jython
Pandas
jython | Pandas | |
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5 | 395 | |
1,108 | 41,983 | |
2.9% | 0.6% | |
7.5 | 10.0 | |
9 days ago | 3 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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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.
jython
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how is python actually implemented?
Quick note - CPython is the OG and most popular implementation of Python, but it is not the only implementation of Python. Some other fairly well-known examples are Jython (Java implementation), RustPython (Rust implementation), or the more mind-bending PyPy (Python implemented via Python 🤯). Python is an interpreted language, and you can think of all these different Python implementations as being different implementations of the interpreter itself. The interpreter is the program that takes your Python code and executes it via a virtual machine. This differs from a compiled language (like C) which needs the high-level C code to first be converted to machine code and then executed by the CPU.
- Jython 2.7.3 Released
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How are Python and C related? I've read that Python is 'made from C'. Does that mean that Python is just an abstraction of lots of large C functions?
The program I linked is the Python interpreter you're probably using, but the abstract set of rules that make up Python are not in any particular way tied to C. You could also use a Java program or a C# program to interpret your Python code. They've even made Python in Python itself, although it uses a more restricted version of Python so it's easier to compile it.
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Your python 4 dream list.
Check their Github, last commit was 9 days ago.
- Python stands to lose its GIL, and gain a lot of speed
Pandas
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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.
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Mastering Pandas read_csv() with Examples - A Tutorial by Codes With Pankaj
Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.
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What Would Go in Your Dream Documentation Solution?
So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:
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How do people know when to use what programming language?
Weirdly most of my time spent with data analysis was in the C layers in pandas.
What are some alternatives?
graalpython - A Python 3 implementation built on GraalVM
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
Iron python - Implementation of the Python programming language for .NET Framework; built on top of the Dynamic Language Runtime (DLR).
tensorflow - An Open Source Machine Learning Framework for Everyone
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
pip_search - Searching thought pip when hard times strike
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
IronPython - Implementation of Python 3.x for .NET Framework that is built on top of the Dynamic Language Runtime.
Keras - Deep Learning for humans
wasmtime - A fast and secure runtime for WebAssembly
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration