# 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 3.7.1 and above, 3.8, and 3.9.

## 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.

    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 | 1.17.3
python-dateutil | 2.7.3
pytz | 2017.3

### Recommended dependencies

- 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 2.7.0 or higher.

- 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.

Note

You are highly encouraged to install these libraries, as they provide speed improvements, especially when working with large data sets.

### 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
--- | --- | ---
setuptools | 38.6.0 | Utils for entry points of plotting backend
matplotlib | 2.2.3 | Plotting library
Jinja2 | 2.10 | Conditional formatting with DataFrame.style
tabulate | 0.8.7 | Printing in Markdown-friendly format (see tabulate)

#### Computation

Dependency | Minimum Version | Notes
--- | --- | ---
SciPy | 1.12.0 | Miscellaneous statistical functions
numba | 0.46.0 | Alternative execution engine for rolling operations (see Enhancing Performance)
xarray | 0.12.3 | pandas-like API for N-dimensional data

#### Excel files

Dependency | Minimum Version | Notes
--- | --- | ---
xlrd | 1.2.0 | Reading Excel
xlwt | 1.3.0 | Writing Excel
xlsxwriter | 1.0.2 | Writing Excel
openpyxl | 3.0.0 | Reading / writing for xlsx files
pyxlsb | 1.0.6 | Reading for xlsb files

#### HTML

Dependency | Minimum Version | Notes
--- | --- | ---
BeautifulSoup4 | 4.6.0 | HTML parser for read_html
html5lib | 1.0.1 | HTML parser for read_html
lxml | 4.3.0 | 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

Dependency | Minimum Version | Notes
--- | --- | ---
lxml | 4.3.0 | XML parser for read_xml and tree builder for to_xml

#### SQL databases

Dependency | Minimum Version | Notes
--- | --- | ---
SQLAlchemy | 1.3.0 | SQL support for databases other than sqlite
psycopg2 | 2.7 | PostgreSQL engine for sqlalchemy
pymysql | 0.8.1 | MySQL engine for sqlalchemy

#### Other data sources

Dependency | Minimum Version | Notes
--- | --- | ---
PyTables | 3.5.1 | HDF5-based reading / writing
blosc | 1.17.0 | Compression for HDF5
zlib |  | Compression for HDF5
fastparquet | 0.4.0 | Parquet reading / writing
pyarrow | 0.17.0 | Parquet, ORC, and feather reading / writing
pyreadstat |  | 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 | 0.12.0 | 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