Dataset (data frame) manipulation API for the tech.ml.dataset library

tech-ml-version

“7.021”

tablecloth-version

“7.021”

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.

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

SOURCE CODE

Join the discussion on Zulip

Let’s require main namespace and define dataset used in most examples:

(require '[tablecloth.api :as tc]
         '[tech.v3.datatype.functional :as dfn])
(def DS (tc/dataset {:V1 (take 9 (cycle [1 2]))
                      :V2 (range 1 10)
                      :V3 (take 9 (cycle [0.5 1.0 1.5]))
                      :V4 (take 9 (cycle ["A" "B" "C"]))}))

DS

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Functionality

Dataset

Dataset is a special type which can be considered as a map of columns implemented around tech.ml.dataset library. Each column can be considered as named sequence of typed data. Supported types include integers, floats, string, boolean, date/time, objects etc.

Dataset creation

Dataset can be created from various of types of Clojure structures and files:

single values
sequence of maps
map of sequences or values
sequence of columns (taken from other dataset or created manually)
sequence of pairs: [string column-data] or [keyword column-data]
array of any arrays
file types: raw/gzipped csv/tsv, json, xls(x) taken from local file system or URL
input stream

tc/dataset accepts:

data
options (see documentation of tech.ml.dataset/->dataset function for full list):
- :dataset-name - name of the dataset
- :num-rows - number of rows to read from file
- :header-row? - indication if first row in file is a header
- :key-fn - function applied to column names (eg. keyword, to convert column names to keywords)
- :separator - column separator
- :single-value-column-name - name of the column when single value is provided
- :column-names - in case you want to name columns - only works for sequential input (arrays) or empty dataset
- :layout - for numerical, native array of arrays - treat entries :as-rows or :as-columns (default)

tc/let-dataset accepts bindings symbol-column-data to simulate R’s tibble function. Each binding is converted into a column. You can refer previous columns to in further bindings (as in let).

Empty dataset.

(tc/dataset)

_unnamed [0 0]

Empty dataset with column names

(tc/dataset nil {:column-names [:a :b]})

_unnamed [0 2]:

| :a | :b |
|----|----|

Sequence of pairs (first = column name, second = value(s)).

(tc/dataset [[:A 33] [:B 5] [:C :a]])

_unnamed [1 3]:

:A	:B	:C
33	5	:a

Not sequential values are repeated row-count number of times.

(tc/dataset [[:A [1 2 3 4 5 6]] [:B "X"] [:C :a]])

_unnamed [6 3]:

:A	:B	:C
1	X	:a
2	X	:a
3	X	:a
4	X	:a
5	X	:a
6	X	:a

Dataset created from map (keys = column names, vals = value(s)). Works the same as sequence of pairs.

(tc/dataset {:A 33})
(tc/dataset {:A [1 2 3]})
(tc/dataset {:A [3 4 5] :B "X"})

_unnamed [1 1]:

:A
33

_unnamed [3 1]:

:A
1
2
3

_unnamed [3 2]:

:A	:B
3	X
4	X
5	X

You can put any value inside a column

(tc/dataset {:A [[3 4 5] [:a :b]] :B "X"})

_unnamed [2 2]:

:A	:B
[3 4 5]	X
[:a :b]	X

Sequence of maps

(tc/dataset [{:a 1 :b 3} {:b 2 :a 99}])
(tc/dataset [{:a 1 :b [1 2 3]} {:a 2 :b [3 4]}])

_unnamed [2 2]:

:a	:b
1	3
99	2

_unnamed [2 2]:

:a	:b
1	[1 2 3]
2	[3 4]

Missing values are marked by nil

(tc/dataset [{:a nil :b 1} {:a 3 :b 4} {:a 11}])

_unnamed [3 2]:

:a	:b
	1
3	4
11

Reading from arrays, by default :as-rows

(-> (map int-array [[1 2] [3 4] [5 6]])
    (into-array)
    (tc/dataset))

:_unnamed [3 2]:

0	1
1	2
3	4
5	6

:as-columns

(-> (map int-array [[1 2] [3 4] [5 6]])
    (into-array)
    (tc/dataset {:layout :as-columns}))

:_unnamed [2 3]:

0	1	2
1	3	5
2	4	6

:as-rows with names

(-> (map int-array [[1 2] [3 4] [5 6]])
    (into-array)
    (tc/dataset {:layout :as-rows
                 :column-names [:a :b]}))

:_unnamed [3 2]:

:a	:b
1	2
3	4
5	6

Any objects

(-> (map to-array [[:a :z] ["ee" "ww"] [9 10]])
    (into-array)
    (tc/dataset {:column-names [:a :b :c]
                 :layout :as-columns}))

:_unnamed [2 3]:

:a	:b	:c
:a	ee	9
:z	ww	10

Create dataset using macro let-dataset to simulate R tibble function. Each binding is converted into a column.

(tc/let-dataset [x (range 1 6)
                  y 1
                  z (dfn/+ x y)])

_unnamed [5 3]:

:x	:y	:z
1	1	2
2	1	3
3	1	4
4	1	5
5	1	6

Import CSV file

(tc/dataset "data/family.csv")

data/family.csv [5 5]:

family	dob_child1	dob_child2	gender_child1	gender_child2
1	1998-11-26	2000-01-29	1	2
2	1996-06-22		2
3	2002-07-11	2004-04-05	2	2
4	2004-10-10	2009-08-27	1	1
5	2000-12-05	2005-02-28	2	1

Import from URL

(defonce ds (tc/dataset "https://vega.github.io/vega-lite/examples/data/seattle-weather.csv"))

ds

https://vega.github.io/vega-lite/examples/data/seattle-weather.csv [1461 6]:

date	precipitation	temp_max	temp_min	wind	weather
2012-01-01	0.0	12.8	5.0	4.7	drizzle
2012-01-02	10.9	10.6	2.8	4.5	rain
2012-01-03	0.8	11.7	7.2	2.3	rain
2012-01-04	20.3	12.2	5.6	4.7	rain
2012-01-05	1.3	8.9	2.8	6.1	rain
2012-01-06	2.5	4.4	2.2	2.2	rain
2012-01-07	0.0	7.2	2.8	2.3	rain
2012-01-08	0.0	10.0	2.8	2.0	sun
2012-01-09	4.3	9.4	5.0	3.4	rain
2012-01-10	1.0	6.1	0.6	3.4	rain
…	…	…	…	…	…
2015-12-21	27.4	5.6	2.8	4.3	rain
2015-12-22	4.6	7.8	2.8	5.0	rain
2015-12-23	6.1	5.0	2.8	7.6	rain
2015-12-24	2.5	5.6	2.2	4.3	rain
2015-12-25	5.8	5.0	2.2	1.5	rain
2015-12-26	0.0	4.4	0.0	2.5	sun
2015-12-27	8.6	4.4	1.7	2.9	rain
2015-12-28	1.5	5.0	1.7	1.3	rain
2015-12-29	0.0	7.2	0.6	2.6	fog
2015-12-30	0.0	5.6	-1.0	3.4	sun
2015-12-31	0.0	5.6	-2.1	3.5	sun

When none of above works, singleton dataset is created. Along with the error message from the exception thrown by tech.ml.dataset

(tc/dataset 999)

_unnamed [1 2]:

:$value \| :$error
999	Don’t know how to create ISeq from: java.lang.Long

To see the stack trace, turn it on by setting :stack-trace? to true.

Set column name for single value. Also set the dataset name and turn off creating error message column.

(tc/dataset 999 {:single-value-column-name "my-single-value"
                 :error-column? false})
(tc/dataset 999 {:single-value-column-name ""
                 :dataset-name "Single value"
                 :error-column? false})

_unnamed [1 1]:

my-single-value
999

Single value [1 1]:

0
999

Saving

Export dataset to a file or output stream can be done by calling tc/write!. Function accepts:

dataset
file name with one of the extensions: .csv, .tsv, .csv.gz and .tsv.gz or output stream
options:
:separator - string or separator char.

(tc/write! ds "output.tsv.gz")
(.exists (clojure.java.io/file "output.tsv.gz"))

1462
true

Nippy

(tc/write! DS "output.nippy.gz")

nil

(tc/dataset "output.nippy.gz")

output.nippy.gz [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Dataset related functions

Summary functions about the dataset like number of rows, columns and basic stats.

Number of rows

(tc/row-count ds)

Number of columns

(tc/column-count ds)

Shape of the dataset, [row count, column count]

(tc/shape ds)

[1461 6]

General info about dataset. There are three variants:

default - containing information about columns with basic statistics
:basic - just name, row and column count and information if dataset is a result of group-by operation
:columns - columns’ metadata

(tc/info ds)
(tc/info ds :basic)
(tc/info ds :columns)

https://vega.github.io/vega-lite/examples/data/seattle-weather.csv: descriptive-stats [6 12]:

:col-name	:datatype	:n-valid	:min	:mean	:mode	:max	:standard-deviation	:skew	:first	:last
date	:packed-local-date	1461	2012-01-01	2013-12-31		2015-12-31	3.64520463E+10	1.98971418E-17	2012-01-01	2015-12-31
precipitation	:float64	1461	0.000	3.029		55.90	6.68019432E+00	3.50564372E+00	0.000	0.000
temp_max	:float64	1461	-1.600	16.44		35.60	7.34975810E+00	2.80929992E-01	12.80	5.600
temp_min	:float64	1461	-7.100	8.235		18.30	5.02300418E+00	-2.49458552E-01	5.000	-2.100
wind	:float64	1461	0.4000	3.241		9.500	1.43782506E+00	8.91667519E-01	4.700	3.500
weather	:string	1461			rain				drizzle	sun

https://vega.github.io/vega-lite/examples/data/seattle-weather.csv :basic info [1 4]:

:name	:grouped?	:rows	:columns
https://vega.github.io/vega-lite/examples/data/seattle-weather.csv	false	1461	6

https://vega.github.io/vega-lite/examples/data/seattle-weather.csv :column info [6 4]:

:name	:datatype	:n-elems	:categorical?
date	:packed-local-date	1461
precipitation	:float64	1461
temp_max	:float64	1461
temp_min	:float64	1461
wind	:float64	1461
weather	:string	1461	true

Getting a dataset name

(tc/dataset-name ds)

"https://vega.github.io/vega-lite/examples/data/seattle-weather.csv"

Setting a dataset name (operation is immutable).

(->> "seattle-weather"
     (tc/set-dataset-name ds)
     (tc/dataset-name))

"seattle-weather"

Columns and rows

Get columns and rows as sequences. column, columns and rows treat grouped dataset as regular one. See Groups to read more about grouped datasets.

Possible result types:

:as-seq or :as-seqs - sequence of seqences (default)
:as-maps - sequence of maps (rows)
:as-map - map of sequences (columns)
:as-double-arrays - array of double arrays
:as-vecs - sequence of vectors (rows)

For rows setting :nil-missing? option to false will elide keys for nil values.

Select column.

(ds "wind")
(tc/column ds "date")

#tech.v3.dataset.column<float64>[1461]
wind
[4.700, 4.500, 2.300, 4.700, 6.100, 2.200, 2.300, 2.000, 3.400, 3.400, 5.100, 1.900, 1.300, 5.300, 3.200, 5.000, 5.600, 5.000, 1.600, 2.300...]
#tech.v3.dataset.column<packed-local-date>[1461]
date
[2012-01-01, 2012-01-02, 2012-01-03, 2012-01-04, 2012-01-05, 2012-01-06, 2012-01-07, 2012-01-08, 2012-01-09, 2012-01-10, 2012-01-11, 2012-01-12, 2012-01-13, 2012-01-14, 2012-01-15, 2012-01-16, 2012-01-17, 2012-01-18, 2012-01-19, 2012-01-20...]

Columns as sequence

(take 2 (tc/columns ds))

(#tech.v3.dataset.column<packed-local-date>[1461]
date
[2012-01-01, 2012-01-02, 2012-01-03, 2012-01-04, 2012-01-05, 2012-01-06, 2012-01-07, 2012-01-08, 2012-01-09, 2012-01-10, 2012-01-11, 2012-01-12, 2012-01-13, 2012-01-14, 2012-01-15, 2012-01-16, 2012-01-17, 2012-01-18, 2012-01-19, 2012-01-20...] #tech.v3.dataset.column<float64>[1461]
precipitation
[0.000, 10.90, 0.8000, 20.30, 1.300, 2.500, 0.000, 0.000, 4.300, 1.000, 0.000, 0.000, 0.000, 4.100, 5.300, 2.500, 8.100, 19.80, 15.20, 13.50...])

Columns as map

(keys (tc/columns ds :as-map))

("date" "precipitation" "temp_max" "temp_min" "wind" "weather")

Rows as sequence of sequences

(take 2 (tc/rows ds))

([#object[java.time.LocalDate 0x315e8f87 "2012-01-01"] 0.0 12.8 5.0 4.7 "drizzle"] [#object[java.time.LocalDate 0x6d412a98 "2012-01-02"] 10.9 10.6 2.8 4.5 "rain"])

Select rows/columns as double-double-array

(-> ds
    (tc/select-columns :type/numerical)
    (tc/head)
    (tc/rows :as-double-arrays))

#object["[[D" 0x5e90b0e0 "[[D@5e90b0e0"]

(-> ds
    (tc/select-columns :type/numerical)
    (tc/head)
    (tc/columns :as-double-arrays))

#object["[[D" 0x4ff448d7 "[[D@4ff448d7"]

Rows as sequence of maps

(clojure.pprint/pprint (take 2 (tc/rows ds :as-maps)))

({"date" #object[java.time.LocalDate 0x70dfbfc0 "2012-01-01"],
  "precipitation" 0.0,
  "temp_max" 12.8,
  "temp_min" 5.0,
  "wind" 4.7,
  "weather" "drizzle"}
 {"date" #object[java.time.LocalDate 0x59f8523b "2012-01-02"],
  "precipitation" 10.9,
  "temp_max" 10.6,
  "temp_min" 2.8,
  "wind" 4.5,
  "weather" "rain"})

Rows with missing values

(-> {:a [1 nil 2]
     :b [3 4 nil]}
    (tc/dataset)
    (tc/rows :as-maps))

[{:a 1, :b 3} {:a nil, :b 4} {:a 2, :b nil}]

Rows with elided missing values

(-> {:a [1 nil 2]
     :b [3 4 nil]}
    (tc/dataset)
    (tc/rows :as-maps {:nil-missing? false}))

[{:a 1, :b 3} {:b 4} {:a 2}]

Single entry

Get single value from the table using get-in from Clojure API or get-entry. First argument is column name, second is row number.

(get-in ds ["wind" 2])

2.3

(tc/get-entry ds "wind" 2)

2.3

Printing

Dataset is printed using dataset->str or print-dataset functions. Options are the same as in tech.ml.dataset/dataset-data->str. Most important is :print-line-policy which can be one of the: :single, :repl or :markdown.

