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.
Officially Python 3.7.1 and above, 3.8, and 3.9.
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).
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
pandas can be installed via pip from PyPI.
pip install pandas
Installation instructions for ActivePython can be found here. Versions 2.7, 3.5 and 3.6 include pandas.
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
Ubuntu
official Ubuntu repository
OpenSuse
OpenSuse Repository
zypper in python3-pandas
Fedora
official Fedora repository
dnf install python3-pandas
Centos/RHEL
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.
pip
conda
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.
which python
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>.
installation instructions </getting_started.html>
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.
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 >= 5.0.1 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 =====================
Package
Minimum supported version
setuptools
24.2.0
NumPy
1.16.5
python-dateutil
2.7.3
pytz
2017.3
numexpr: for accelerating certain numerical operations. numexpr uses multiple cores as well as smart chunking and caching to achieve large speedups. If installed, must be Version 2.6.8 or higher.
numexpr
bottleneck: for accelerating certain types of nan evaluations. bottleneck uses specialized cython routines to achieve large speedups. If installed, must be Version 1.2.1 or higher.
nan
bottleneck
Note
You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.
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.
pandas.read_hdf()
pytables
DataFrame.to_markdown()
tabulate
ImportError
Dependency
Minimum Version
Notes
BeautifulSoup4
4.6.0
HTML parser for read_html (see note)
Jinja2
2.10
Conditional formatting with DataFrame.style
PyQt4
Clipboard I/O
PyQt5
PyTables
3.5.1
HDF5-based reading / writing
SQLAlchemy
1.2.8
SQL support for databases other than sqlite
SciPy
1.12.0
Miscellaneous statistical functions
xlsxwriter
1.0.2
Excel writing
blosc
1.15.0
Compression for HDF5
fsspec
0.7.4
Handling files aside from local and HTTP
fastparquet
0.3.2
Parquet reading / writing
gcsfs
0.6.0
Google Cloud Storage access
html5lib
1.0.1
lxml
4.3.0
matplotlib
2.2.3
Visualization
numba
0.46.0
Alternative execution engine for rolling operations
openpyxl
2.6.0
Reading / writing for xlsx files
pandas-gbq
0.12.0
Google Big Query access
psycopg2
2.7
PostgreSQL engine for sqlalchemy
pyarrow
0.15.0
Parquet, ORC, and feather reading / writing
pymysql
0.7.11
MySQL engine for sqlalchemy
pyreadstat
SPSS files (.sav) reading
pyxlsb
1.0.6
Reading for xlsb files
qtpy
s3fs
0.4.0
Amazon S3 access
0.8.3
Printing in Markdown-friendly format (see tabulate)
xarray
0.12.3
pandas-like API for N-dimensional data
xclip
Clipboard I/O on linux
xlrd
1.2.0
Excel reading
xlwt
1.3.0
xsel
zlib
One of the following combinations of libraries is needed to use the top-level read_html() function:
read_html()
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.