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  1. Stackups
  2. Application & Data
  3. Media Processing
  4. Media Transcoding
  5. NumPy vs Panda

NumPy vs Panda

OverviewComparisonAlternatives

Overview

Panda
Panda
Stacks14
Followers28
Votes0
NumPy
NumPy
Stacks4.3K
Followers799
Votes15
GitHub Stars30.7K
Forks11.7K

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.

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Detailed Comparison

Panda
Panda
NumPy
NumPy

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.<br>

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.

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.;Broad format support- We support all of the most popular video and audio codecs including H.264, AAC, OGG, MP3, FlV, MP4 and many more;Web interface- Panda is easy for everyone with our innovative web interface that provides a straightforward process to upload, encode and monitor your content.;iPhone and iPad streaming- We support Apple HTTP Live Streaming (HLS), which dynamically adjusts the movie quality to match the speed of a connecting device.;Choose your region- Choose whether you want your video to be transferred and encoded in North America (USA) or in Europe (UK).;Supported Langyages: RUBY, PHP, PYTHON, OBJECTIVE-C, NODE.JS, MICROSOFT .NET<br>
Powerful n-dimensional arrays; Numerical computing tools; Interoperable; Performant; Easy to use
Statistics
GitHub Stars
-
GitHub Stars
30.7K
GitHub Forks
-
GitHub Forks
11.7K
Stacks
14
Stacks
4.3K
Followers
28
Followers
799
Votes
0
Votes
15
Pros & Cons
No community feedback yet
Pros
  • 10
    Great for data analysis
  • 4
    Faster than list
Integrations
Heroku
Heroku
Python
Python

What are some alternatives to Panda, NumPy?

Pandas

Pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Zencoder

Zencoder

Zencoder downloads the video and converts it to as many formats as you need. Every output is encoded concurrently, with virtually no waiting—whether you do one or one hundred. Zencoder then uploads the resulting videos to a server, CDN, an S3 bucket, or wherever you dictate in your API call.

PyXLL

PyXLL

Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!

Kurento

Kurento

It is a WebRTC media server and a set of client APIs making simple the development of advanced video applications for WWW and smartphone platforms. Media Server features include group communications, transcoding and more.

GStreamer

GStreamer

It is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing.

Cloudflare Stream

Cloudflare Stream

Cloudflare Stream makes integrating high-quality streaming video into a web or mobile application easy. Using a single, integrated workflow through a robust API or drag and drop UI, application owners can focus on creating the best video experience.

Bacon AI

Bacon AI

Create studio-quality images, videos, and UGC - in minutes

Vmake

Vmake

Is a video editor designed for talking head videos, making it easier to generate creative video editing ideas.

SciPy

SciPy

Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Dataform

Dataform

Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.

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