Thank you Alexandre!  I have to admit it is a *ton* of work.  I think there
are lots of good pathways literally every direction such as simplifying the
numerics layer (tech.datatype), potentially getting a subset working on
graalvm-native, zero-copy conversion when possible for parquet and arrow
(totally possible in lots of cases), etc. etc; it just depends on what
seems like it provides the most value to everyone.

Plus learning just exactly how to use this system is a thing; it is complex
as are numpy, pandas, data.table ....  Bridging between Clojure and APL,
and C puts this in a unique position.

That being said, Thomaz has released tablecloth
<https://github.com/scicloj/tablecloth> which has a more advanced dataset
api based on the primitives in tech.ml.dataset with some great documentation
<https://scicloj.github.io/tablecloth/index.html>.

On Mon, Jun 15, 2020 at 1:48 PM Alexandre Almosni <
alexandre.almo...@gmail.com> wrote:

> Congratulations. This is really a great effort and something we really
> needed. I hope the community takes this as the base layer for data science
> and we can build on your efforts, expand the documentation, etc.
>
>
>
> On Monday, June 15, 2020 at 5:50:52 PM UTC+1, Chris Nuernberger wrote:
>>
>> Good morning Clojurians :-)
>>
>> It is with much pride that I announce version 2.0 of tech.ml.dataset
>> <https://github.com/techascent/tech.ml.dataset>, our library that maps
>> powerful concepts from libraries like Pandas and data.table into Clojure
>> using functional paradigms. This data frame
>> <https://github.com/mobileink/data.frame/wiki/What-is-a-Data-Frame%3F>
>> library has unified loading from csv, tsv, xlsx, xls, Apache parquet,
>> Apache arrow (.feather), sql, json and sequences of maps as well as
>> efficient cpu and memory
>> <https://gist.github.com/cnuernber/26b88ed259dd1d0dc6ac2aa138eecf37>
>> performance. Finally, because the dataset knows the datatype of each
>> column, you can interoperate with schema-ful things like SQL
>> <https://github.com/techascent/tech.ml.dataset.sql> without writing down
>> the schema.
>>
>>
>> user> (require '[tech.ml.dataset :as ds])
>> nil
>> user> (-> (ds/->dataset "https://vega.github.io/vega/data/stocks.csv";)
>>           
>> (ds/descriptive-stats))https://vega.github.io/vega/data/stocks.csv: 
>> descriptive-stats [3 10]:
>>
>> | :col-name |          :datatype | :n-valid | :n-missing |       :min |      
>> :mean | :mode |       :max | :standard-deviation | :skew |
>> |-----------|--------------------|----------|------------|------------|------------|-------|------------|---------------------|-------|
>> |      date | :packed-local-date |      560 |          0 | 2000-01-01 | 
>> 2005-05-12 |       | 2010-03-01 |                     |       |
>> |     price |           :float32 |      560 |          0 |      5.970 |      
>> 100.7 |       |      707.0 |               132.6 | 2.413 |
>> |    symbol |            :string |      560 |          0 |            |      
>>       |  MSFT |            |                     |       |
>>
>> Data science is (still) alive and well in Clojure and the JVM. Stepping
>> back and considering python bindings
>> <https://github.com/clj-python/libpython-clj>, R bindings
>> <https://github.com/scicloj/clojisr>, smile <https://haifengl.github.io/>,
>> the next-gen blas/numerics library Neanderthal
>> <https://github.com/uncomplicate/neanderthal> and the exceptionally
>> powerful saite science platform <https://github.com/jsa-aerial/saite>,
>> we have really come a long way in the last year!
>>
>> Thanks and enjoy :-)
>>
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