(tc/print-dataset (tc/group-by DS :V1) {:print-line-policy :markdown})

_unnamed [2 3]:

| :name | :group-id |                                                                                                                                                                                                                                                             :data |
|------:|----------:|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|     1 |         0 | Group: 1 [5 4]:<br><br>\| :V1 \| :V2 \| :V3 \| :V4 \|<br>\|----:\|----:\|----:\|-----\|<br>\|   1 \|   1 \| 0.5 \|   A \|<br>\|   1 \|   3 \| 1.5 \|   C \|<br>\|   1 \|   5 \| 1.0 \|   B \|<br>\|   1 \|   7 \| 0.5 \|   A \|<br>\|   1 \|   9 \| 1.5 \|   C \| |
|     2 |         1 |                                   Group: 2 [4 4]:<br><br>\| :V1 \| :V2 \| :V3 \| :V4 \|<br>\|----:\|----:\|----:\|-----\|<br>\|   2 \|   2 \| 1.0 \|   B \|<br>\|   2 \|   4 \| 0.5 \|   A \|<br>\|   2 \|   6 \| 1.5 \|   C \|<br>\|   2 \|   8 \| 1.0 \|   B \| |

(tc/print-dataset (tc/group-by DS :V1) {:print-line-policy :repl})

_unnamed [2 3]:

| :name | :group-id |                          :data |
|------:|----------:|--------------------------------|
|     1 |         0 | Group: 1 [5 4]:                |
|       |           |                                |
|       |           | \| :V1 \| :V2 \| :V3 \| :V4 \| |
|       |           | \|----:\|----:\|----:\|-----\| |
|       |           | \|   1 \|   1 \| 0.5 \|   A \| |
|       |           | \|   1 \|   3 \| 1.5 \|   C \| |
|       |           | \|   1 \|   5 \| 1.0 \|   B \| |
|       |           | \|   1 \|   7 \| 0.5 \|   A \| |
|       |           | \|   1 \|   9 \| 1.5 \|   C \| |
|     2 |         1 | Group: 2 [4 4]:                |
|       |           |                                |
|       |           | \| :V1 \| :V2 \| :V3 \| :V4 \| |
|       |           | \|----:\|----:\|----:\|-----\| |
|       |           | \|   2 \|   2 \| 1.0 \|   B \| |
|       |           | \|   2 \|   4 \| 0.5 \|   A \| |
|       |           | \|   2 \|   6 \| 1.5 \|   C \| |
|       |           | \|   2 \|   8 \| 1.0 \|   B \| |

(tc/print-dataset (tc/group-by DS :V1) {:print-line-policy :single})

_unnamed [2 3]:

| :name | :group-id |           :data |
|------:|----------:|-----------------|
|     1 |         0 | Group: 1 [5 4]: |
|     2 |         1 | Group: 2 [4 4]: |

Group-by

Grouping by is an operation which splits dataset into subdatasets and packs it into new special type of… dataset. I distinguish two types of dataset: regular dataset and grouped dataset. The latter is the result of grouping.

Operations that perform a transformation on a regular dataset, generally apply that same transformation to individual sub-datasets in a grouped dataset. For example,

(tc/select-rows DS [0 1 2])

_unnamed [3 4]:

| :V1 | :V2 | :V3 | :V4 |
|----:|----:|----:|-----|
|   1 |   1 | 0.5 |   A |
|   2 |   2 | 1.0 |   B |
|   1 |   3 | 1.5 |   C |

returns a dataset containing only the first three rows of DS, while

(-> DS
    (tc/group-by :V1)
    (tc/select-rows [0 1 2]))

_unnamed [2 3]:

| :name | :group-id |           :data |
|------:|----------:|-----------------|
|     1 |         0 | Group: 1 [3 4]: |
|     2 |         1 | Group: 2 [3 4]: |

returns a grouped dataset, in which each sub-dataset contains only the first three rows of the sub-datasets in the grouped dataset created by (tc/group-by DS :V1).

Almost all functions recognize type of the dataset (grouped or not) and operate accordingly.

However, you can’t apply reshaping or join/concat functions on grouped datasets.

Grouped dataset is annotated by the :grouped? meta tag and consists of the following columns:

:name - group name or structure
:group-id - integer assigned to the group
:data - groups as datasets

Grouping

Grouping is done by calling group-by function with arguments:

ds - dataset
grouping-selector - what to use for grouping
options:
:result-type - what to return:
:as-dataset (default) - return grouped dataset
:as-indexes - return rows ids (row number from original dataset)
:as-map - return map with group names as keys and subdataset as values
:as-seq - return sequens of subdatasets
:select-keys - list of the columns passed to a grouping selector function

All subdatasets (groups) have set name as the group name, additionally group-id is in meta.

Grouping can be done by:

single column name
seq of column names
value returned by function taking row as map (limited to :select-keys)
map of keys (arbitrary group names) to sequences of row indexes

In the case of the first three of these methods, each sub-dataset contains all and only rows from the original data set that share the same grouping value:

the value of the row in a specified single column
a map from column names to corresponding values found in the row
the value returned by the function taking row as map

In the case of the map from group names to sequences of indexes, each sub-dataset will contain all and only rows with the indexes listed in the sequence for a given group name (a key).

Note: currently dataset inside dataset is printed recursively so it renders poorly from markdown. So I will use :as-seq result type to show just group names and groups.

List of columns in grouped dataset

(-> DS
    (tc/group-by :V1)
    (tc/column-names))

(:V1 :V2 :V3 :V4)

List of columns in grouped dataset treated as regular dataset

(-> DS
    (tc/group-by :V1)
    (tc/as-regular-dataset)
    (tc/column-names))

(:name :group-id :data)

Content of the grouped dataset

(tc/columns (tc/group-by DS :V1) :as-map)

{:name #tech.v3.dataset.column<int64>[2]
:name
[1, 2], :group-id #tech.v3.dataset.column<int64>[2]
:group-id
[0, 1], :data #tech.v3.dataset.column<dataset>[2]
:data
[Group: 1 [5 4]:

| :V1 | :V2 | :V3 | :V4 |
|----:|----:|----:|-----|
|   1 |   1 | 0.5 |   A |
|   1 |   3 | 1.5 |   C |
|   1 |   5 | 1.0 |   B |
|   1 |   7 | 0.5 |   A |
|   1 |   9 | 1.5 |   C |
, Group: 2 [4 4]:

| :V1 | :V2 | :V3 | :V4 |
|----:|----:|----:|-----|
|   2 |   2 | 1.0 |   B |
|   2 |   4 | 0.5 |   A |
|   2 |   6 | 1.5 |   C |
|   2 |   8 | 1.0 |   B |
]}

Grouped dataset as map

(keys (tc/group-by DS :V1 {:result-type :as-map}))

(1 2)

(vals (tc/group-by DS :V1 {:result-type :as-map}))

(Group: 1 [5 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	3	1.5	C
1	5	1.0	B
1	7	0.5	A
1	9	1.5	C

Group: 2 [4 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	4	0.5	A
2	6	1.5	C
2	8	1.0	B

)

Group dataset as map of indexes (row ids)

(tc/group-by DS :V1 {:result-type :as-indexes})

{1 [0 2 4 6 8], 2 [1 3 5 7]}

Grouped datasets are printed as follows by default.

(tc/group-by DS :V1)

_unnamed [2 3]:

:name	:group-id	:data
1	0	Group: 1 [5 4]:
2	1	Group: 2 [4 4]:

To get groups as sequence or a map can be done from grouped dataset using groups->seq and groups->map functions.

Groups as seq can be obtained by just accessing :data column.

I will use temporary dataset here.

(let [ds (-> {"a" [1 1 2 2]
              "b" ["a" "b" "c" "d"]}
             (tc/dataset)
             (tc/group-by "a"))]
  (seq (ds :data))) ;; seq is not necessary but Markdown treats `:data` as command here

(Group: 1 [2 2]:

a	b
1	a
1	b

Group: 2 [2 2]:

a	b
2	c
2	d

)

(-> {"a" [1 1 2 2]
     "b" ["a" "b" "c" "d"]}
    (tc/dataset)
    (tc/group-by "a")
    (tc/groups->seq))

(Group: 1 [2 2]:

a	b
1	a
1	b

Group: 2 [2 2]:

a	b
2	c
2	d

)

Groups as map

(-> {"a" [1 1 2 2]
     "b" ["a" "b" "c" "d"]}
    (tc/dataset)
    (tc/group-by "a")
    (tc/groups->map))

{1 Group: 1 [2 2]:

a	b
1	a
1	b

, 2 Group: 2 [2 2]:

a	b
2	c
2	d

}

Grouping by more than one column. You can see that group names are maps. When ungrouping is done these maps are used to restore column names.

(tc/group-by DS [:V1 :V3] {:result-type :as-seq})

(Group: {:V1 1, :V3 0.5} [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	7	0.5	A

Group: {:V1 2, :V3 1.0} [2 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	8	1.0	B

Group: {:V1 1, :V3 1.5} [2 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
1	9	1.5	C

Group: {:V1 2, :V3 0.5} [1 4]:

:V1	:V2	:V3	:V4
2	4	0.5	A

Group: {:V1 1, :V3 1.0} [1 4]:

:V1	:V2	:V3	:V4
1	5	1.0	B

Group: {:V1 2, :V3 1.5} [1 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C

)

Grouping can be done by providing just row indexes. This way you can assign the same row to more than one group.

(tc/group-by DS {"group-a" [1 2 1 2]
                  "group-b" [5 5 5 1]} {:result-type :as-seq})

(Group: group-a [4 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
1	3	1.5	C
2	2	1.0	B
1	3	1.5	C

Group: group-b [4 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C
2	6	1.5	C
2	6	1.5	C
2	2	1.0	B

)

You can group by a result of grouping function which gets row as map and should return group name. When map is used as a group name, ungrouping restore original column names.

(tc/group-by DS (fn [row] (* (:V1 row)
                             (:V3 row))) {:result-type :as-seq})

(Group: 0.5 [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	7	0.5	A

Group: 2.0 [2 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	8	1.0	B

Group: 1.5 [2 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
1	9	1.5	C

Group: 1.0 [2 4]:

:V1	:V2	:V3	:V4
2	4	0.5	A
1	5	1.0	B

Group: 3.0 [1 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C

)

You can use any predicate on column to split dataset into two groups.

(tc/group-by DS (comp #(< % 1.0) :V3) {:result-type :as-seq})

(Group: true [3 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
1	7	0.5	A

Group: false [6 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
1	3	1.5	C
1	5	1.0	B
2	6	1.5	C
2	8	1.0	B
1	9	1.5	C

)

juxt is also helpful

(tc/group-by DS (juxt :V1 :V3) {:result-type :as-seq})

(Group: [1 0.5] [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	7	0.5	A

Group: [2 1.0] [2 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	8	1.0	B

Group: [1 1.5] [2 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
1	9	1.5	C

Group: [2 0.5] [1 4]:

:V1	:V2	:V3	:V4
2	4	0.5	A

Group: [1 1.0] [1 4]:

:V1	:V2	:V3	:V4
1	5	1.0	B

Group: [2 1.5] [1 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C

)

tech.ml.dataset provides an option to limit columns which are passed to grouping functions. It’s done for performance purposes.

(tc/group-by DS identity {:result-type :as-seq
                           :select-keys [:V1]})

(Group: {:V1 1} [5 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	3	1.5	C
1	5	1.0	B
1	7	0.5	A
1	9	1.5	C

Group: {:V1 2} [4 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	4	0.5	A
2	6	1.5	C
2	8	1.0	B

)

Ungrouping

Ungrouping simply concats all the groups into the dataset. Following options are possible

:order? - order groups according to the group name ascending order. Default: false
:add-group-as-column - should group name become a column? If yes column is created with provided name (or :$group-name if argument is true). Default: nil.
:add-group-id-as-column - should group id become a column? If yes column is created with provided name (or :$group-id if argument is true). Default: nil.
:dataset-name - to name resulting dataset. Default: nil (_unnamed)

If group name is a map, it will be splitted into separate columns. Be sure that groups (subdatasets) doesn’t contain the same columns already.

If group name is a vector, it will be splitted into separate columns. If you want to name them, set vector of target column names as :add-group-as-column argument.

After ungrouping, order of the rows is kept within the groups but groups are ordered according to the internal storage.

Grouping and ungrouping.

(-> DS
    (tc/group-by :V3)
    (tc/ungroup))

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
1	7	0.5	A
2	2	1.0	B
1	5	1.0	B
2	8	1.0	B
1	3	1.5	C
2	6	1.5	C
1	9	1.5	C

Groups sorted by group name and named.

(-> DS
    (tc/group-by :V3)
    (tc/ungroup {:order? true
                  :dataset-name "Ordered by V3"}))

Ordered by V3 [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
1	7	0.5	A
2	2	1.0	B
1	5	1.0	B
2	8	1.0	B
1	3	1.5	C
2	6	1.5	C
1	9	1.5	C

Groups sorted descending by group name and named.

(-> DS
    (tc/group-by :V3)
    (tc/ungroup {:order? :desc
                  :dataset-name "Ordered by V3 descending"}))

Ordered by V3 descending [9 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
2	6	1.5	C
1	9	1.5	C
2	2	1.0	B
1	5	1.0	B
2	8	1.0	B
1	1	0.5	A
2	4	0.5	A
1	7	0.5	A

Let’s add group name and id as additional columns

(-> DS
    (tc/group-by (comp #(< % 4) :V2))
    (tc/ungroup {:add-group-as-column true
                  :add-group-id-as-column true}))

_unnamed [9 6]:

:$group-name \| :$group-id	:V1	:V2	:V3	:V4
true	0	1	1	0.5	A
true	0	2	2	1.0	B
true	0	1	3	1.5	C
false	1	2	4	0.5	A
false	1	1	5	1.0	B
false	1	2	6	1.5	C
false	1	1	7	0.5	A
false	1	2	8	1.0	B
false	1	1	9	1.5	C

Let’s assign different column names

(-> DS
    (tc/group-by (comp #(< % 4) :V2))
    (tc/ungroup {:add-group-as-column "Is V2 less than 4?"
                  :add-group-id-as-column "group id"}))

_unnamed [9 6]:

Is V2 less than 4?	group id	:V1	:V2	:V3	:V4
true	0	1	1	0.5	A
true	0	2	2	1.0	B
true	0	1	3	1.5	C
false	1	2	4	0.5	A
false	1	1	5	1.0	B
false	1	2	6	1.5	C
false	1	1	7	0.5	A
false	1	2	8	1.0	B
false	1	1	9	1.5	C

If we group by map, we can automatically create new columns out of group names.

(-> DS
    (tc/group-by (fn [row] {"V1 and V3 multiplied" (* (:V1 row)
                                                      (:V3 row))
                            "V4 as lowercase" (clojure.string/lower-case (:V4 row))}))
    (tc/ungroup {:add-group-as-column true}))

_unnamed [9 6]:

V1 and V3 multiplied	V4 as lowercase	:V1	:V2	:V3	:V4
0.5	a	1	1	0.5	A
0.5	a	1	7	0.5	A
2.0	b	2	2	1.0	B
2.0	b	2	8	1.0	B
1.5	c	1	3	1.5	C
1.5	c	1	9	1.5	C
1.0	a	2	4	0.5	A
1.0	b	1	5	1.0	B
3.0	c	2	6	1.5	C

We can add group names without separation

(-> DS
    (tc/group-by (fn [row] {"V1 and V3 multiplied" (* (:V1 row)
                                                      (:V3 row))
                            "V4 as lowercase" (clojure.string/lower-case (:V4 row))}))
    (tc/ungroup {:add-group-as-column "just map"
                  :separate? false}))

_unnamed [9 5]:

just map	:V1	:V2	:V3	:V4
{“V1 and V3 multiplied” 0.5, “V4 as lowercase” “a”}	1	1	0.5	A
{“V1 and V3 multiplied” 0.5, “V4 as lowercase” “a”}	1	7	0.5	A
{“V1 and V3 multiplied” 2.0, “V4 as lowercase” “b”}	2	2	1.0	B
{“V1 and V3 multiplied” 2.0, “V4 as lowercase” “b”}	2	8	1.0	B
{“V1 and V3 multiplied” 1.5, “V4 as lowercase” “c”}	1	3	1.5	C
{“V1 and V3 multiplied” 1.5, “V4 as lowercase” “c”}	1	9	1.5	C
{“V1 and V3 multiplied” 1.0, “V4 as lowercase” “a”}	2	4	0.5	A
{“V1 and V3 multiplied” 1.0, “V4 as lowercase” “b”}	1	5	1.0	B
{“V1 and V3 multiplied” 3.0, “V4 as lowercase” “c”}	2	6	1.5	C

The same applies to group names as sequences

(-> DS
    (tc/group-by (juxt :V1 :V3))
    (tc/ungroup {:add-group-as-column "abc"}))

_unnamed [9 6]:

:abc-0	:abc-1	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

Let’s provide column names

(-> DS
    (tc/group-by (juxt :V1 :V3))
    (tc/ungroup {:add-group-as-column ["v1" "v3"]}))

_unnamed [9 6]:

v1	v3	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

Also we can supress separation

(-> DS
    (tc/group-by (juxt :V1 :V3))
    (tc/ungroup {:separate? false
                  :add-group-as-column true}))
;; => _unnamed [9 5]:

_unnamed [9 5]:

:$group-name	:V1	:V2	:V3	:V4
[1 0.5]	1	1	0.5	A
[1 0.5]	1	7	0.5	A
[2 1.0]	2	2	1.0	B
[2 1.0]	2	8	1.0	B
[1 1.5]	1	3	1.5	C
[1 1.5]	1	9	1.5	C
[2 0.5]	2	4	0.5	A
[1 1.0]	1	5	1.0	B
[2 1.5]	2	6	1.5	C

Other functions

To check if dataset is grouped or not just use grouped? function.

