Python

pip install cairocffi : Explore ways to install python packages with pip

pip install cairocffi is a command to install cairocffi python package. cairocffi  is a python library for handling 2d graphics. In this article, we will explore various aspects for installing cairocffi using pip.

pip install cairocffi : ( Installing ) –

pip install cairocffi official image

Once we run the above command it will install the latest version for cairocffi. If you want to downgrade the version for cairocffi package. Use the below command –

pip install cairocffi==version
pip cairocffi release version

Download the source code for cairocffi –

Please go through this link for downloading the source code for cairocffi

github for cairocffi

This is the latest library for this python package.

 

Similar package to cairocffi  ( Installation view ) –

Firstly, it is very common to use pip. But for beginners, who are new to python. We would to explain that

pip install {python_package_name}

is helpful to install any of the python packages. Let’s see with some examples.

pip install pubnub –

Secondly, this command is helpful to install pubnub python package. This pubnub python package provided multi-feature to implement real-time communication to applications using the python interface.

pip install pubnub

 

pip install pydicom  –

This command is again similar to the above. It will install the latest version for pydicom package. This pydicom package is useful in reading, writing, and creating dicom package. Please refer to the below screenshot.

pip install pydicom

Well, this “pip install” command is very simple and straightway to install any python package. Usually, most of the python package is uploaded in PypI community. But this pip package manager will install only the python packages which are available in to PyPI community storage hub. Since pip is the default package manager with the PyPI community.

If you want to read more about pip package manager and python packaging, please visit this article. Finally Hope you enjoyed this article.

Thanks 

Data Science Learner Team