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.
Plan for dropping Python 2.7
The Python core team plans to stop supporting Python 2.7 on January 1st, 2020. In line with NumPy’s plans, all pandas releases through December 31, 2018 will support Python 2.
The 0.24.x feature release will be the last release to support Python 2. The released package will continue to be available on PyPI and through conda.
If there are people interested in continued support for Python 2.7 past December 31, 2018 (either backporting bug fixes or funding) please reach out to the maintainers on the issue tracker.
Python version support
Officially Python 2.7, 3.5, 3.6, and 3.7.
Installing with Anaconda
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, Mac OS X, 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.
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).
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.
$ pip install pandas
Installing with ActivePython
Installing using your Linux distribution’s package manager.
The commands in this table will install pandas for Python 3 from your distribution. To install pandas for Python 2, you may need to use the
|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
conda methods described above.
Installing from source
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 >= 4.0.2 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|
- numexpr: for accelerating certain numerical operations.
numexpruses multiple cores as well as smart chunking and caching to achieve large speedups. If installed, must be Version 2.6.2 or higher.
- bottleneck: for accelerating certain types of
bottleneckuses specialized cython routines to achieve large speedups. If installed, must be Version 1.2.1 or higher.
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. If the optional dependency is not installed, pandas will raise an
ImportError when the method requiring that dependency is called.
|BeautifulSoup4||4.6.0||HTML parser for read_html (see [[note](#optional-html)](#optional-html))|
|Jinja2||Conditional formatting with DataFrame.style|
|PyTables||3.4.2||HDF5-based reading / writing|
|SQLAlchemy||1.1.4||SQL support for databases other than sqlite|
|SciPy||0.19.0||Miscellaneous statistical functions|
|blosc||Compression for msgpack|
|fastparquet||0.2.1||Parquet reading / writing|
|gcsfs||0.2.2||Google Cloud Storage access|
|html5lib||HTML parser for read_html (see note)|
|lxml||3.8.0||HTML parser for read_html (see note)|
|openpyxl||2.4.8||Reading / writing for xlsx files|
|pandas-gbq||0.8.0||Google Big Query access|
|psycopg2||PostgreSQL engine for sqlalchemy|
|pyarrow||0.9.0||Parquet and feather reading / writing|
|pymysql||0.7.11||MySQL engine for sqlalchemy|
|pyreadstat||SPSS files (.sav) reading|
|pytables||3.4.2||HDF5 reading / writing|
|s3fs||0.0.8||Amazon S3 access|
|xarray||0.8.2||pandas-like API for N-dimensional data|
|xclip||Clipboard I/O on linux|
|xsel||Clipboard I/O on linux|
|zlib||Compression for msgpack|
Optional dependencies for parsing HTML
One of the following combinations of libraries is needed to use the top-level
Changed in version 0.23.0.