(tc/grouped? DS)

nil

(tc/grouped? (tc/group-by DS :V1))

true

If you want to remove grouping annotation (to make all the functions work as with regular dataset) you can use unmark-group or as-regular-dataset (alias) functions.

It can be important when you want to remove some groups (rows) from grouped dataset using drop-rows or something like that.

(-> DS
    (tc/group-by :V1)
    (tc/as-regular-dataset)
    (tc/grouped?))

nil

You can also operate on grouped dataset as a regular one in case you want to access its columns using without-grouping-> threading macro.

(-> DS
    (tc/group-by [:V4 :V1])
    (tc/without-grouping->
     (tc/order-by (comp (juxt :V4 :V1) :name))))

_unnamed [6 3]:

:name	:group-id	:data
{:V4 “A”, :V1 1}	0	Group: {:V4 “A”, :V1 1} [2 4]:
{:V4 “A”, :V1 2}	3	Group: {:V4 “A”, :V1 2} [1 4]:
{:V4 “B”, :V1 1}	4	Group: {:V4 “B”, :V1 1} [1 4]:
{:V4 “B”, :V1 2}	1	Group: {:V4 “B”, :V1 2} [2 4]:
{:V4 “C”, :V1 1}	2	Group: {:V4 “C”, :V1 1} [2 4]:
{:V4 “C”, :V1 2}	5	Group: {:V4 “C”, :V1 2} [1 4]:

This is considered internal.

If you want to implement your own mapping function on grouped dataset you can call process-group-data and pass function operating on datasets. Result should be a dataset to have ungrouping working.

(-> DS
    (tc/group-by :V1)
    (tc/process-group-data #(str "Shape: " (vector (tc/row-count %) (tc/column-count %))))
    (tc/as-regular-dataset))

_unnamed [2 3]:

:name	:group-id	:data
1	0	Shape: [5 4]
2	1	Shape: [4 4]

Columns

Column is a special tech.ml.dataset structure. For our purposes we cat treat columns as typed and named sequence bound to particular dataset.

Type of the data is inferred from a sequence during column creation.

Names

To select dataset columns or column names columns-selector is used. columns-selector can be one of the following:

:all keyword - selects all columns
column name - for single column
sequence of column names - for collection of columns
regex - to apply pattern on column names or datatype
filter predicate - to filter column names or datatype
type namespaced keyword for specific datatype or group of datatypes

Column name can be anything.

column-names function returns names according to columns-selector and optional meta-field. meta-field is one of the following:

:name (default) - to operate on column names
:datatype - to operated on column types
:all - if you want to process all metadata

Datatype groups are:

:type/numerical - any numerical type
:type/float - floating point number (:float32 and :float64)
:type/integer - any integer
:type/datetime - any datetime type

If qualified keyword starts with :!type, complement set is used.

To select all column names you can use column-names function.

(tc/column-names DS)

(:V1 :V2 :V3 :V4)

(tc/column-names DS :all)

(:V1 :V2 :V3 :V4)

In case you want to select column which has name :all (or is sequence or map), put it into a vector. Below code returns empty sequence since there is no such column in the dataset.

(tc/column-names DS [:all])

()

Obviously selecting single name returns it’s name if available

(tc/column-names DS :V1)
(tc/column-names DS "no such column")

(:V1)
()

Select sequence of column names.

(tc/column-names DS [:V1 "V2" :V3 :V4 :V5])

(:V1 :V3 :V4)

Select names based on regex, columns ends with 1 or 4

(tc/column-names DS #".*[14]")

(:V1 :V4)

Select names based on regex operating on type of the column (to check what are the column types, call (tc/info DS :columns). Here we want to get integer columns only.

(tc/column-names DS #"^:int.*" :datatype)

(:V1 :V2)

(tc/column-names DS :type/integer)

(:V1 :V2)

And finally we can use predicate to select names. Let’s select double precision columns.

(tc/column-names DS #{:float64} :datatype)

(:V3)

(tc/column-names DS :type/float64)

(:V3)

If you want to select all columns but given, use complement function. Works only on a predicate.

(tc/column-names DS (complement #{:V1}))
(tc/column-names DS (complement #{:float64}) :datatype)
(tc/column-names DS :!type/float64)

(:V2 :V3 :V4)
(:V1 :V2 :V4)
(:V1 :V2 :V4)

You can select column names based on all column metadata at once by using :all metadata selector. Below we want to select column names ending with 1 which have long datatype.

(tc/column-names DS (fn [meta]
                       (and (= :int64 (:datatype meta))
                            (clojure.string/ends-with? (:name meta) "1"))) :all)

(:V1)

Select

select-columns creates dataset with columns selected by columns-selector as described above. Function works on regular and grouped dataset.

Select only float64 columns

(tc/select-columns DS #(= :float64 %) :datatype)

_unnamed [9 1]:

:V3
0.5
1.0
1.5
0.5
1.0
1.5
0.5
1.0
1.5

(tc/select-columns DS :type/float64)

_unnamed [9 1]:

:V3
0.5
1.0
1.5
0.5
1.0
1.5
0.5
1.0
1.5

Select all but :V1 columns

(tc/select-columns DS (complement #{:V1}))

_unnamed [9 3]:

:V2	:V3	:V4
1	0.5	A
2	1.0	B
3	1.5	C
4	0.5	A
5	1.0	B
6	1.5	C
7	0.5	A
8	1.0	B
9	1.5	C

If we have grouped data set, column selection is applied to every group separately.

(-> DS
    (tc/group-by :V1)
    (tc/select-columns [:V2 :V3])
    (tc/groups->map))

{1 Group: 1 [5 2]:

:V2	:V3
1	0.5
3	1.5
5	1.0
7	0.5
9	1.5

, 2 Group: 2 [4 2]:

:V2	:V3
2	1.0
4	0.5
6	1.5
8	1.0

}

Drop

drop-columns creates dataset with removed columns.

Drop float64 columns

(tc/drop-columns DS #(= :float64 %) :datatype)

_unnamed [9 3]:

:V1	:V2	:V4
1	1	A
2	2	B
1	3	C
2	4	A
1	5	B
2	6	C
1	7	A
2	8	B
1	9	C

(tc/drop-columns DS :type/float64)

_unnamed [9 3]:

:V1	:V2	:V4
1	1	A
2	2	B
1	3	C
2	4	A
1	5	B
2	6	C
1	7	A
2	8	B
1	9	C

Drop all columns but :V1 and :V2

(tc/drop-columns DS (complement #{:V1 :V2}))

_unnamed [9 2]:

:V1	:V2
1	1
2	2
1	3
2	4
1	5
2	6
1	7
2	8
1	9

If we have grouped data set, column selection is applied to every group separately. Selected columns are dropped.

(-> DS
    (tc/group-by :V1)
    (tc/drop-columns [:V2 :V3])
    (tc/groups->map))

{1 Group: 1 [5 2]:

:V1	:V4
1	A
1	C
1	B
1	A
1	C

, 2 Group: 2 [4 2]:

:V1	:V4
2	B
2	A
2	C
2	B

}

Rename

If you want to rename colums use rename-columns and pass map where keys are old names, values new ones.

You can also pass mapping function with optional columns-selector

(tc/rename-columns DS {:V1 "v1"
                        :V2 "v2"
                        :V3 [1 2 3]
                        :V4 (Object.)})

_unnamed [9 4]:

v1	v2	[1 2 3]	java.lang.Object@2d25775b
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Map all names with function

(tc/rename-columns DS (comp str second name))

_unnamed [9 4]:

1	2	3	4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Map selected names with function

(tc/rename-columns DS [:V1 :V3] (comp str second name))

_unnamed [9 4]:

1	:V2	3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Function works on grouped dataset

(-> DS
    (tc/group-by :V1)
    (tc/rename-columns {:V1 "v1"
                         :V2 "v2"
                         :V3 [1 2 3]
                         :V4 (Object.)})
    (tc/groups->map))

{1 Group: 1 [5 4]:

v1	v2	[1 2 3]	java.lang.Object@329232e7
1	1	0.5	A
1	3	1.5	C
1	5	1.0	B
1	7	0.5	A
1	9	1.5	C

, 2 Group: 2 [4 4]:

v1	v2	[1 2 3]	java.lang.Object@329232e7
2	2	1.0	B
2	4	0.5	A
2	6	1.5	C
2	8	1.0	B

}

Add or update

To add (or replace existing) column call add-column function. Function accepts:

ds - a dataset
column-name - if it’s existing column name, column will be replaced
column - can be column (from other dataset), sequence, single value or function. Too big columns are always trimmed. Too small are cycled or extended with missing values (according to size-strategy argument)
size-strategy (optional) - when new column is shorter than dataset row count, following strategies are applied:
- :cycle - repeat data
- :na - append missing values
- :strict - (default) throws an exception when sizes mismatch

Function works on grouped dataset.

Add single value as column

(tc/add-column DS :V5 "X")

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	X
2	2	1.0	B	X
1	3	1.5	C	X
2	4	0.5	A	X
1	5	1.0	B	X
2	6	1.5	C	X
1	7	0.5	A	X
2	8	1.0	B	X
1	9	1.5	C	X

Replace one column (column is trimmed)

(tc/add-column DS :V1 (repeatedly rand))

_unnamed [9 4]:

:V1	:V2	:V3	:V4
0.64649382	1	0.5	A
0.89795149	2	1.0	B
0.30971120	3	1.5	C
0.22866676	4	0.5	A
0.57409080	5	1.0	B
0.12716814	6	1.5	C
0.70445797	7	0.5	A
0.04564501	8	1.0	B
0.69727730	9	1.5	C

Copy column

(tc/add-column DS :V5 (DS :V1))

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	2	1.0	B	2
1	3	1.5	C	1
2	4	0.5	A	2
1	5	1.0	B	1
2	6	1.5	C	2
1	7	0.5	A	1
2	8	1.0	B	2
1	9	1.5	C	1

When function is used, argument is whole dataset and the result should be column, sequence or single value

(tc/add-column DS :row-count tc/row-count)

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	9
2	2	1.0	B	9
1	3	1.5	C	9
2	4	0.5	A	9
1	5	1.0	B	9
2	6	1.5	C	9
1	7	0.5	A	9
2	8	1.0	B	9
1	9	1.5	C	9

Above example run on grouped dataset, applies function on each group separately.

(-> DS
    (tc/group-by :V1)
    (tc/add-column :row-count tc/row-count)
    (tc/ungroup))

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	5
1	3	1.5	C	5
1	5	1.0	B	5
1	7	0.5	A	5
1	9	1.5	C	5
2	2	1.0	B	4
2	4	0.5	A	4
2	6	1.5	C	4
2	8	1.0	B	4

When column which is added is longer than row count in dataset, column is trimmed. When column is shorter, it’s cycled or missing values are appended.

(tc/add-column DS :V5 [:r :b] :cycle)

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	2	1.0	B	:b
1	3	1.5	C	:r
2	4	0.5	A	:b
1	5	1.0	B	:r
2	6	1.5	C	:b
1	7	0.5	A	:r
2	8	1.0	B	:b
1	9	1.5	C	:r

(tc/add-column DS :V5 [:r :b] :na)

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	2	1.0	B	:b
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Exception is thrown when :strict (default) strategy is used and column size is not equal row count

(try
  (tc/add-column DS :V5 [:r :b])
  (catch Exception e (str "Exception caught: "(ex-message e))))

"Exception caught: Column size (2) should be exactly the same as dataset row count (9). Consider `:cycle` or `:na` strategy."

Tha same applies for grouped dataset

(-> DS
    (tc/group-by :V3)
    (tc/add-column :V5 [:r :b] :na)
    (tc/ungroup))

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	4	0.5	A	:b
1	7	0.5	A
2	2	1.0	B	:r
1	5	1.0	B	:b
2	8	1.0	B
1	3	1.5	C	:r
2	6	1.5	C	:b
1	9	1.5	C

Let’s use other column to fill groups

(-> DS
    (tc/group-by :V3)
    (tc/add-column :V5 (DS :V2) :cycle)
    (tc/ungroup))

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	4	0.5	A	2
1	7	0.5	A	3
2	2	1.0	B	1
1	5	1.0	B	2
2	8	1.0	B	3
1	3	1.5	C	1
2	6	1.5	C	2
1	9	1.5	C	3

In case you want to add or update several columns you can call add-columns and provide map where keys are column names, vals are columns.

(tc/add-columns DS {:V1 #(map inc (% :V1))
                               :V5 #(map (comp keyword str) (% :V4))
                               :V6 11})

_unnamed [9 6]:

:V1	:V2	:V3	:V4	:V5	:V6
2	1	0.5	A	:A	11
3	2	1.0	B	:B	11
2	3	1.5	C	:C	11
3	4	0.5	A	:A	11
2	5	1.0	B	:B	11
3	6	1.5	C	:C	11
2	7	0.5	A	:A	11
3	8	1.0	B	:B	11
2	9	1.5	C	:C	11

Update

If you want to modify specific column(s) you can call update-columns. Arguments:

dataset
one of:
- columns-selector and function (or sequence of functions)
- map where keys are column names and vals are function

Functions accept column and have to return column or sequence

Reverse of columns

(tc/update-columns DS :all reverse)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	9	1.5	C
2	8	1.0	B
1	7	0.5	A
2	6	1.5	C
1	5	1.0	B
2	4	0.5	A
1	3	1.5	C
2	2	1.0	B
1	1	0.5	A

Apply dec/inc on numerical columns

(tc/update-columns DS :type/numerical [(partial map dec)
                                        (partial map inc)])

_unnamed [9 4]:

:V1	:V2	:V3	:V4
0	2	-0.5	A
1	3	0.0	B
0	4	0.5	C
1	5	-0.5	A
0	6	0.0	B
1	7	0.5	C
0	8	-0.5	A
1	9	0.0	B
0	10	0.5	C

You can also assign a function to a column by packing operations into the map.

(tc/update-columns DS {:V1 reverse
                        :V2 (comp shuffle seq)})

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	2	0.5	A
2	1	1.0	B
1	8	1.5	C
2	5	0.5	A
1	6	1.0	B
2	3	1.5	C
1	9	0.5	A
2	7	1.0	B
1	4	1.5	C

Map

The other way of creating or updating column is to map rows as regular map function. The arity of mapping function should be the same as number of selected columns.

