speed-comparison
Pandas
speed-comparison | Pandas | |
---|---|---|
9 | 399 | |
439 | 42,217 | |
- | 0.6% | |
4.6 | 10.0 | |
4 months ago | 6 days ago | |
Earthly | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
speed-comparison
- Douglas Crockford: “We should stop using JavaScript”
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How often do you guys actually use C?
For example, Java runs on the JVM (Java Virtual Machine) instead of running directly on the hardware, and it also has a garbage collector to handle memory management. Running on a virtual machine means your code is more abstracted: you only have to worry about the JVM and not about the platform you’re running on (since the JVM is the platform), and it’s more portable since your code can go on anything that runs the JVM. But running the JVM as an intermediate layer takes more computing power and so does running garbage collection, meaning that you experience a performance penalty. Here’s one benchmark I could find comparing the use of different programming languages to compute pi, in which Java took about 3x as long as C to complete the same task
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AITA for telling my 9 y/o daughter she sucked for not writing professional-level code?
Or you've got the speed comparisons (https://github.com/niklas-heer/speed-comparison) -- Python is probably something like 10% the speed of C/C++ (although, like I said, 99% of the time that's comparable to premature optimization).
- sou iniciante e com uma dúvida, python é realmente lento? ou é só meme?
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Why does Julia use jit?
Looks like a PR was merged yesterday to make the code more simd friendly https://github.com/niklas-heer/speed-comparison/pull/52
- speed comparison of various programming languages, Julia (AOT) is on fire!!!
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An Apple fan walks into a bar....
Sure, they could have chosen Python. But I doubt the language differences account for even a noticeable percentage of the slowness of Brew.
- There is framework for everything.
Pandas
- The Birth of Parquet
- PDEP-13: The Pandas Logical Type System
- PHP Doesn't Suck Anymore
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience.
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Pandas reset_index(): How To Reset Indexes in Pandas
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method.
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Deploying a Serverless Dash App with AWS SAM and Lambda
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.
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Help Us Build Our Roadmap – Pydantic
there is pull request to integrate in both pydantic extra types and into pandas cose [1]
[1]: https://github.com/pandas-dev/pandas/issues/53999
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Stuff I Learned during Hanukkah of Data 2023
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.
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Introducing Flama for Robust Machine Learning APIs
pandas: A library for data analysis in Python
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.
What are some alternatives?
arl - lists of most popular repositories for most favoured programming languages (according to StackOverflow)
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
OpenCV - Open Source Computer Vision Library
tensorflow - An Open Source Machine Learning Framework for Everyone
docx4j - JAXB-based Java library for Word docx, Powerpoint pptx, and Excel xlsx files
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
pivotnacci - A tool to make socks connections through HTTP agents
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Apache ZooKeeper - Apache ZooKeeper
Keras - Deep Learning for humans
NumPy - The fundamental package for scientific computing with Python.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration