Apache Arrow VS Pandas

Apache Arrow Reviews

Pandas Reviews

Apache Arrow mentions (33)

• How moving from Pandas to Polars made me write better code without writing better code

  In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++. - Source: dev.to / 2 months ago

• Time Series Analysis with Polars

  One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on... - Source: dev.to / 5 months ago

• TXR Lisp

  IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/. - Source: Hacker News / 5 months ago

• A Polars exploration into Kedro

  Polars is an open-source library for Python, Rust, and NodeJS that provides in-memory dataframes, out-of-core processing capabilities, and more. It is based on the Rust implementation of the Apache Arrow columnar data format (you can read more about Arrow on my earlier blog post “Demystifying Apache Arrow”), and it is optimised to be blazing fast. - Source: dev.to / 12 months ago

• Demystifying Apache Arrow

  Apache Arrow (Arrow for short) is an open source project that defines itself as "a language-independent columnar memory format" (more on that later). It is part of the Apache Software Foundation, and as such is governed by a community of several stakeholders. It has implementations in several languages (C++ and also Rust, Julia, Go, and even JavaScript) and bindings for Python, R and others that wrap the C++... - Source: dev.to / 12 months ago

Pandas mentions (198)

• 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. - Source: dev.to / 14 days ago

• 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. - Source: dev.to / 7 days ago

• 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.... - Source: dev.to / 2 months ago

• 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. - Source: dev.to / 5 months ago

• 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. - Source: dev.to / 5 months ago