Arguments:

ds - dataset
column-name - target column name
columns-selector - columns selected
map-fn - mapping function

Let’s add numerical columns together

(tc/map-columns DS
                 :sum-of-numbers
                 (tc/column-names DS  #{:int64 :float64} :datatype)
                 (fn [& rows]
                   (reduce + rows)))

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:sum-of-numbers
1	1	0.5	A	2.5
2	2	1.0	B	5.0
1	3	1.5	C	5.5
2	4	0.5	A	6.5
1	5	1.0	B	7.0
2	6	1.5	C	9.5
1	7	0.5	A	8.5
2	8	1.0	B	11.0
1	9	1.5	C	11.5

The same works on grouped dataset

(-> DS
    (tc/group-by :V4)
    (tc/map-columns :sum-of-numbers
                     (tc/column-names DS  #{:int64 :float64} :datatype)
                     (fn [& rows]
                       (reduce + rows)))
    (tc/ungroup))

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:sum-of-numbers
1	1	0.5	A	2.5
2	4	0.5	A	6.5
1	7	0.5	A	8.5
2	2	1.0	B	5.0
1	5	1.0	B	7.0
2	8	1.0	B	11.0
1	3	1.5	C	5.5
2	6	1.5	C	9.5
1	9	1.5	C	11.5

Reorder

To reorder columns use columns selectors to choose what columns go first. The unseleted columns are appended to the end.

(tc/reorder-columns DS :V4 [:V3 :V2])

_unnamed [9 4]:

:V4	:V3	:V2	:V1
A	0.5	1	1
B	1.0	2	2
C	1.5	3	1
A	0.5	4	2
B	1.0	5	1
C	1.5	6	2
A	0.5	7	1
B	1.0	8	2
C	1.5	9	1

This function doesn’t let you select meta field, so you have to call column-names in such case. Below we want to add integer columns at the end.

(tc/reorder-columns DS (tc/column-names DS (complement #{:int64}) :datatype))

_unnamed [9 4]:

:V3	:V4	:V1	:V2
0.5	A	1	1
1.0	B	2	2
1.5	C	1	3
0.5	A	2	4
1.0	B	1	5
1.5	C	2	6
0.5	A	1	7
1.0	B	2	8
1.5	C	1	9

Type conversion

To convert column into given datatype can be done using convert-types function. Not all the types can be converted automatically also some types require slow parsing (every conversion from string). In case where conversion is not possible you can pass conversion function.

Arguments:

ds - dataset
Two options:
- coltype-map in case when you want to convert several columns, keys are column names, vals are new types
- column-selector and new-types - column name and new datatype (or datatypes as sequence)

new-types can be:

a type like :int64 or :string or sequence of types
or sequence of pair of datetype and conversion function

After conversion additional infomation is given on problematic values.

The other conversion is casting column into java array (->array) of the type column or provided as argument. Grouped dataset returns sequence of arrays.

Basic conversion

(-> DS
    (tc/convert-types :V1 :float64)
    (tc/info :columns))

_unnamed :column info [4 6]:

:name	:datatype	:n-elems	:unparsed-indexes	:unparsed-data	:categorical?
:V1	:float64	9	{}	[]
:V2	:int64	9
:V3	:float64	9
:V4	:string	9			true

Using custom converter. Let’s treat :V4 as haxadecimal values. See that this way we can map column to any value.

(-> DS
    (tc/convert-types :V4 [[:int16 #(Integer/parseInt % 16)]]))

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	10
2	2	1.0	11
1	3	1.5	12
2	4	0.5	10
1	5	1.0	11
2	6	1.5	12
1	7	0.5	10
2	8	1.0	11
1	9	1.5	12

You can process several columns at once

(-> DS
    (tc/convert-types {:V1 :float64
                        :V2 :object
                        :V3 [:boolean #(< % 1.0)]
                        :V4 :object})
    (tc/info :columns))

_unnamed :column info [4 6]:

:name	:datatype	:n-elems	:unparsed-indexes	:unparsed-data	:categorical?
:V1	:float64	9	{}	[]
:V2	:object	9	{}	[]	true
:V3	:boolean	9	{}	[]
:V4	:object	9			true

Convert one type into another

(-> DS
    (tc/convert-types :type/numerical :int16)
    (tc/info :columns))

_unnamed :column info [4 6]:

:name	:datatype	:n-elems	:unparsed-indexes	:unparsed-data	:categorical?
:V1	:int16	9	{}	[]
:V2	:int16	9	{}	[]
:V3	:int16	9	{}	[]
:V4	:string	9			true

Function works on the grouped dataset

(-> DS
    (tc/group-by :V1)
    (tc/convert-types :V1 :float32)
    (tc/ungroup)
    (tc/info :columns))

_unnamed :column info [4 6]:

:name	:datatype	:n-elems	:unparsed-indexes	:unparsed-data	:categorical?
:V1	:float32	9	{}	[]
:V2	:int64	9
:V3	:float64	9
:V4	:string	9			true

Double array conversion.

(tc/->array DS :V1)

#object["[J" 0x23a24c11 "[J@23a24c11"]

Function also works on grouped dataset

(-> DS
    (tc/group-by :V3)
    (tc/->array :V2))

(#object["[J" 0x2d616536 "[J@2d616536"] #object["[J" 0xd264282 "[J@d264282"] #object["[J" 0x3dbb9667 "[J@3dbb9667"])

You can also cast the type to the other one (if casting is possible):

(tc/->array DS :V4 :string)
(tc/->array DS :V1 :float32)

#object["[Ljava.lang.String;" 0x777dde98 "[Ljava.lang.String;@777dde98"]
#object["[F" 0x2210bbc6 "[F@2210bbc6"]

Rows

Rows can be selected or dropped using various selectors:

row id(s) - row index as number or seqence of numbers (first row has index 0, second 1 and so on)
sequence of true/false values
filter by predicate (argument is row as a map)

When predicate is used you may want to limit columns passed to the function (select-keys option).

Additionally you may want to precalculate some values which will be visible for predicate as additional columns. It’s done internally by calling add-columns on a dataset. :pre is used as a column definitions.

Select

Select fifth row

(tc/select-rows DS 4)

_unnamed [1 4]:

:V1	:V2	:V3	:V4
1	5	1.0	B

Select 3 rows

(tc/select-rows DS [1 4 5])

_unnamed [3 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
1	5	1.0	B
2	6	1.5	C

Select rows using sequence of true/false values

(tc/select-rows DS [true nil nil true])

_unnamed [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A

Select rows using predicate

(tc/select-rows DS (comp #(< % 1) :V3))

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
1	7	0.5	A

The same works on grouped dataset, let’s select first row from every group.

(-> DS
    (tc/group-by :V1)
    (tc/select-rows 0)
    (tc/ungroup))

_unnamed [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B

If you want to select :V2 values which are lower than or equal mean in grouped dataset you have to precalculate it using :pre.

(-> DS
    (tc/group-by :V4)
    (tc/select-rows (fn [row] (<= (:V2 row) (:mean row)))
                     {:pre {:mean #(tech.v3.datatype.functional/mean (% :V2))}})
    (tc/ungroup))

_unnamed [6 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
2	2	1.0	B
1	5	1.0	B
1	3	1.5	C
2	6	1.5	C

Drop

drop-rows removes rows, and accepts exactly the same parameters as select-rows

Drop values lower than or equal :V2 column mean in grouped dataset.

(-> DS
    (tc/group-by :V4)
    (tc/drop-rows (fn [row] (<= (:V2 row) (:mean row)))
                   {:pre {:mean #(tech.v3.datatype.functional/mean (% :V2))}})
    (tc/ungroup))

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Map rows

Call a mapping function for every row. Mapping function should return a map, where keys are column names (new or old) and values are column values.

Works on grouped dataset too.

(tc/map-rows DS (fn [{:keys [V1 V2]}] {:V1 0
                                       :V5 (/ (+ V1 V2) (double V2))}))

_unnamed [9 5]:

:V2	:V3	:V4	:V5
1	0.5	A	2.00000000
2	1.0	B	2.00000000
3	1.5	C	1.33333333
4	0.5	A	1.50000000
5	1.0	B	1.20000000
6	1.5	C	1.33333333
7	0.5	A	1.14285714
8	1.0	B	1.25000000
9	1.5	C	1.11111111

Other

There are several function to select first, last, random rows, or display head, tail of the dataset. All functions work on grouped dataset.

All random functions accept :seed as an option if you want to fix returned result.

First row

(tc/first DS)

_unnamed [1 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A

Last row

(tc/last DS)

_unnamed [1 4]:

:V1	:V2	:V3	:V4
1	9	1.5	C

Random row (single)

(tc/rand-nth DS)

_unnamed [1 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C

Random row (single) with seed

(tc/rand-nth DS {:seed 42})

_unnamed [1 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C

Random n (default: row count) rows with repetition.

(tc/random DS)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
2	4	0.5	A
1	9	1.5	C
1	9	1.5	C
1	7	0.5	A
1	5	1.0	B
1	5	1.0	B
2	8	1.0	B
2	8	1.0	B
2	2	1.0	B

Five random rows with repetition

(tc/random DS 5)

_unnamed [5 4]:

:V1	:V2	:V3	:V4
1	7	0.5	A
1	9	1.5	C
1	5	1.0	B
2	8	1.0	B
1	5	1.0	B

Five random, non-repeating rows

(tc/random DS 5 {:repeat? false})

_unnamed [5 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
2	4	0.5	A
2	6	1.5	C
2	2	1.0	B
1	1	0.5	A

Five random, with seed

(tc/random DS 5 {:seed 42})

_unnamed [5 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C
1	5	1.0	B
1	3	1.5	C
1	1	0.5	A
1	9	1.5	C

Shuffle dataset

(tc/shuffle DS)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	6	1.5	C
2	8	1.0	B
1	7	0.5	A
1	9	1.5	C
2	4	0.5	A
1	5	1.0	B
1	3	1.5	C
1	1	0.5	A

Shuffle with seed

(tc/shuffle DS {:seed 42})

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	5	1.0	B
2	2	1.0	B
2	6	1.5	C
2	4	0.5	A
2	8	1.0	B
1	3	1.5	C
1	7	0.5	A
1	1	0.5	A
1	9	1.5	C

First n rows (default 5)

(tc/head DS)

_unnamed [5 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B

Last n rows (default 5)

(tc/tail DS)

_unnamed [5 4]:

:V1	:V2	:V3	:V4
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

by-rank calculates rank on column(s). It’s base on R rank() with addition of :dense (default) tie strategy which give consecutive rank numbering.

:desc? options (default: true) sorts input with descending order, giving top values under 0 value.

rank is zero based and is defined at tablecloth.api.utils namespace.

(tc/by-rank DS :V3 zero?) ;; most V3 values

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
2	6	1.5	C
1	9	1.5	C

(tc/by-rank DS :V3 zero? {:desc? false}) ;; least V3 values

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
1	7	0.5	A

Rank also works on multiple columns

(tc/by-rank DS [:V1 :V3] zero? {:desc? false})

_unnamed [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	7	0.5	A

Select 5 random rows from each group

(-> DS
    (tc/group-by :V4)
    (tc/random 5)
    (tc/ungroup))

_unnamed [15 4]:

:V1	:V2	:V3	:V4
1	7	0.5	A
1	1	0.5	A
1	7	0.5	A
2	4	0.5	A
2	4	0.5	A
2	8	1.0	B
2	8	1.0	B
2	8	1.0	B
2	2	1.0	B
2	2	1.0	B
1	9	1.5	C
1	9	1.5	C
1	3	1.5	C
1	3	1.5	C
1	9	1.5	C

Aggregate

Aggregating is a function which produces single row out of dataset.

Aggregator is a function or sequence or map of functions which accept dataset as an argument and result single value, sequence of values or map.

Where map is given as an input or result, keys are treated as column names.

Grouped dataset is ungrouped after aggregation. This can be turned off by setting :ungroup? to false. In case you want to pass additional ungrouping parameters add them to the options.

By default resulting column names are prefixed with summary prefix (set it with :default-column-name-prefix option).

Let’s calculate mean of some columns

(tc/aggregate DS #(reduce + (% :V2)))

_unnamed [1 1]:

summary
45

Let’s give resulting column a name.

(tc/aggregate DS {:sum-of-V2 #(reduce + (% :V2))})

_unnamed [1 1]:

:sum-of-V2
45

Sequential result is spread into separate columns

(tc/aggregate DS #(take 5(% :V2)))

_unnamed [1 5]:

:summary-0	:summary-1	:summary-2	:summary-3	:summary-4
1	2	3	4	5

You can combine all variants and rename default prefix

(tc/aggregate DS [#(take 3 (% :V2))
                   (fn [ds] {:sum-v1 (reduce + (ds :V1))
                            :prod-v3 (reduce * (ds :V3))})] {:default-column-name-prefix "V2-value"})

_unnamed [1 5]:

:V2-value-0-0	:V2-value-0-1	:V2-value-0-2	:V2-value-1-sum-v1	:V2-value-1-prod-v3
1	2	3	13	0.421875

Processing grouped dataset

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate [#(take 3 (% :V2))
                    (fn [ds] {:sum-v1 (reduce + (ds :V1))
                             :prod-v3 (reduce * (ds :V3))})] {:default-column-name-prefix "V2-value"}))

_unnamed [3 6]:

:V4	:V2-value-0-0	:V2-value-0-1	:V2-value-0-2	:V2-value-1-sum-v1	:V2-value-1-prod-v3
A	1	4	7	4	0.125
B	2	5	8	5	1.000
C	3	6	9	4	3.375

Result of aggregating is automatically ungrouped, you can skip this step by stetting :ungroup? option to false.

(-> DS
    (tc/group-by [:V3])
    (tc/aggregate [#(take 3 (% :V2))
                    (fn [ds] {:sum-v1 (reduce + (ds :V1))
                             :prod-v3 (reduce * (ds :V3))})] {:default-column-name-prefix "V2-value"
                                                              :ungroup? false}))

_unnamed [3 3]:

:name	:group-id	:data
{:V3 0.5}	0	_unnamed [1 5]:
{:V3 1.0}	1	_unnamed [1 5]:
{:V3 1.5}	2	_unnamed [1 5]:

Column

You can perform columnar aggreagation also. aggregate-columns selects columns and apply aggregating function (or sequence of functions) for each column separately.

(tc/aggregate-columns DS [:V1 :V2 :V3] #(reduce + %))

_unnamed [1 3]:

:V1	:V2	:V3
13	45	9.0

(tc/aggregate-columns DS [:V1 :V2 :V3] [#(reduce + %)
                                         #(reduce max %)
                                         #(reduce * %)])

_unnamed [1 3]:

:V1	:V2	:V3
13	9	0.421875

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate-columns [:V1 :V2 :V3] #(reduce + %)))

_unnamed [3 4]:

:V4	:V1	:V2	:V3
A	4	12	1.5
B	5	15	3.0
C	4	18	4.5

You can also aggregate whole dataset

(-> DS
    (tc/drop-columns :V4)
    (tc/aggregate-columns #(reduce + %)))

_unnamed [1 3]:

:V1	:V2	:V3
13	45	9.0

Crosstab

Cross tabulation built from two sets of columns. First rows and cols are used to construct grouped dataset, then aggregation function is applied for each pair. By default it counts rows from each group.

