Introduction

tech.ml.dataset is a great and fast library which brings columnar dataset to the Clojure. Chris Nuernberger has been working on this library for last year as a part of bigger tech.ml stack.

I've started to test the library and help to fix uncovered bugs. My main goal was to compare functionalities with the other standards from other platforms. I focused on R solutions: dplyr, tidyr and data.table.

During conversions of the examples I've come up how to reorganized existing tech.ml.dataset functions into simple to use API. The main goals were:

• Focus on dataset manipulation functionality, leaving other parts of tech.ml like pipelines, datatypes, readers, ML, etc.
• Single entry point for common operations - one function dispatching on given arguments.
• group-by results with special kind of dataset - a dataset containing subsets created after grouping as a column.
• Most operations recognize regular dataset and grouped dataset and process data accordingly.
• One function form to enable thread-first on dataset.

Important! This library is not the replacement of tech.ml.dataset nor a separate library. It should be considered as a addition on the top of tech.ml.dataset.

If you want to know more about tech.ml.dataset and tech.ml.datatype please refer their documentation:

• Datatype
• Date/time
• Dataset

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Documentation

Please refer detailed documentation with examples

Usage example

(require '[tablecloth.api :as api])

(-> "https://raw.githubusercontent.com/techascent/tech.ml.dataset/master/test/data/stocks.csv"
    (api/dataset {:key-fn keyword})
    (api/group-by (fn [row]
                    {:symbol (:symbol row)
                     :year (tech.v2.datatype.datetime.operations/get-years (:date row))}))
    (api/aggregate #(tech.v2.datatype.functional/mean (% :price)))
    (api/order-by [:symbol :year])
    (api/head 10))

_unnamed [10 3]:

:symbol	:year	:summary
AAPL	2000	21.75
AAPL	2001	10.18
AAPL	2002	9.408
AAPL	2003	9.347
AAPL	2004	18.72
AAPL	2005	48.17
AAPL	2006	72.04
AAPL	2007	133.4
AAPL	2008	138.5
AAPL	2009	150.4

TODO

• tests
• more information about data manipulation

Licence

Copyright (c) 2020 Scicloj

The MIT Licence