 and 3.6 include pandas.

Installing using your Linux distribution’s package manager.#

The commands in this table will install pandas for Python 3 from your distribution.

Distribution

Status

Download / Repository Link

Install method

Debian

stable

official Debian repository

sudo apt-get install python3-pandas

Debian & Ubuntu

unstable (latest packages)

NeuroDebian

sudo apt-get install python3-pandas

Ubuntu

stable

official Ubuntu repository

sudo apt-get install python3-pandas

OpenSuse

stable

OpenSuse Repository

zypper in python3-pandas

Fedora

stable

official Fedora repository

dnf install python3-pandas

Centos/RHEL

stable

EPEL repository

yum install python3-pandas

However, the packages in the linux package managers are often a few versions behind, so to get the newest version of pandas, it’s recommended to install using the pip or conda methods described above.

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: installation instructions </getting_started.html>.

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.

Running the test suite#

pandas is equipped with an exhaustive set of unit tests, covering about 97% of the code base as of this writing. To run it on your machine to verify that everything is working (and that you have all of the dependencies, soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 3.58, then run:

>>> pd.test()
running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site-packages\pandas
============================= test session starts =============================
platform win32 -- Python 3.6.2, pytest-3.6.0, py-1.4.34, pluggy-0.4.0
rootdir: C:\Users\TP\Documents\Python\pandasdev\pandas, inifile: setup.cfg
collected 12145 items / 3 skipped

..................................................................S......
........S................................................................
.........................................................................

==================== 12130 passed, 12 skipped in 368.339 seconds =====================

Dependencies#

Package

Minimum supported version

NumPy

1.18.5

python-dateutil

2.8.1

pytz

2020.1

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.

Visualization#

Dependency

Minimum Version

Notes

matplotlib

3.3.2

Plotting library

Jinja2

2.11

Conditional formatting with DataFrame.style

tabulate

0.8.7

Printing in Markdown-friendly format (see tabulate)

Computation#

Dependency

Minimum Version

Notes

SciPy

1.4.1

Miscellaneous statistical functions

numba

0.50.1

Alternative execution engine for rolling operations (see Enhancing Performance)

xarray

0.15.1

pandas-like API for N-dimensional data

Excel files#

Dependency

Minimum Version

Notes

xlrd

2.0.1

Reading Excel

xlwt

1.3.0

Writing Excel

xlsxwriter

1.2.2

Writing Excel

openpyxl

3.0.3

Reading / writing for xlsx files

pyxlsb

1.0.6

Reading for xlsb files

HTML#

Dependency

Minimum Version

Notes

BeautifulSoup4

4.8.2

HTML parser for read_html

html5lib

1.1

HTML parser for read_html

lxml

4.5.0

HTML parser for read_html

One of the following combinations of libraries is needed to use the top-level read_html() function:

Warning

XML#

Dependency

Minimum Version

Notes

lxml

4.5.0

XML parser for read_xml and tree builder for to_xml

SQL databases#

Dependency

Minimum Version

Notes

SQLAlchemy

1.4.0

SQL support for databases other than sqlite

psycopg2

2.8.4

PostgreSQL engine for sqlalchemy

pymysql

0.10.1

MySQL engine for sqlalchemy

Other data sources#

Dependency

Minimum Version

Notes

PyTables

3.6.1

HDF5-based reading / writing

blosc

1.20.1

Compression for HDF5

zlib

Compression for HDF5

fastparquet

0.4.0

Parquet reading / writing

pyarrow

1.0.1

Parquet, ORC, and feather reading / writing

pyreadstat

1.1.0

SPSS files (.sav) reading

Warning

  • If you want to use read_orc(), it is highly recommended to install pyarrow using conda. The following is a summary of the environment in which read_orc() can work.

    System

    Conda

    PyPI

    Linux

    Successful

    Failed(pyarrow==3.0 Successful)

    macOS

    Successful

    Failed

    Windows

    Failed

    Failed

Access data in the cloud#

Dependency

Minimum Version

Notes

fsspec

0.7.4

Handling files aside from simple local and HTTP

gcsfs

0.6.0

Google Cloud Storage access

pandas-gbq

0.14.0

Google Big Query access

s3fs

0.4.0

Amazon S3 access

Clipboard#

Dependency

Minimum Version

Notes

PyQt4/PyQt5

Clipboard I/O

qtpy

Clipboard I/O

xclip

Clipboard I/O on linux

xsel

Clipboard I/O on linux

Compression#

Dependency

Minimum Version

Notes

brotli

0.7.0

Brotli compression

python-snappy

0.6.0

Snappy compression

Zstandard

0.15.2

Zstandard compression

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