inflection
Pandas
inflection | Pandas | |
---|---|---|
2 | 397 | |
481 | 42,039 | |
- | 0.7% | |
2.5 | 10.0 | |
9 months ago | 3 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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inflection
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To Ruby from Python
> Could you elaborate on this
I think it's more than evaluating each feature in isolation like migrations, ORM, template engine, etc..
As much as I like Python (I use Flask a lot too besides Rails), I always found Rails to include more useful features for building web applications than Django. There's lots of examples but Rails' inflector is one of them. This happens all the time in web apps, which is wanting to output "1 person" or "2 people". Rails give you a template helper for this. Python has options in the form of third party tools like https://github.com/jpvanhal/inflection, but would you rather pull in a third party tool that hasn't been updated in 2+ years or use a solution maintained by a group of folks who are building web apps used by millions of people and then extracted those features into a framework?
The APIs in Rails feel more intuitive to me (super opinion based of course), but it's like someone tried 10 different variants in a few large web apps, tinkered with it for a while, arrived at a solution and that's the one that ships with Rails. There's so much thought put into everything and you know when it's released it's been put through the ringer at Basecamp, Hey, GitHub and Shopify because those sites all run off Rails master. That's a massive amount of confidence that it'll for you too, and the best part is you get to benefit from that on day 1 when a new stable release is shipped.
It's not that Django is bad or unstable but in my opinion if I were looking to use a batteries included framework I wouldn't look anywhere else besides Rails. It's just one of those things where it feels like a really good combination of things all came together (Ruby, Matz, DHH, Basecamp, lots of sites using it, enough community support to find blog posts for tons of stuff, great third party SDK support, etc.). You could say a number of languages have similar traits but they lack the first 4 things which are IMO the most important.
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PyHeck: I wrote a fast case conversion library with just 106 lines of Rust code
PyHeck is 5-10x faster than the established case conversion library, inflection.
Pandas
- 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.
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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.
What are some alternatives?
SpriteKit+Spring - SpriteKit API reproducing UIView's spring animations with SKAction
Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis
heck - oh heck, a case conversion library
tensorflow - An Open Source Machine Learning Framework for Everyone
pyheck - Python bindings for heck, the Rust case conversion library
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
R.swift - Strong typed, autocompleted resources like images, fonts and segues in Swift projects
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
unholy - a ruby-to-pyc compiler - _why mirror
Keras - Deep Learning for humans
Ruby on Rails - Ruby on Rails
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration