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Data.table Alternatives
Similar projects and alternatives to data.table
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vaex
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
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rust-dataframe
Discontinued A Rust DataFrame implementation, built on Apache Arrow
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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TypedTables.jl
Simple, fast, column-based storage for data analysis in Julia
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ballista
Discontinued Distributed compute platform implemented in Rust, and powered by Apache Arrow.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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awesome-pandas-alternatives
Awesome list of alternative dataframe libraries in Python.
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datatable
A Python package for manipulating 2-dimensional tabular data structures
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Rfast
A collection of Rfast functions for data analysis. Note 1: The vast majority of the functions accept matrices only, not data.frames. Note 2: Do not have matrices or vectors with have missing data (i.e NAs). We do no check about them and C++ internally transforms them into zeros (0), so you may get wrong results. Note 3: In general, make sure you give the correct input, in order to get the correct output. We do no checks and this is one of the many reasons we are fast.
data.table reviews and mentions
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Alternative to Pandas
There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.
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Hiring an R coder to improve efficiency of code?
Some suggestions: (1) https://github.com/Rdatatable/data.table Code based on the data.table will probably be fastest. There are a number of reasons for this. More here: https://cran.r-project.org/web/packages/data.table/vignettes/ and here: https://rdatatable.gitlab.io/data.table/library/data.table/html/datatable-optimize.html The GForce set of optimizations is well explained here: https://www.brodieg.com/2019/02/24/a-strategy-for-faster-group-statisitics/ (2) setDTthreads() is your friend in data.table (3) I have found (on Windows at least) Microsoft Open R use of parallel MKL faster than CRAN's latest release. See https://mran.microsoft.com/documents/rro/multithread Microsoft recommends using setMKLthreads() if it will help. (4) I think rfast ( https://github.com/RfastOfficial/Rfast ) is a library worth considering although I don't know if it will help you with brms and stan operations.
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Piping in R is like baking!
Take a look at the 22nd new feature of v1.14.3 on development here.
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I wrote one of the fastest DataFrame libraries
data.table is basically a highly optimized C library
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How many of you who are employed as data Analysts/ Scientists use R vs Python vs other related software such as Power BI, Simio or Minitab?
Imo, the R syntax is way more readable and intuitive. Also R's ggplot is way more intuitive and easier to learn than remembering all the properties/methods with confusing names from matplotlib. Also dplyr works with data.table (the faster data processing library available for R, which is also incredibly faster than pandas).
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Stats
Rdatatable/data.table is an open source project licensed under Mozilla Public License 2.0 which is an OSI approved license.
The primary programming language of data.table is R.