static-frame
vidgear
static-frame | vidgear | |
---|---|---|
8 | 14 | |
406 | 3,200 | |
1.0% | - | |
9.9 | 7.2 | |
1 day ago | 13 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
static-frame
- Static-frame: Immutable/statically-typed DataFrames with runtime type validation
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Type-Hinting DataFrames for Static Analysis and Runtime Validation
This is inadequate, as it ignores the types contained within the container. A DataFrame might have string column labels and three columns of integer, string, and floating-point values; these characteristics define the type. A function argument with such type hints provides developers, static analyzers, and runtime checkers with all the information needed to understand the expectations of the interface. StaticFrame 2 now permits this:
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Memoizing DataFrame Functions: Using Hashable DataFrames and Message Digests to Optimize Repeated Calculations
StaticFrame is an alternative DataFrame library that offers efficient solutions to this problem, both for in-memory and disk-based memoization.
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The Performance Advantage of No-Copy DataFrame Operations
A NumPy array is a Python object that stores data in a contiguous C-array buffer. The excellent performance of these arrays comes not only from this compact representation, but also from the ability of arrays to share "views" of that buffer among many arrays. NumPy makes frequent use of "no-copy" array operations, producing derived arrays without copying underling data buffers. By taking full advantage of NumPy's efficiency, the StaticFrame DataFrame library offers orders-of-magnitude better performance than Pandas for many common operations.
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Which not so well known Python packages do you like to use on a regular basis and why?
static-frame. An immutable alternative to pandas.
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One Fill Value Is Not Enough: Preserving Columnar Types When Reindexing DataFrames
StaticFrame is an immutable DataFrame library that offers solutions to such problems. In StaticFrame, alternative fill value representations can be used to preserve columnar types in reindexing, shifting, and many other operations that require fill_value arguments. For operations on heterogeneously typed columnar data, one fill value is simply not enough.
- static-frame: Immutable and grow-only Pandas-like DataFrames with a more explicit and consistent interface.
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Bug Sur 11.4 stuttering issues on RX 6800
For me, one example of high cpu usage is when i visit links like this one (https://github.com/InvestmentSystems/static-frame/blob/master/static_frame/performance/core.py) on GitHub. Safari is extremely laggy when i do nothing more than just scrolling around. Do you have sth like this?
vidgear
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Why HTTP/3 is eating the world
My experience that played out over the last few weeks lead me to a similar belief, somewhat. For rather uninteresting reasons I decided I wanted to create mp4 videos of an animation programmatically, from scratch.
The first solution suggested when googling around is to just create all the frames, save them to disk, and then let ffmpeg do its thing from there. I would have just gone with that for a one-off task, but it seems like a pretty bad solution if the video is long, or high res, or both. Plus, what I really wanted was to build something more "scalable/flexible".
Maybe I didn't know the right keywords to search for, but there really didn't seem to be many options for creating frames, piping them straight to an encoder, and writing just the final video file to disk. The only one I found that seemed like it could maybe do it the way I had in mind was VidGear[1] (Python). I had figured that with the popularity of streaming, and video in general on the web, there would be so much more tooling for these sorts of things.
I ended up digging way deeper into this than I had intended, and built myself something on top of Membrane[2] (Elixir)
[1] https://abhitronix.github.io/vidgear/
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Need help to choose toolchain for setting up a video streaming server on my PC.
I've been googling and reading for a while but I'm very unsure about which tools I need, which tools will help me achieve what I want the easiest way. What about (pylivestream)[https://pypi.org/project/pylivestream/] for example? Will this do the job for me? What about a lower level approach including (pyopencv)[https://pypi.org/project/opencv-python/]? What about a higher level approach using (vidgear)[https://github.com/abhiTronix/vidgear], which seems promising but I don't feel confident in assessing if it's the tool I really need?
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Which not so well known Python packages do you like to use on a regular basis and why?
Vidgear and new deffcode library are my best. I bet you don't know none of them. But they're pretty awesome when it comes to video-processing and stuff.
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Deffcode: FFmpeg decoding made easy with python.
Yes, fortunately I already resolved it in my previous(popular) library called vidgearthrough its WriteGear API: https://abhitronix.github.io/vidgear/latest/gears/writegear/compression/overview/
- VidGear Is a High-Performance Video Processing Python Library
- VidGear: Making Video-Processing with Python as easy as pie
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I created VidGear that makes Video-Processing with Python as easy as can be
Code: https://github.com/abhiTronix/vidgear
- VidGear 0.2.3: Video-Processing with Python as easy as can.
- VidGear – A High-Performance Video Processing Python Framework
What are some alternatives?
pandas-ta - Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators
moviepy - Video editing with Python
pandastable - Table analysis in Tkinter using pandas DataFrames.
scikit-video - Video processing routines for SciPy
python-lenses - A python lens library for manipulating deeply nested immutable structures
OpenCV - Open Source Computer Vision Library
bidict - The bidirectional mapping library for Python.
SaveTube - Youtube-dl GUI Wrapper
bambi - BAyesian Model-Building Interface (Bambi) in Python.
opencv-steel-darts - Automatic scoring system for steel darts using OpenCV, a Raspberry Pi 3 Model B and two webcams.
rubygems - Library packaging and distribution for Ruby.
ffmpeg-normalize - Audio Normalization for Python/ffmpeg