AWS Data Wrangler
gonum
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| AWS Data Wrangler | gonum | |
|---|---|---|
| 9 | 24 | |
| 3,797 | 7,249 | |
| 1.1% | 1.3% | |
| 9.4 | 8.2 | |
| about 17 hours ago | about 13 hours ago | |
| Python | Go | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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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
gonum
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How to set up interface to accept multi-dimension array?
But if you want to see what can be done for numeric stuff, check out gonum. Personally, I still wouldn't use Go, and I rather suspect it's still pretty easy to reach for something like what you're trying to do and not find it because Go just can't write that type sensibly, but you can at least see what is available, written by people who disagree with me about Go not being a great language for this.
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packages similar to Pandas
Numpy functionality is largely covered by https://www.gonum.org/ but for pandas I'm not sure if there is an equivalent as widely accepted. However, you might try https://github.com/rocketlaunchr/dataframe-go which I have not tried but it looks like it covers some of what you're looking for
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What libraries are missing?
Math libraries. It's just gonum right now. Missing things that often require people to link C or Python libs. E.g. https://github.com/gonum/gonum/issues/354
- Gonum Numerical Packages
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SIMD Accelerated vector math
Maybe this way you could avoid having Mul, Mul_Inplace, Mul_Into variants. Gonum mostly follows the same pattern.
- Modern hardware is fast, so let's choose the slowest language to balance it out
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graph: A generic Go library for creating graph data structures and performing operations on them. It supports different kinds of graphs such as directed graphs, acyclic graphs, or trees.
How does this compare to gonum graph? https://github.com/gonum/gonum/tree/master/graph
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From Python to NumPy
Go is quite a bit cleaner than Python and its concurrency/parallelism primitives can be well suited to scientific workloads.
You may want to have a look at Gonum (https://www.gonum.org), and the Go HEP package developed by CERN (https://go-hep.org).
I was also surprised to see DSP and pretty sophisticated packages, although I never used them: https://awesome-go.com/science-and-data-analysis
And of course Go has Jupyter integration, it's almost like running a script thanks to its fast compilation time.
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Go for science?
You should check out this https://github.com/gonum/gonum
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What makes concurrency in Go better than multiprocesing/multithreading in Python?
No, using CPU extensions and GPUs is a different thing than doing multitasking. There is Gonum but it is still slower than Numpy: https://github.com/gonum/gonum/issues/511
What are some alternatives?
PyAthena - PyAthena is a Python DB API 2.0 (PEP 249) client for Amazon Athena.
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
gosl - Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, differential equations.
ga-extractor - Tool for extracting Google Analytics data suitable for migrating to other platforms/databases
Stats - A well tested and comprehensive Golang statistics library package with no dependencies.
python-mysql-replication - Pure Python Implementation of MySQL replication protocol build on top of PyMYSQL
gonum/plot - A repository for plotting and visualizing data
Redash - Make Your Company Data Driven. Connect to any data source, easily visualize, dashboard and share your data.
PiHex - PiHex Library, written in Go, generates a hexadecimal number sequence in the number Pi in the range from 0 to 10,000,000.
zef - Toolkit for graph-relational data across space and time
goraph - Package goraph implements graph data structure and algorithms.