Options are:

:aggregator - function which aggregates values of grouped dataset, default it’s row-count
:marginal-rows and :marginal-cols - if true, sum of rows and cols are added as an additional columns and row. May be custom function which accepts pure row and col as a seq.
:replace-missing? - should missing values be replaced (default: true) with :missing-value (default: 0)
:pivot? - if false, flat aggregation result is returned (default: false)

(def ctds (tc/dataset {:a [:foo :foo :bar :bar :foo :foo]
                       :b [:one :one :two :one :two :one]
                       :c [:dull :dull :shiny :dull :dull :shiny]}))

#’user/ctds

ctds

_unnamed [6 3]:

|   :a |   :b |     :c |
|------|------|--------|
| :foo | :one |  :dull |
| :foo | :one |  :dull |
| :bar | :two | :shiny |
| :bar | :one |  :dull |
| :foo | :two |  :dull |
| :foo | :one | :shiny |

(tc/crosstab ctds :a [:b :c])

_unnamed [2 5]:

rows/cols	[:one :dull]	[:two :shiny]	[:two :dull]	[:one :shiny]
:foo	2	0	1	1
:bar	1	1	0	0

With marginals

(tc/crosstab ctds :a [:b :c] {:marginal-rows true :marginal-cols true})

_unnamed [3 6]:

rows/cols	[:one :dull]	[:two :shiny]	[:two :dull]	[:one :shiny]	:summary
:foo	2	0	1	1	4
:bar	1	1	0	0	2
:summary	3	1	1	1	6

Set missing value to -1

(tc/crosstab ctds :a [:b :c] {:missing-value -1})

_unnamed [2 5]:

rows/cols	[:one :dull]	[:two :shiny]	[:two :dull]	[:one :shiny]
:foo	2	-1	1	1
:bar	1	1	-1	-1

Turn off pivoting

(tc/crosstab ctds :a [:b :c] {:pivot? false})

_unnamed [5 3]:

:rows	:cols	summary
:foo	[:one :dull]	2
:bar	[:two :shiny]	1
:bar	[:one :dull]	1
:foo	[:two :dull]	1
:foo	[:one :shiny]	1

Order

Ordering can be done by column(s) or any function operating on row. Possible order can be:

:asc for ascending order (default)
:desc for descending order
custom comparator

:select-keys limits row map provided to ordering functions.

Order by single column, ascending

(tc/order-by DS :V1)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	3	1.5	C
1	5	1.0	B
1	7	0.5	A
1	9	1.5	C
2	6	1.5	C
2	4	0.5	A
2	8	1.0	B
2	2	1.0	B

Descending order

(tc/order-by DS :V1 :desc)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	4	0.5	A
2	6	1.5	C
2	8	1.0	B
1	5	1.0	B
1	3	1.5	C
1	7	0.5	A
1	1	0.5	A
1	9	1.5	C

Order by two columns

(tc/order-by DS [:V1 :V2])

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	3	1.5	C
1	5	1.0	B
1	7	0.5	A
1	9	1.5	C
2	2	1.0	B
2	4	0.5	A
2	6	1.5	C
2	8	1.0	B

Use different orders for columns

(tc/order-by DS [:V1 :V2] [:asc :desc])

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	9	1.5	C
1	7	0.5	A
1	5	1.0	B
1	3	1.5	C
1	1	0.5	A
2	8	1.0	B
2	6	1.5	C
2	4	0.5	A
2	2	1.0	B

(tc/order-by DS [:V1 :V2] [:desc :desc])

_unnamed [9 4]:

:V1	:V2	:V3	:V4
2	8	1.0	B
2	6	1.5	C
2	4	0.5	A
2	2	1.0	B
1	9	1.5	C
1	7	0.5	A
1	5	1.0	B
1	3	1.5	C
1	1	0.5	A

(tc/order-by DS [:V1 :V3] [:desc :asc])

_unnamed [9 4]:

:V1	:V2	:V3	:V4
2	4	0.5	A
2	2	1.0	B
2	8	1.0	B
2	6	1.5	C
1	1	0.5	A
1	7	0.5	A
1	5	1.0	B
1	3	1.5	C
1	9	1.5	C

Custom function can be used to provided ordering key. Here order by :V4 descending, then by product of other columns ascending.

(tc/order-by DS [:V4 (fn [row] (* (:V1 row)
                                  (:V2 row)
                                  (:V3 row)))] [:desc :asc])

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
1	9	1.5	C
2	6	1.5	C
2	2	1.0	B
1	5	1.0	B
2	8	1.0	B
1	1	0.5	A
1	7	0.5	A
2	4	0.5	A

Custom comparator also can be used in case objects are not comparable by default. Let’s define artificial one: if Euclidean distance is lower than 2, compare along z else along x and y. We use first three columns for that.

(defn dist
  [v1 v2]
  (->> v2
       (map - v1)
       (map #(* % %))
       (reduce +)
       (Math/sqrt)))

#'user/dist

(tc/order-by DS [:V1 :V2 :V3] (fn [[x1 y1 z1 :as v1] [x2 y2 z2 :as v2]]
                                (let [d (dist v1 v2)]
                                  (if (< d 2.0)
                                    (compare z1 z2)
                                    (compare [x1 y1] [x2 y2])))))

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	5	1.0	B
1	7	0.5	A
1	9	1.5	C
2	2	1.0	B
2	4	0.5	A
1	3	1.5	C
2	6	1.5	C
2	8	1.0	B

Unique

Remove rows which contains the same data. By default unique-by removes duplicates from whole dataset. You can also pass list of columns or functions (similar as in group-by) to remove duplicates limited by them. Default strategy is to keep the first row. More strategies below.

unique-by works on groups

Remove duplicates from whole dataset

(tc/unique-by DS)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Remove duplicates from each group selected by column.

(tc/unique-by DS :V1)

_unnamed [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B

Pair of columns

(tc/unique-by DS [:V1 :V3])

_unnamed [6 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C

Also function can be used, split dataset by modulo 3 on columns :V2

(tc/unique-by DS (fn [m] (mod (:V2 m) 3)))

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C

The same can be achived with group-by

(-> DS
    (tc/group-by (fn [m] (mod (:V2 m) 3)))
    (tc/first)
    (tc/ungroup))

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C

Grouped dataset

(-> DS
    (tc/group-by :V4)
    (tc/unique-by :V1)
    (tc/ungroup))

_unnamed [6 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
2	2	1.0	B
1	5	1.0	B
1	3	1.5	C
2	6	1.5	C

Strategies

There are 4 strategies defined:

:first - select first row (default)
:last - select last row
:random - select random row
any function - apply function to a columns which are subject of uniqueness

Last

(tc/unique-by DS :V1 {:strategy :last})

_unnamed [2 4]:

:V1	:V2	:V3	:V4
2	8	1.0	B
1	9	1.5	C

Random

(tc/unique-by DS :V1 {:strategy :random})

_unnamed [2 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
1	5	1.0	B

Pack columns into vector

(tc/unique-by DS :V4 {:strategy vec})

_unnamed [3 3]:

:V1	:V2	:V3
[1 2 1]	[1 4 7]	[0.5 0.5 0.5]
[2 1 2]	[2 5 8]	[1.0 1.0 1.0]
[1 2 1]	[3 6 9]	[1.5 1.5 1.5]

Sum columns

(tc/unique-by DS :V4 {:strategy (partial reduce +)})

_unnamed [3 3]:

:V1	:V2	:V3
4	12	1.5
5	15	3.0
4	18	4.5

Group by function and apply functions

(tc/unique-by DS (fn [m] (mod (:V2 m) 3)) {:strategy vec})

_unnamed [3 4]:

:V1	:V2	:V3	:V4
[1 2 1]	[1 4 7]	[0.5 0.5 0.5]	[“A” “A” “A”]
[2 1 2]	[2 5 8]	[1.0 1.0 1.0]	[“B” “B” “B”]
[1 2 1]	[3 6 9]	[1.5 1.5 1.5]	[“C” “C” “C”]

Grouped dataset

(-> DS
    (tc/group-by :V1)
    (tc/unique-by (fn [m] (mod (:V2 m) 3)) {:strategy vec})
    (tc/ungroup {:add-group-as-column :from-V1}))

_unnamed [6 5]:

:from-V1	:V1	:V2	:V3	:V4
1	[1 1]	[1 7]	[0.5 0.5]	[“A” “A”]
1	[1 1]	[3 9]	[1.5 1.5]	[“C” “C”]
1	[1]	[5]	[1.0]	[“B”]
2	[2 2]	[2 8]	[1.0 1.0]	[“B” “B”]
2	[2]	[4]	[0.5]	[“A”]
2	[2]	[6]	[1.5]	[“C”]

Missing

When dataset contains missing values you can select or drop rows with missing values or replace them using some strategy.

column-selector can be used to limit considered columns

Let’s define dataset which contains missing values

(def DSm (tc/dataset {:V1 (take 9 (cycle [1 2 nil]))
                      :V2 (range 1 10)
                      :V3 (take 9 (cycle [0.5 1.0 nil 1.5]))
                      :V4 (take 9 (cycle ["A" "B" "C"]))}))

DSm

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
	3		C
1	4	1.5	A
2	5	0.5	B
	6	1.0	C
1	7		A
2	8	1.5	B
	9	0.5	C

Select

Select rows with missing values

(tc/select-missing DSm)

_unnamed [4 4]:

:V1	:V2	:V3	:V4
	3		C
	6	1.0	C
1	7		A
	9	0.5	C

Select rows with missing values in :V1

(tc/select-missing DSm :V1)

_unnamed [3 4]:

:V2	:V3	:V4
3		C
6	1.0	C
9	0.5	C

The same with grouped dataset

(-> DSm
    (tc/group-by :V4)
    (tc/select-missing :V3)
    (tc/ungroup))

_unnamed [2 4]:

:V1	:V2	:V3	:V4
1	7		A
	3		C

Drop

Drop rows with missing values

(tc/drop-missing DSm)

_unnamed [5 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	4	1.5	A
2	5	0.5	B
2	8	1.5	B

Drop rows with missing values in :V1

(tc/drop-missing DSm :V1)

_unnamed [6 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	4	1.5	A
2	5	0.5	B
1	7		A
2	8	1.5	B

The same with grouped dataset

(-> DSm
    (tc/group-by :V4)
    (tc/drop-missing :V1)
    (tc/ungroup))

_unnamed [6 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	4	1.5	A
1	7		A
2	2	1.0	B
2	5	0.5	B
2	8	1.5	B

Replace

Missing values can be replaced using several strategies. replace-missing accepts:

dataset
column selector, default: :all
strategy, default: :nearest
value (optional)
- single value
- sequence of values (cycled)
- function, applied on column(s) with stripped missings
- map with [index,value] pairs

Strategies are:

:value - replace with given value
:up - copy values up
:down - copy values down
:updown - copy values up and then down for missing values at the end
:downup - copy values down and then up for missing values at the beginning
:mid or :nearest - copy values around known values
:midpoint - use average value from previous and next non-missing
:lerp - trying to lineary approximate values, works for numbers and datetime, otherwise applies :nearest. For numbers always results in float datatype.

Let’s define special dataset here:

(def DSm2 (tc/dataset {:a [nil nil nil 1.0 2  nil nil nil nil  nil 4   nil  11 nil nil]
                       :b [2   2   2 nil nil nil nil nil nil 13   nil   3  4  5 5]}))

DSm2

_unnamed [15 2]:

:a	:b
	2
	2
	2
1.0
2.0




	13
4.0
	3
11.0	4
	5
	5

Replace missing with default strategy for all columns

(tc/replace-missing DSm2)

_unnamed [15 2]:

:a	:b
1.0	2
1.0	2
1.0	2
1.0	2
2.0	2
2.0	2
2.0	13
2.0	13
4.0	13
4.0	13
4.0	13
4.0	3
11.0	4
11.0	5
11.0	5

Replace missing with single value in whole dataset

(tc/replace-missing DSm2 :all :value 999)

_unnamed [15 2]:

:a	:b
999.0	2
999.0	2
999.0	2
1.0	999
2.0	999
999.0	999
999.0	999
999.0	999
999.0	999
999.0	13
4.0	999
999.0	3
11.0	4
999.0	5
999.0	5

Replace missing with single value in :a column

(tc/replace-missing DSm2 :a :value 999)

_unnamed [15 2]:

:a	:b
999.0	2
999.0	2
999.0	2
1.0
2.0
999.0
999.0
999.0
999.0
999.0	13
4.0
999.0	3
11.0	4
999.0	5
999.0	5

Replace missing with sequence in :a column

(tc/replace-missing DSm2 :a :value [-999 -998 -997])

_unnamed [15 2]:

:a	:b
-999.0	2
-998.0	2
-997.0	2
1.0
2.0
-999.0
-998.0
-997.0
-999.0
-998.0	13
4.0
-997.0	3
11.0	4
-999.0	5
-998.0	5

Replace missing with a function (mean)

(tc/replace-missing DSm2 :a :value tech.v3.datatype.functional/mean)

_unnamed [15 2]:

:a	:b
4.5	2
4.5	2
4.5	2
1.0
2.0
4.5
4.5
4.5
4.5
4.5	13
4.0
4.5	3
11.0	4
4.5	5
4.5	5

Replace missing some missing values with a map

(tc/replace-missing DSm2 :a :value {0 100 1 -100 14 -1000})

_unnamed [15 2]:

:a	:b
100.0	2
-100.0	2
	2
1.0
2.0




	13
4.0
	3
11.0	4
	5
-1000.0	5

Using :down strategy, fills gaps with values from above. You can see that if missings are at the beginning, the are filled with first value

(tc/replace-missing DSm2 [:a :b] :downup)

_unnamed [15 2]:

:a	:b
1.0	2
1.0	2
1.0	2
1.0	2
2.0	2
2.0	2
2.0	2
2.0	2
2.0	2
2.0	13
4.0	13
4.0	3
11.0	4
11.0	5
11.0	5

To fix above issue you can provide value

(tc/replace-missing DSm2 [:a :b] :down 999)

_unnamed [15 2]:

:a	:b
999.0	2
999.0	2
999.0	2
1.0	2
2.0	2
2.0	2
2.0	2
2.0	2
2.0	2
2.0	13
4.0	13
4.0	3
11.0	4
11.0	5
11.0	5

The same applies for :up strategy which is opposite direction.

(tc/replace-missing DSm2 [:a :b] :up)

_unnamed [15 2]:

:a	:b
1.0	2
1.0	2
1.0	2
1.0	13
2.0	13
4.0	13
4.0	13
4.0	13
4.0	13
4.0	13
4.0	3
11.0	3
11.0	4
	5
	5

(tc/replace-missing DSm2 [:a :b] :updown)

_unnamed [15 2]:

:a	:b
1.0	2
1.0	2
1.0	2
1.0	13
2.0	13
4.0	13
4.0	13
4.0	13
4.0	13
4.0	13
4.0	3
11.0	3
11.0	4
11.0	5
11.0	5

The same applies for :up strategy which is opposite direction.

