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Cómo usar Pip en Python

Pip es un sistema de administración de paquetes que se utiliza para instalar y administrar paquetes de software, como los que se encuentran en el Índice de paquetes de Python.

¿Qué es Pip?


Pip is a replacement for easy_install. 

Packages installs the packages default under site-packages.

Instalación de Pip


To install Pip on your system, you can use either the source tarball or
by using easy_install.
>> $ easy_install pip

After that, the pip application is installed.

Uso de Pip


How to use Pip
Instalar un paquete
$ pip install simplejson
[... progress report ...]
Successfully installed simplejson
Actualizar un paquete
$ pip install --upgrade simplejson
[... progress report ...]
Successfully installed simplejson
Eliminar un paquete
$ pip uninstall simplejson
Uninstalling simplejson:
  /home/me/env/lib/python2.7/site-packages/simplejson
  /home/me/env/lib/python2.7/site-packages/simplejson-2.2.1-py2.7.egg-info
Proceed (y/n)? y
  Successfully uninstalled simplejson
Buscando un paquete
#Search PyPI for packages
$ pip search "query"
Comprobación del estado de un paquete
# To get info about an installed package, including its location and files:
pip show ProjectName

¿Por qué utilizar Pip en lugar de easy_install?


(The answer is taken from this post on stackoverflow)

All packages are downloaded before installation. 

Partially-completed installation doesn’t occur as a result.

Care is taken to present useful output on the console.

The reasons for actions are kept track of. 

For instance, if a package is being installed, pip keeps track of why that 
package was required.

Error messages should be useful.

The code is relatively concise and cohesive, making it easier to use 
programmatically.

Packages don’t have to be installed as egg archives, they can be installed flat.

Native support for other version control systems (Git, Mercurial and Bazaar)

Uninstallation of packages.

Simple to define fixed sets of requirements and reliably reproduce a set of 
packages.

Entrenamiento de Python recomendado

Para el entrenamiento de Python, nuestra principal recomendación es DataCamp.