NumPy vs Panda: What are the differences?
Developers describe NumPy as "Fundamental package for scientific computing with Python". Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. On the other hand, Panda is detailed as "Dedicated video encoding in the cloud". Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.
NumPy belongs to "Data Science Tools" category of the tech stack, while Panda can be primarily classified under "Media Transcoding".
Some of the features offered by NumPy are:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
On the other hand, Panda provides the following key features:
- Unlimited encoding- When we say unlimited we mean unlimited. With your own dedicated resources, you can upload as much media as you like with no per-minute charge.
- Deliver everywhere- Encode your videos to be viewable in any browser, with any player, on any device.
- High definition- From the cellphone to the big screen, your video will always look gorgeous with 1080p HD video.
NumPy is an open source tool with 11.1K GitHub stars and 3.67K GitHub forks. Here's a link to NumPy's open source repository on GitHub.
What is NumPy?
What is Panda?
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What are the cons of using Panda?
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We utilize NumPy, SciPy, Pandas, and iPython Notebooks to power our analysis and analytics tools.