Pandas

• 1. Overview

• 2. Install

  • 2.1. Installing from PyPI

  • 2.2. Installing with Anaconda

  • 2.3. Installing with Miniconda

1. Overview

pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Name	URL
Website	https://pandas.pydata.org/
Documentation	https://pandas.pydata.org/docs
GitHub Group	https://github.com/pandas-dev

2. Install

https://pandas.pydata.org/docs/getting_started/install.html

2.1. Installing from PyPI

pip install pandas

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]"

All optional dependencies can be installed with pandas[all]:

pip install "pandas[all]"

The full list of extras that can be installed can be found in the dependency section.

Note:

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

pip install "pandas[performance]"

2.2. Installing with Anaconda

Anaconda: https://docs.continuum.io/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, macOS, Windows) Python distribution for data analytics and scientific computing.

2.3. Installing with Miniconda

Miniconda: https://docs.conda.io/en/latest/miniconda.html

Miniconda is a free minimal installer for conda.

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).