AWS Data Wrangler
prose
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| AWS Data Wrangler | prose | |
|---|---|---|
| 9 | 1 | |
| 3,802 | 2,924 | |
| 1.3% | - | |
| 9.4 | 1.9 | |
| 2 days ago | almost 2 years ago | |
| Python | Go | |
| Apache License 2.0 | MIT 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.
AWS Data Wrangler
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Read files from s3 using Pandas/s3fs or AWS Data Wrangler?
I had no problem with awswrangler (https://github.com/aws/aws-sdk-pandas) and it supports reading and writing partitions which was really helpful and a few other optimizations that made it a great tool
- I agree that Arrow Tables are great, but we decided to keep the library focused on the Pandas interface. [wont implement]
- Automate some wrangling and data visualization in Python
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Redshift API vs. other ways to connect?
awslabs has developed their own package for this and given it's for their product, seem likely to maintain it. https://github.com/awslabs/aws-data-wrangler
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Parquet files
AWS data wrangler works well. it's a wrapper on pandas: https://github.com/awslabs/aws-data-wrangler
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Reading s3 file data with Python lambda function
you'll find pre-made zips here: https://github.com/awslabs/aws-data-wrangler/releases
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A guide to load (almost) anything into a DataFrame
Don't forget about https://aws-data-wrangler.readthedocs.io/
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Go+: Go designed for data science
Yep, agreed. Go is a great language for AWS Lambda type workflows.
Python isn't as great (Python Lambda Layers built on Macs don't always work). AWS Data Wrangler (https://github.com/awslabs/aws-data-wrangler) provides pre-built layers, which is a work around, but something that's as portable as Go would be the best solution.
- Best way to install pandas and bumpy to AWS Lanbda
prose
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Go+: Go designed for data science
Apart from Gonum[1] numerical libraries, I haven't found specific data science related Go libraries in my search for it for some hobby projects when compared to Python ecosystem.
Interestingly Prose[2] A Go library for text processing yielded better results for named-entity extraction when compared to NLTK in my tests in terms of accuracy and obviously performance.
Perhaps Go is not being applied enough in the Data Science/ML and for fields where it's applied (Network) Math in the standard library seems to be sufficient.
[1] https://github.com/gonum/gonum
[2] https://github.com/jdkato/prose
What are some alternatives?
PyAthena - PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.
gse - Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others.
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
go-i18n - Translate your Go program into multiple languages.
ga-extractor - Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
textcat - A Go package for n-gram based text categorization, with support for utf-8 and raw text
python-mysql-replication - Pure Python Implementation of MySQL replication protocol build on top of PyMYSQL
porter2 - High Performance Porter2 Stemmer
gonum - Gonum is a set of numeric libraries for the Go programming language. It contains libraries for matrices, statistics, optimization, and more
gojieba - "结巴"中文分词的Golang版本
zef - Toolkit for graph-relational data across space and time
go-mystem - CGo bindings to Yandex.Mystem