(tc/replace-missing DSm2 [:a :b] :midpoint)

_unnamed [15 2]:

:a	:b
1.0	2.0
1.0	2.0
1.0	2.0
1.0	7.5
2.0	7.5
3.0	7.5
3.0	7.5
3.0	7.5
3.0	7.5
3.0	13.0
4.0	8.0
7.5	3.0
11.0	4.0
11.0	5.0
11.0	5.0

We can use a function which is applied after applying :up or :down

(tc/replace-missing DSm2 [:a :b] :down tech.v3.datatype.functional/mean)

_unnamed [15 2]:

:a	:b
4.5	2
4.5	2
4.5	2
1.0	2
2.0	2
2.0	2
2.0	2
2.0	2
2.0	2
2.0	13
4.0	13
4.0	3
11.0	4
11.0	5
11.0	5

Lerp tries to apply linear interpolation of the values

(tc/replace-missing DSm2 [:a :b] :lerp)

_unnamed [15 2]:

:a	:b
1.00000000	2.00000000
1.00000000	2.00000000
1.00000000	2.00000000
1.00000000	3.57142857
2.00000000	5.14285714
2.33333333	6.71428571
2.66666667	8.28571429
3.00000000	9.85714286
3.33333333	11.42857143
3.66666667	13.00000000
4.00000000	8.00000000
7.50000000	3.00000000
11.00000000	4.00000000
11.00000000	5.00000000
11.00000000	5.00000000

Lerp works also on dates

(-> (tc/dataset {:dt [(java.time.LocalDateTime/of 2020 1 1 11 22 33)
                      nil nil nil nil nil nil nil
                      (java.time.LocalDateTime/of 2020 10 1 1 1 1)]})
    (tc/replace-missing :lerp))

_unnamed [9 1]:

:dt
2020-01-01T11:22:33
2020-02-04T16:04:51.500
2020-03-09T20:47:10
2020-04-13T01:29:28.500
2020-05-17T06:11:47
2020-06-20T10:54:05.500
2020-07-24T15:36:24
2020-08-27T20:18:42.500
2020-10-01T01:01:01

Inject

When your column contains not continuous data range you can fill up with lacking values. Arguments:

dataset
column name
expected step (max-span, milliseconds in case of datetime column)
(optional) missing-strategy - how to replace missing, default :down (set to nil if none)
(optional) missing-value - optional value for replace missing

(-> (tc/dataset {:a [1 2 9]
                 :b [:a :b :c]})
    (tc/fill-range-replace :a 1))

_unnamed [9 2]:

:a	:b
1.0	:a
2.0	:b
3.0	:b
4.0	:b
5.0	:b
6.0	:b
7.0	:b
8.0	:b
9.0	:c

Join/Separate Columns

Joining or separating columns are operations which can help to tidy messy dataset.

join-columns joins content of the columns (as string concatenation or other structure) and stores it in new column
separate-column splits content of the columns into set of new columns

Join

join-columns accepts:

dataset
column selector (as in select-columns)
options
:separator (default "-")
:drop-columns? - whether to drop source columns or not (default true)
:result-type
:map - packs data into map
:seq - packs data into sequence
:string - join strings with separator (default)
or custom function which gets row as a vector
:missing-subst - substitution for missing value

Default usage. Create :joined column out of other columns.

(tc/join-columns DSm :joined [:V1 :V2 :V4])

_unnamed [9 2]:

:V3	:joined
0.5	1-1-A
1.0	2-2-B
	3-C
1.5	1-4-A
0.5	2-5-B
1.0	6-C
	1-7-A
1.5	2-8-B
0.5	9-C

Without dropping source columns.

(tc/join-columns DSm :joined [:V1 :V2 :V4] {:drop-columns? false})

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:joined
1	1	0.5	A	1-1-A
2	2	1.0	B	2-2-B
	3		C	3-C
1	4	1.5	A	1-4-A
2	5	0.5	B	2-5-B
	6	1.0	C	6-C
1	7		A	1-7-A
2	8	1.5	B	2-8-B
	9	0.5	C	9-C

Let’s replace missing value with “NA” string.

(tc/join-columns DSm :joined [:V1 :V2 :V4] {:missing-subst "NA"})

_unnamed [9 2]:

:V3	:joined
0.5	1-1-A
1.0	2-2-B
	NA-3-C
1.5	1-4-A
0.5	2-5-B
1.0	NA-6-C
	1-7-A
1.5	2-8-B
0.5	NA-9-C

We can use custom separator.

(tc/join-columns DSm :joined [:V1 :V2 :V4] {:separator "/"
                                            :missing-subst "."})

_unnamed [9 2]:

:V3	:joined
0.5	1/1/A
1.0	2/2/B
	./3/C
1.5	1/4/A
0.5	2/5/B
1.0	./6/C
	1/7/A
1.5	2/8/B
0.5	./9/C

Or even sequence of separators.

(tc/join-columns DSm :joined [:V1 :V2 :V4] {:separator ["-" "/"]
                                            :missing-subst "."})

_unnamed [9 2]:

:V3	:joined
0.5	1-1/A
1.0	2-2/B
	.-3/C
1.5	1-4/A
0.5	2-5/B
1.0	.-6/C
	1-7/A
1.5	2-8/B
0.5	.-9/C

The other types of results, map:

(tc/join-columns DSm :joined [:V1 :V2 :V4] {:result-type :map})

_unnamed [9 2]:

:V3	:joined
0.5	{:V1 1, :V2 1, :V4 “A”}
1.0	{:V1 2, :V2 2, :V4 “B”}
	{:V1 nil, :V2 3, :V4 “C”}
1.5	{:V1 1, :V2 4, :V4 “A”}
0.5	{:V1 2, :V2 5, :V4 “B”}
1.0	{:V1 nil, :V2 6, :V4 “C”}
	{:V1 1, :V2 7, :V4 “A”}
1.5	{:V1 2, :V2 8, :V4 “B”}
0.5	{:V1 nil, :V2 9, :V4 “C”}

Sequence

(tc/join-columns DSm :joined [:V1 :V2 :V4] {:result-type :seq})

_unnamed [9 2]:

:V3	:joined
0.5	(1 1 “A”)
1.0	(2 2 “B”)
	(nil 3 “C”)
1.5	(1 4 “A”)
0.5	(2 5 “B”)
1.0	(nil 6 “C”)
	(1 7 “A”)
1.5	(2 8 “B”)
0.5	(nil 9 “C”)

Custom function, calculate hash

(tc/join-columns DSm :joined [:V1 :V2 :V4] {:result-type hash})

_unnamed [9 2]:

:V3	:joined
0.5	535226087
1.0	1128801549
	-1842240303
1.5	2022347171
0.5	1884312041
1.0	-1555412370
	1640237355
1.5	-967279152
0.5	1128367958

Grouped dataset

(-> DSm
    (tc/group-by :V4)
    (tc/join-columns :joined [:V1 :V2 :V4])
    (tc/ungroup))

_unnamed [9 2]:

:V3	:joined
0.5	1-1-A
1.5	1-4-A
	1-7-A
1.0	2-2-B
0.5	2-5-B
1.5	2-8-B
	3-C
1.0	6-C
0.5	9-C

Tidyr examples

source

(def df (tc/dataset {:x ["a" "a" nil nil]
                      :y ["b" nil "b" nil]}))

#'user/df

df

_unnamed [4 2]:

:x	:y
a	b
a
	b

(tc/join-columns df "z" [:x :y] {:drop-columns? false
                                  :missing-subst "NA"
                                  :separator "_"})

_unnamed [4 3]:

:x	:y	z
a	b	a_b
a		a_NA
	b	NA_b
		NA_NA

(tc/join-columns df "z" [:x :y] {:drop-columns? false
                                  :separator "_"})

_unnamed [4 3]:

:x	:y	z
a	b	a_b
a		a
	b	b

Separate

Column can be also separated into several other columns using string as separator, regex or custom function. Arguments:

dataset
source column
target columns - can be nil or :infer to automatically create columns
separator as:
- string - it’s converted to regular expression and passed to clojure.string/split function
- regex
- or custom function (default: identity)
options
- :drop-column? - whether drop source column(s) or not (default: true). Set to :all to keep only separation result.
- :missing-subst - values which should be treated as missing, can be set, sequence, value or function (default: "")

Custom function (as separator) should return seqence of values for given value or a sequence of map.

Separate float into integer and factional values

(tc/separate-column DS :V3 [:int-part :frac-part] (fn [^double v]
                                                     [(int (quot v 1.0))
                                                      (mod v 1.0)]))

_unnamed [9 5]:

:V1	:V2	:int-part	:frac-part	:V4
1	1	0	0.5	A
2	2	1	0.0	B
1	3	1	0.5	C
2	4	0	0.5	A
1	5	1	0.0	B
2	6	1	0.5	C
1	7	0	0.5	A
2	8	1	0.0	B
1	9	1	0.5	C

Source column can be kept

(tc/separate-column DS :V3 [:int-part :frac-part] (fn [^double v]
                                                     [(int (quot v 1.0))
                                                      (mod v 1.0)]) {:drop-column? false})

_unnamed [9 6]:

:V1	:V2	:V3	:int-part	:frac-part	:V4
1	1	0.5	0	0.5	A
2	2	1.0	1	0.0	B
1	3	1.5	1	0.5	C
2	4	0.5	0	0.5	A
1	5	1.0	1	0.0	B
2	6	1.5	1	0.5	C
1	7	0.5	0	0.5	A
2	8	1.0	1	0.0	B
1	9	1.5	1	0.5	C

We can treat 0 or 0.0 as missing value

(tc/separate-column DS :V3 [:int-part :frac-part] (fn [^double v]
                                                     [(int (quot v 1.0))
                                                      (mod v 1.0)]) {:missing-subst [0 0.0]})

_unnamed [9 5]:

:V1	:V2	:int-part	:frac-part	:V4
1	1		0.5	A
2	2	1		B
1	3	1	0.5	C
2	4		0.5	A
1	5	1		B
2	6	1	0.5	C
1	7		0.5	A
2	8	1		B
1	9	1	0.5	C

Works on grouped dataset

(-> DS
    (tc/group-by :V4)
    (tc/separate-column :V3 [:int-part :fract-part] (fn [^double v]
                                                       [(int (quot v 1.0))
                                                        (mod v 1.0)]))
    (tc/ungroup))

_unnamed [9 5]:

:V1	:V2	:int-part	:fract-part	:V4
1	1	0	0.5	A
2	4	0	0.5	A
1	7	0	0.5	A
2	2	1	0.0	B
1	5	1	0.0	B
2	8	1	0.0	B
1	3	1	0.5	C
2	6	1	0.5	C
1	9	1	0.5	C

Separate using separator returning sequence of maps.

(tc/separate-column DS :V3 (fn [^double v]
                              {:int-part (int (quot v 1.0))
                               :fract-part (mod v 1.0)}))

_unnamed [9 5]:

:V1	:V2	:int-part	:fract-part	:V4
1	1	0	0.5	A
2	2	1	0.0	B
1	3	1	0.5	C
2	4	0	0.5	A
1	5	1	0.0	B
2	6	1	0.5	C
1	7	0	0.5	A
2	8	1	0.0	B
1	9	1	0.5	C

Keeping all columns

(tc/separate-column DS :V3 nil (fn [^double v]
                                  {:int-part (int (quot v 1.0))
                                   :fract-part (mod v 1.0)}) {:drop-column? false})

_unnamed [9 6]:

:V1	:V2	:V3	:int-part	:fract-part	:V4
1	1	0.5	0	0.5	A
2	2	1.0	1	0.0	B
1	3	1.5	1	0.5	C
2	4	0.5	0	0.5	A
1	5	1.0	1	0.0	B
2	6	1.5	1	0.5	C
1	7	0.5	0	0.5	A
2	8	1.0	1	0.0	B
1	9	1.5	1	0.5	C

Droping all colums but separated

(tc/separate-column DS :V3 nil (fn [^double v]
                                 {:int-part (int (quot v 1.0))
                                  :fract-part (mod v 1.0)}) {:drop-column? :all})

_unnamed [9 2]:

:int-part	:fract-part
0	0.5
1	0.0
1	0.5
0	0.5
1	0.0
1	0.5
0	0.5
1	0.0
1	0.5

Infering column names

(tc/separate-column DS :V3 (fn [^double v]
                             [(int (quot v 1.0)) (mod v 1.0)]))

_unnamed [9 5]:

:V1	:V2	:V3-0	:V3-1	:V4
1	1	0	0.5	A
2	2	1	0.0	B
1	3	1	0.5	C
2	4	0	0.5	A
1	5	1	0.0	B
2	6	1	0.5	C
1	7	0	0.5	A
2	8	1	0.0	B
1	9	1	0.5	C

Join and separate together.

(-> DSm
    (tc/join-columns :joined [:V1 :V2 :V4] {:result-type :map})
    (tc/separate-column :joined [:v1 :v2 :v4] (juxt :V1 :V2 :V4)))

_unnamed [9 4]:

:V3	:v1	:v2	:v4
0.5	1	1	A
1.0	2	2	B
		3	C
1.5	1	4	A
0.5	2	5	B
1.0		6	C
	1	7	A
1.5	2	8	B
0.5		9	C

(-> DSm
    (tc/join-columns :joined [:V1 :V2 :V4] {:result-type :seq})
    (tc/separate-column :joined [:v1 :v2 :v4] identity))

_unnamed [9 4]:

:V3	:v1	:v2	:v4
0.5	1	1	A
1.0	2	2	B
		3	C
1.5	1	4	A
0.5	2	5	B
1.0		6	C
	1	7	A
1.5	2	8	B
0.5		9	C

Tidyr examples

separate source extract source

(def df-separate (tc/dataset {:x [nil "a.b" "a.d" "b.c"]}))
(def df-separate2 (tc/dataset {:x ["a" "a b" nil "a b c"]}))
(def df-separate3 (tc/dataset {:x ["a?b" nil "a.b" "b:c"]}))
(def df-extract (tc/dataset {:x [nil "a-b" "a-d" "b-c" "d-e"]}))

#'user/df-separate
#'user/df-separate2
#'user/df-separate3
#'user/df-extract

df-separate

_unnamed [4 1]:

:x

a.b
a.d
b.c

df-separate2

_unnamed [4 1]:

:x
a
a b

a b c

df-separate3

_unnamed [4 1]:

:x
a?b

a.b
b:c

df-extract

_unnamed [5 1]:

:x

a-b
a-d
b-c
d-e

(tc/separate-column df-separate :x [:A :B] "\\.")

_unnamed [4 2]:

:A	:B

a	b
a	d
b	c

You can drop columns after separation by setting nil as a name. We need second value here.

(tc/separate-column df-separate :x [nil :B] "\\.")

_unnamed [4 1]:

:B

b
d
c

Extra data is dropped

(tc/separate-column df-separate2 :x ["a" "b"] " ")

_unnamed [4 2]:

a	b
a
a	b

a	b

Split with regular expression

(tc/separate-column df-separate3 :x ["a" "b"] "[?\\.:]")

_unnamed [4 2]:

a	b
a	b

a	b
b	c

Or just regular expression to extract values

(tc/separate-column df-separate3 :x ["a" "b"] #"(.).(.)")

_unnamed [4 2]:

a	b
a	b

a	b
b	c

Extract first value only

(tc/separate-column df-extract :x ["A"] "-")

_unnamed [5 1]:

A

a
a
b
d

Split with regex

(tc/separate-column df-extract :x ["A" "B"] #"(\p{Alnum})-(\p{Alnum})")

_unnamed [5 2]:

A	B

a	b
a	d
b	c
d	e

Only a,b,c,d strings

(tc/separate-column df-extract :x ["A" "B"] #"([a-d]+)-([a-d]+)")

_unnamed [5 2]:

A	B

a	b
a	d
b	c

Array column conversion

A dataset can have as well columns of type java array. We can convert from normal columns to a single array column and back like this:

(-> (tc/dataset {:x [(double-array [1 2 3])
                     (double-array [4 5 6])]
                 :y [:a :b]})
    (tc/array-column->columns :x))

_unnamed [2 4]:

| :y |   0 |   1 |   2 |
|----|----:|----:|----:|
| :a | 1.0 | 2.0 | 3.0 |
| :b | 4.0 | 5.0 | 6.0 |

and the other way around:

(-> (tc/dataset {0 [0.0 1 2]
                 1 [3.0 4 5]
                 :x [:a :b :c]})
    (tc/columns->array-column [0 1] :y))

_unnamed [3 2]:

| :x |          :y |
|----|-------------|
| :a | [D@34f2577f |
| :b | [D@24e6d129 |
| :c | [D@59e24452 |

Fold/Unroll Rows

To pack or unpack the data into single value you can use fold-by and unroll functions.

fold-by groups dataset and packs columns data from each group separately into desired datastructure (like vector or sequence). unroll does the opposite.

Fold-by

Group-by and pack columns into vector

(tc/fold-by DS [:V3 :V4 :V1])

_unnamed [6 4]:

:V3	:V4	:V1	:V2
0.5	A	1	[1 7]
1.0	B	2	[2 8]
1.5	C	1	[3 9]
0.5	A	2	[4]
1.0	B	1	[5]
1.5	C	2	[6]

You can pack several columns at once.

(tc/fold-by DS [:V4])

_unnamed [3 4]:

:V4	:V1	:V2	:V3
A	[1 2 1]	[1 4 7]	[0.5 0.5 0.5]
B	[2 1 2]	[2 5 8]	[1.0 1.0 1.0]
C	[1 2 1]	[3 6 9]	[1.5 1.5 1.5]

You can use custom packing function

(tc/fold-by DS [:V4] seq)

_unnamed [3 4]:

:V4	:V1	:V2	:V3
A	(1 2 1)	(1 4 7)	(0.5 0.5 0.5)
B	(2 1 2)	(2 5 8)	(1.0 1.0 1.0)
C	(1 2 1)	(3 6 9)	(1.5 1.5 1.5)

(tc/fold-by DS [:V4] set)

_unnamed [3 4]:

:V4	:V1	:V2	:V3
A	#{1 2}	#{7 1 4}	#{0.5}
B	#{1 2}	#{2 5 8}	#{1.0}
C	#{1 2}	#{6 3 9}	#{1.5}

This works also on grouped dataset

(-> DS
    (tc/group-by :V1)
    (tc/fold-by :V4)
    (tc/ungroup))

_unnamed [6 4]:

:V4	:V1	:V2	:V3
A	[1 1]	[1 7]	[0.5 0.5]
C	[1 1]	[3 9]	[1.5 1.5]
B	[1]	[5]	[1.0]
B	[2 2]	[2 8]	[1.0 1.0]
A	[2]	[4]	[0.5]
C	[2]	[6]	[1.5]

Unroll

unroll unfolds sequences stored in data, multiplying other ones when necessary. You can unroll more than one column at once (folded data should have the same size!).

Options:

:indexes? if true (or column name), information about index of unrolled sequence is added.
:datatypes list of datatypes which should be applied to restored columns, a map

Unroll one column

(tc/unroll (tc/fold-by DS [:V4]) [:V1])

_unnamed [9 4]:

:V4	:V2	:V3	:V1
A	[1 4 7]	[0.5 0.5 0.5]	1
A	[1 4 7]	[0.5 0.5 0.5]	2
A	[1 4 7]	[0.5 0.5 0.5]	1
B	[2 5 8]	[1.0 1.0 1.0]	2
B	[2 5 8]	[1.0 1.0 1.0]	1
B	[2 5 8]	[1.0 1.0 1.0]	2
C	[3 6 9]	[1.5 1.5 1.5]	1
C	[3 6 9]	[1.5 1.5 1.5]	2
C	[3 6 9]	[1.5 1.5 1.5]	1

Unroll all folded columns

(tc/unroll (tc/fold-by DS [:V4]) [:V1 :V2 :V3])

_unnamed [9 4]:

:V4	:V1	:V2	:V3
A	1	1	0.5
A	2	4	0.5
A	1	7	0.5
B	2	2	1.0
B	1	5	1.0
B	2	8	1.0
C	1	3	1.5
C	2	6	1.5
C	1	9	1.5

Unroll one by one leads to cartesian product

(-> DS
    (tc/fold-by [:V4 :V1])
    (tc/unroll [:V2])
    (tc/unroll [:V3]))

_unnamed [15 4]:

:V4	:V1	:V2	:V3
A	1	1	0.5
A	1	1	0.5
A	1	7	0.5
A	1	7	0.5
B	2	2	1.0
B	2	2	1.0
B	2	8	1.0
B	2	8	1.0
C	1	3	1.5
C	1	3	1.5
C	1	9	1.5
C	1	9	1.5
A	2	4	0.5
B	1	5	1.0
C	2	6	1.5

You can add indexes

(tc/unroll (tc/fold-by DS [:V1]) [:V4 :V2 :V3] {:indexes? true})

_unnamed [9 5]:

:V1	:indexes	:V4	:V2	:V3
1	0	A	1	0.5
1	1	C	3	1.5
1	2	B	5	1.0
1	3	A	7	0.5
1	4	C	9	1.5
2	0	B	2	1.0
2	1	A	4	0.5
2	2	C	6	1.5
2	3	B	8	1.0

(tc/unroll (tc/fold-by DS [:V1]) [:V4 :V2 :V3] {:indexes? "vector idx"})

_unnamed [9 5]:

:V1	vector idx	:V4	:V2	:V3
1	0	A	1	0.5
1	1	C	3	1.5
1	2	B	5	1.0
1	3	A	7	0.5
1	4	C	9	1.5
2	0	B	2	1.0
2	1	A	4	0.5
2	2	C	6	1.5
2	3	B	8	1.0

You can also force datatypes

(-> DS
    (tc/fold-by [:V1])
    (tc/unroll [:V4 :V2 :V3] {:datatypes {:V4 :string
                                           :V2 :int16
                                           :V3 :float32}})
    (tc/info :columns))

_unnamed :column info [4 4]:

:name	:datatype	:n-elems	:categorical?
:V1	:int64	9
:V4	:string	9	true
:V2	:int16	9
:V3	:float32	9

This works also on grouped dataset

(-> DS
    (tc/group-by :V1)
    (tc/fold-by [:V1 :V4])
    (tc/unroll :V3 {:indexes? true})
    (tc/ungroup))

_unnamed [9 5]:

:V1	:V4	:V2	:indexes	:V3
1	A	[1 7]	0	0.5
1	A	[1 7]	1	0.5
1	C	[3 9]	0	1.5
1	C	[3 9]	1	1.5
1	B	[5]	0	1.0
2	B	[2 8]	0	1.0
2	B	[2 8]	1	1.0
2	A	[4]	0	0.5
2	C	[6]	0	1.5

Reshape

Reshaping data provides two types of operations:

pivot->longer - converting columns to rows
pivot->wider - converting rows to columns

Both functions are inspired on tidyr R package and provide almost the same functionality.

All examples are taken from mentioned above documentation.

Both functions work only on regular dataset.

Longer

pivot->longer converts columns to rows. Column names are treated as data.

Arguments:

dataset
columns selector
options:
- :target-columns - names of the columns created or columns pattern (see below) (default: :$column)
- :value-column-name - name of the column for values (default: :$value)
- :splitter - string, regular expression or function which splits source column names into data
- :drop-missing? - remove rows with missing? (default: true)
- :datatypes - map of target columns data types
- :coerce-to-number - try to convert extracted values to numbers if possible (default: true)

:target-columns - can be:

column name - source columns names are put there as a data
column names as seqence - source columns names after split are put separately into :target-columns as data
pattern - is a sequence of names, where some of the names are nil. nil is replaced by a name taken from splitter and such column is used for values.

Create rows from all columns but "religion".

(def relig-income (tc/dataset "data/relig_income.csv"))

relig-income

data/relig_income.csv [18 11]:

religion	<$10k	$10-20k	$20-30k	$30-40k	$40-50k	$50-75k	$75-100k	$100-150k	>150k	Don’t know/refused
Agnostic	27	34	60	81	76	137	122	109	84	96
Atheist	12	27	37	52	35	70	73	59	74	76
Buddhist	27	21	30	34	33	58	62	39	53	54
Catholic	418	617	732	670	638	1116	949	792	633	1489
Don’t know/refused	15	14	15	11	10	35	21	17	18	116
Evangelical Prot	575	869	1064	982	881	1486	949	723	414	1529
Hindu	1	9	7	9	11	34	47	48	54	37
Historically Black Prot	228	244	236	238	197	223	131	81	78	339
Jehovah’s Witness	20	27	24	24	21	30	15	11	6	37
Jewish	19	19	25	25	30	95	69	87	151	162
Mainline Prot	289	495	619	655	651	1107	939	753	634	1328
Mormon	29	40	48	51	56	112	85	49	42	69
Muslim	6	7	9	10	9	23	16	8	6	22
Orthodox	13	17	23	32	32	47	38	42	46	73
Other Christian	9	7	11	13	13	14	18	14	12	18
Other Faiths	20	33	40	46	49	63	46	40	41	71
Other World Religions	5	2	3	4	2	7	3	4	4	8
Unaffiliated	217	299	374	365	341	528	407	321	258	597

(tc/pivot->longer relig-income (complement #{"religion"}))

data/relig_income.csv [180 3]:

religion	:$column \| :$value
Agnostic	<$10k	27
Atheist	<$10k	12
Buddhist	<$10k	27
Catholic	<$10k	418
Don’t know/refused	<$10k	15
Evangelical Prot	<$10k	575
Hindu	<$10k	1
Historically Black Prot	<$10k	228
Jehovah’s Witness	<$10k	20
Jewish	<$10k	19
…	…	…
Historically Black Prot	>150k	78
Jehovah’s Witness	>150k	6
Jewish	>150k	151
Mainline Prot	>150k	634
Mormon	>150k	42
Muslim	>150k	6
Orthodox	>150k	46
Other Christian	>150k	12
Other Faiths	>150k	41
Other World Religions	>150k	4
Unaffiliated	>150k	258

Convert only columns starting with "wk" and pack them into :week column, values go to :rank column

(def bilboard (-> (tc/dataset "data/billboard.csv.gz")
                  (tc/drop-columns :type/boolean))) ;; drop some boolean columns, tidyr just skips them

(->> bilboard
     (tc/column-names)
     (take 13)
     (tc/select-columns bilboard))

data/billboard.csv.gz [317 13]:

artist	track	date.entered	wk1	wk2	wk3	wk4	wk5	wk6	wk7	wk8	wk9	wk10
2 Pac	Baby Don’t Cry (Keep…	2000-02-26	87	82	72	77	87	94	99
2Ge+her	The Hardest Part Of …	2000-09-02	91	87	92
3 Doors Down	Kryptonite	2000-04-08	81	70	68	67	66	57	54	53	51	51
3 Doors Down	Loser	2000-10-21	76	76	72	69	67	65	55	59	62	61
504 Boyz	Wobble Wobble	2000-04-15	57	34	25	17	17	31	36	49	53	57
98^0	Give Me Just One Nig…	2000-08-19	51	39	34	26	26	19	2	2	3	6
A*Teens	Dancing Queen	2000-07-08	97	97	96	95	100
Aaliyah	I Don’t Wanna	2000-01-29	84	62	51	41	38	35	35	38	38	36
Aaliyah	Try Again	2000-03-18	59	53	38	28	21	18	16	14	12	10
Adams, Yolanda	Open My Heart	2000-08-26	76	76	74	69	68	67	61	58	57	59
…	…	…	…	…	…	…	…	…	…	…	…	…
Wallflowers, The	Sleepwalker	2000-10-28	73	73	74	80	90	96
Westlife	Swear It Again	2000-04-01	96	82	66	55	55	46	44	44	37	35
Williams, Robbie	Angels	1999-11-20	85	77	69	69	62	56	56	64	54	53
Wills, Mark	Back At One	2000-01-15	89	55	51	43	37	37	36	39	42	46
Worley, Darryl	When You Need My Lov…	2000-06-17	98	88	93	92	85	85	84	80	80	80
Wright, Chely	It Was	2000-03-04	86	78	75	72	71	69	64	75	85	98
Yankee Grey	Another Nine Minutes	2000-04-29	86	83	77	74	83	79	88	95
Yearwood, Trisha	Real Live Woman	2000-04-01	85	83	83	82	81	91
Ying Yang Twins	Whistle While You Tw…	2000-03-18	95	94	91	85	84	78	74	78	85	89
Zombie Nation	Kernkraft 400	2000-09-02	99	99
matchbox twenty	Bent	2000-04-29	60	37	29	24	22	21	18	16	13	12

(tc/pivot->longer bilboard #(clojure.string/starts-with? % "wk") {:target-columns :week
                                                                   :value-column-name :rank})

data/billboard.csv.gz [5307 5]:

artist	track	date.entered	:week	:rank
3 Doors Down	Kryptonite	2000-04-08	wk35	4
Braxton, Toni	He Wasn’t Man Enough	2000-03-18	wk35	34
Creed	Higher	1999-09-11	wk35	22
Creed	With Arms Wide Open	2000-05-13	wk35	5
Hill, Faith	Breathe	1999-11-06	wk35	8
Joe	I Wanna Know	2000-01-01	wk35	5
Lonestar	Amazed	1999-06-05	wk35	14
Vertical Horizon	Everything You Want	2000-01-22	wk35	27
matchbox twenty	Bent	2000-04-29	wk35	33
Creed	Higher	1999-09-11	wk55	21
…	…	…	…	…
Savage Garden	I Knew I Loved You	1999-10-23	wk24	12
Sisqo	Incomplete	2000-06-24	wk24	31
Sisqo	Thong Song	2000-01-29	wk24	17
Smash Mouth	Then The Morning Com…	1999-10-30	wk24	35
Son By Four	A Puro Dolor (Purest…	2000-04-08	wk24	32
Sonique	It Feels So Good	2000-01-22	wk24	49
SoulDecision	Faded	2000-07-08	wk24	50
Sting	Desert Rose	2000-05-13	wk24	45
Train	Meet Virginia	1999-10-09	wk24	42
Vertical Horizon	Everything You Want	2000-01-22	wk24	6
matchbox twenty	Bent	2000-04-29	wk24	9

We can create numerical column out of column names

(tc/pivot->longer bilboard #(clojure.string/starts-with? % "wk") {:target-columns :week
                                                                   :value-column-name :rank
                                                                   :splitter #"wk(.*)"
                                                                   :datatypes {:week :int16}})

data/billboard.csv.gz [5307 5]:

artist	track	date.entered	:week	:rank
3 Doors Down	Kryptonite	2000-04-08	46	21
Creed	Higher	1999-09-11	46	7
Creed	With Arms Wide Open	2000-05-13	46	37
Hill, Faith	Breathe	1999-11-06	46	31
Lonestar	Amazed	1999-06-05	46	5
3 Doors Down	Kryptonite	2000-04-08	51	42
Creed	Higher	1999-09-11	51	14
Hill, Faith	Breathe	1999-11-06	51	49
Lonestar	Amazed	1999-06-05	51	12
2 Pac	Baby Don’t Cry (Keep…	2000-02-26	6	94
…	…	…	…	…
matchbox twenty	Bent	2000-04-29	5	22
3 Doors Down	Kryptonite	2000-04-08	34	3
Braxton, Toni	He Wasn’t Man Enough	2000-03-18	34	33
Creed	Higher	1999-09-11	34	23
Creed	With Arms Wide Open	2000-05-13	34	5
Hill, Faith	Breathe	1999-11-06	34	5
Joe	I Wanna Know	2000-01-01	34	8
Lonestar	Amazed	1999-06-05	34	17
Nelly	(Hot S**t) Country G…	2000-04-29	34	49
Vertical Horizon	Everything You Want	2000-01-22	34	20
matchbox twenty	Bent	2000-04-29	34	30

When column names contain observation data, such column names can be splitted and data can be restored into separate columns.

(def who (tc/dataset "data/who.csv.gz"))

(->> who
     (tc/column-names)
     (take 10)
     (tc/select-columns who))

data/who.csv.gz [7240 10]:

country	iso2	iso3	year	new_sp_m014	new_sp_m1524	new_sp_m2534	new_sp_m3544	new_sp_m4554	new_sp_m5564
Afghanistan	AF	AFG	1980
Afghanistan	AF	AFG	1981
Afghanistan	AF	AFG	1982
Afghanistan	AF	AFG	1983
Afghanistan	AF	AFG	1984
Afghanistan	AF	AFG	1985
Afghanistan	AF	AFG	1986
Afghanistan	AF	AFG	1987
Afghanistan	AF	AFG	1988
Afghanistan	AF	AFG	1989
…	…	…	…	…	…	…	…	…	…
Zimbabwe	ZW	ZWE	2003	133	874	3048	2228	981	367
Zimbabwe	ZW	ZWE	2004	187	833	2908	2298	1056	366
Zimbabwe	ZW	ZWE	2005	210	837	2264	1855	762	295
Zimbabwe	ZW	ZWE	2006	215	736	2391	1939	896	348
Zimbabwe	ZW	ZWE	2007	138	500	3693	0	716	292
Zimbabwe	ZW	ZWE	2008	127	614	0	3316	704	263
Zimbabwe	ZW	ZWE	2009	125	578		3471	681	293
Zimbabwe	ZW	ZWE	2010	150	710	2208	1682	761	350
Zimbabwe	ZW	ZWE	2011	152	784	2467	2071	780	377
Zimbabwe	ZW	ZWE	2012	120	783	2421	2086	796	360
Zimbabwe	ZW	ZWE	2013

(tc/pivot->longer who #(clojure.string/starts-with? % "new") {:target-columns [:diagnosis :gender :age]
                                                               :splitter #"new_?(.*)_(.)(.*)"
                                                               :value-column-name :count})

data/who.csv.gz [76046 8]:

country	iso2	iso3	year	:diagnosis	:gender	:age	:count
Albania	AL	ALB	2013	rel	m	1524	60
Algeria	DZ	DZA	2013	rel	m	1524	1021
Andorra	AD	AND	2013	rel	m	1524	0
Angola	AO	AGO	2013	rel	m	1524	2992
Anguilla	AI	AIA	2013	rel	m	1524	0
Antigua and Barbuda	AG	ATG	2013	rel	m	1524	1
Argentina	AR	ARG	2013	rel	m	1524	1124
Armenia	AM	ARM	2013	rel	m	1524	116
Australia	AU	AUS	2013	rel	m	1524	105
Austria	AT	AUT	2013	rel	m	1524	44
…	…	…	…	…	…	…	…
United Arab Emirates	AE	ARE	2013	rel	m	2534	9
United Kingdom of Great Britain and Northern Ireland	GB	GBR	2013	rel	m	2534	1158
United States of America	US	USA	2013	rel	m	2534	829
Uruguay	UY	URY	2013	rel	m	2534	142
Uzbekistan	UZ	UZB	2013	rel	m	2534	2371
Vanuatu	VU	VUT	2013	rel	m	2534	9
Venezuela (Bolivarian Republic of)	VE	VEN	2013	rel	m	2534	739
Viet Nam	VN	VNM	2013	rel	m	2534	6302
Yemen	YE	YEM	2013	rel	m	2534	1113
Zambia	ZM	ZMB	2013	rel	m	2534	7808
Zimbabwe	ZW	ZWE	2013	rel	m	2534	5331

When data contains multiple observations per row, we can use splitter and pattern for target columns to create new columns and put values there. In following dataset we have two obseravations dob and gender for two childs. We want to put child infomation into the column and leave dob and gender for values.

(def family (tc/dataset "data/family.csv"))

family

data/family.csv [5 5]:

family	dob_child1	dob_child2	gender_child1	gender_child2
1	1998-11-26	2000-01-29	1	2
2	1996-06-22		2
3	2002-07-11	2004-04-05	2	2
4	2004-10-10	2009-08-27	1	1
5	2000-12-05	2005-02-28	2	1

(tc/pivot->longer family (complement #{"family"}) {:target-columns [nil :child]
                                                    :splitter "_"
                                                    :datatypes {"gender" :int16}})

data/family.csv [9 4]:

family	:child	dob	gender
1	child1	1998-11-26	1
2	child1	1996-06-22	2
3	child1	2002-07-11	2
4	child1	2004-10-10	1
5	child1	2000-12-05	2
1	child2	2000-01-29	2
3	child2	2004-04-05	2
4	child2	2009-08-27	1
5	child2	2005-02-28	1

Similar here, we have two observations: x and y in four groups.

(def anscombe (tc/dataset "data/anscombe.csv"))

anscombe

data/anscombe.csv [11 8]:

x1	x2	x3	x4	y1	y2	y3	y4
10	10	10	8	8.04	9.14	7.46	6.58
8	8	8	8	6.95	8.14	6.77	5.76
13	13	13	8	7.58	8.74	12.74	7.71
9	9	9	8	8.81	8.77	7.11	8.84
11	11	11	8	8.33	9.26	7.81	8.47
14	14	14	8	9.96	8.10	8.84	7.04
6	6	6	8	7.24	6.13	6.08	5.25
4	4	4	19	4.26	3.10	5.39	12.50
12	12	12	8	10.84	9.13	8.15	5.56
7	7	7	8	4.82	7.26	6.42	7.91
5	5	5	8	5.68	4.74	5.73	6.89

(tc/pivot->longer anscombe :all {:splitter #"(.)(.)"
                                  :target-columns [nil :set]})

data/anscombe.csv [44 3]:

:set	x	y
1	10	8.04
1	8	6.95
1	13	7.58
1	9	8.81
1	11	8.33
1	14	9.96
1	6	7.24
1	4	4.26
1	12	10.84
1	7	4.82
…	…	…
4	8	6.58
4	8	5.76
4	8	7.71
4	8	8.84
4	8	8.47
4	8	7.04
4	8	5.25
4	19	12.50
4	8	5.56
4	8	7.91
4	8	6.89

(def pnl (tc/dataset {:x [1 2 3 4]
                       :a [1 1 0 0]
                       :b [0 1 1 1]
                       :y1 (repeatedly 4 rand)
                       :y2 (repeatedly 4 rand)
                       :z1 [3 3 3 3]
                       :z2 [-2 -2 -2 -2]}))

pnl

_unnamed [4 7]:

:x	:a	:b	:y1	:y2	:z1	:z2
1	1	0	0.68958721	0.32578532	3	-2
2	1	1	0.14852797	0.67285016	3	-2
3	0	1	0.47621731	0.08316294	3	-2
4	0	1	0.08537188	0.85794979	3	-2

(tc/pivot->longer pnl [:y1 :y2 :z1 :z2] {:target-columns [nil :times]
                                          :splitter #":(.)(.)"})

_unnamed [8 6]:

:x	:a	:b	:times	y	z
1	1	0	1	0.68958721	3
2	1	1	1	0.14852797	3
3	0	1	1	0.47621731	3
4	0	1	1	0.08537188	3
1	1	0	2	0.32578532	-2
2	1	1	2	0.67285016	-2
3	0	1	2	0.08316294	-2
4	0	1	2	0.85794979	-2

Wider

pivot->wider converts rows to columns.

Arguments:

dataset
columns-selector - values from selected columns are converted to new columns
value-columns - what are values

When multiple columns are used as columns selector, names are joined using :concat-columns-with option. :concat-columns-with can be a string or function (default: “_“). Function accepts sequence of names.

When columns-selector creates non unique set of values, they are folded using :fold-fn (default: vec) option.

When value-columns is a sequence, multiple observations as columns are created appending value column names into new columns. Column names are joined using :concat-value-with option. :concat-value-with can be a string or function (default: “-”). Function accepts current column name and value.

Use station as a name source for columns and seen for values

(def fish (tc/dataset "data/fish_encounters.csv"))

fish

data/fish_encounters.csv [114 3]:

fish	station	seen
4842	Release	1
4842	I80_1	1
4842	Lisbon	1
4842	Rstr	1
4842	Base_TD	1
4842	BCE	1
4842	BCW	1
4842	BCE2	1
4842	BCW2	1
4842	MAE	1
…	…	…
4862	BCE	1
4862	BCW	1
4862	BCE2	1
4862	BCW2	1
4863	Release	1
4863	I80_1	1
4864	Release	1
4864	I80_1	1
4865	Release	1
4865	I80_1	1
4865	Lisbon	1

(tc/pivot->wider fish "station" "seen" {:drop-missing? false})

data/fish_encounters.csv [19 12]:

fish	Release	I80_1	Lisbon	Rstr	Base_TD	BCE	BCW	BCE2	BCW2	MAE	MAW
4842	1	1	1	1	1	1	1	1	1	1	1
4843	1	1	1	1	1	1	1	1	1	1	1
4844	1	1	1	1	1	1	1	1	1	1	1
4858	1	1	1	1	1	1	1	1	1	1	1
4861	1	1	1	1	1	1	1	1	1	1	1
4857	1	1	1	1	1	1	1	1	1
4862	1	1	1	1	1	1	1	1	1
4850	1	1		1	1	1	1
4845	1	1	1	1	1
4855	1	1	1	1	1
4859	1	1	1	1	1
4848	1	1	1	1
4847	1	1	1
4865	1	1	1
4849	1	1
4851	1	1
4854	1	1
4863	1	1
4864	1	1

If selected columns contain multiple values, such values should be folded.

(def warpbreaks (tc/dataset "data/warpbreaks.csv"))

warpbreaks

data/warpbreaks.csv [54 3]:

breaks	wool	tension
26	A	L
30	A	L
54	A	L
25	A	L
70	A	L
52	A	L
51	A	L
26	A	L
67	A	L
18	A	M
…	…	…
39	B	M
29	B	M
20	B	H
21	B	H
24	B	H
17	B	H
13	B	H
15	B	H
15	B	H
16	B	H
28	B	H

Let’s see how many values are for each type of wool and tension groups

(-> warpbreaks
    (tc/group-by ["wool" "tension"])
    (tc/aggregate {:n tc/row-count}))

_unnamed [6 3]:

wool	tension	:n
A	L	9
A	M	9
A	H	9
B	L	9
B	M	9
B	H	9

(-> warpbreaks
    (tc/reorder-columns ["wool" "tension" "breaks"])
    (tc/pivot->wider "wool" "breaks" {:fold-fn vec}))

data/warpbreaks.csv [3 3]:

tension	A	B
L	[26 30 54 25 70 52 51 26 67]	[27 14 29 19 29 31 41 20 44]
M	[18 21 29 17 12 18 35 30 36]	[42 26 19 16 39 28 21 39 29]
H	[36 21 24 18 10 43 28 15 26]	[20 21 24 17 13 15 15 16 28]

We can also calculate mean (aggreate values)

(-> warpbreaks
    (tc/reorder-columns ["wool" "tension" "breaks"])
    (tc/pivot->wider "wool" "breaks" {:fold-fn tech.v3.datatype.functional/mean}))

data/warpbreaks.csv [3 3]:

tension	A	B
L	44.55555556	28.22222222
M	24.00000000	28.77777778
H	24.55555556	18.77777778

Multiple source columns, joined with default separator.

(def production (tc/dataset "data/production.csv"))

production

data/production.csv [45 4]:

product	country	year	production
A	AI	2000	1.63727158
A	AI	2001	0.15870784
A	AI	2002	-1.56797745
A	AI	2003	-0.44455509
A	AI	2004	-0.07133701
A	AI	2005	1.61183090
A	AI	2006	-0.70434682
A	AI	2007	-1.53550542
A	AI	2008	0.83907155
A	AI	2009	-0.37424110
…	…	…	…
B	EI	2004	0.62564999
B	EI	2005	-1.34530299
B	EI	2006	-0.97184975
B	EI	2007	-1.69715821
B	EI	2008	0.04556128
B	EI	2009	1.19315043
B	EI	2010	-1.60557503
B	EI	2011	-0.77235497
B	EI	2012	-2.50262738
B	EI	2013	-1.62753769
B	EI	2014	0.03329645

(tc/pivot->wider production ["product" "country"] "production")

data/production.csv [15 4]:

year	A_AI	B_AI	B_EI
2000	1.63727158	-0.02617661	1.40470848
2001	0.15870784	-0.68863576	-0.59618369
2002	-1.56797745	0.06248741	-0.26568579
2003	-0.44455509	-0.72339686	0.65257808
2004	-0.07133701	0.47248952	0.62564999
2005	1.61183090	-0.94173861	-1.34530299
2006	-0.70434682	-0.34782108	-0.97184975
2007	-1.53550542	0.52425284	-1.69715821
2008	0.83907155	1.83230937	0.04556128
2009	-0.37424110	0.10706491	1.19315043
2010	-0.71158926	-0.32903664	-1.60557503
2011	1.12805634	-1.78319121	-0.77235497
2012	1.45718247	0.61125798	-2.50262738
2013	-1.55934101	-0.78526092	-1.62753769
2014	-0.11695838	0.97843635	0.03329645

Joined with custom function

(tc/pivot->wider production ["product" "country"] "production" {:concat-columns-with vec})

data/production.csv [15 4]:

year	[“A” “AI”]	[“B” “AI”]	[“B” “EI”]
2000	1.63727158	-0.02617661	1.40470848
2001	0.15870784	-0.68863576	-0.59618369
2002	-1.56797745	0.06248741	-0.26568579
2003	-0.44455509	-0.72339686	0.65257808
2004	-0.07133701	0.47248952	0.62564999
2005	1.61183090	-0.94173861	-1.34530299
2006	-0.70434682	-0.34782108	-0.97184975
2007	-1.53550542	0.52425284	-1.69715821
2008	0.83907155	1.83230937	0.04556128
2009	-0.37424110	0.10706491	1.19315043
2010	-0.71158926	-0.32903664	-1.60557503
2011	1.12805634	-1.78319121	-0.77235497
2012	1.45718247	0.61125798	-2.50262738
2013	-1.55934101	-0.78526092	-1.62753769
2014	-0.11695838	0.97843635	0.03329645

Multiple value columns

(def income (tc/dataset "data/us_rent_income.csv"))

income

data/us_rent_income.csv [104 5]:

GEOID	NAME	variable	estimate	moe
1	Alabama	income	24476	136
1	Alabama	rent	747	3
2	Alaska	income	32940	508
2	Alaska	rent	1200	13
4	Arizona	income	27517	148
4	Arizona	rent	972	4
5	Arkansas	income	23789	165
5	Arkansas	rent	709	5
6	California	income	29454	109
6	California	rent	1358	3
…	…	…	…	…
51	Virginia	rent	1166	5
53	Washington	income	32318	113
53	Washington	rent	1120	4
54	West Virginia	income	23707	203
54	West Virginia	rent	681	6
55	Wisconsin	income	29868	135
55	Wisconsin	rent	813	3
56	Wyoming	income	30854	342
56	Wyoming	rent	828	11
72	Puerto Rico	income
72	Puerto Rico	rent	464	6

(tc/pivot->wider income "variable" ["estimate" "moe"] {:drop-missing? false})

data/us_rent_income.csv [52 6]:

GEOID	NAME	income-estimate	income-moe	rent-estimate	rent-moe
1	Alabama	24476	136	747	3
2	Alaska	32940	508	1200	13
4	Arizona	27517	148	972	4
5	Arkansas	23789	165	709	5
6	California	29454	109	1358	3
8	Colorado	32401	109	1125	5
9	Connecticut	35326	195	1123	5
10	Delaware	31560	247	1076	10
11	District of Columbia	43198	681	1424	17
12	Florida	25952	70	1077	3
…	…	…	…	…	…
46	South Dakota	28821	276	696	7
47	Tennessee	25453	102	808	4
48	Texas	28063	110	952	2
49	Utah	27928	239	948	6
50	Vermont	29351	361	945	11
51	Virginia	32545	202	1166	5
53	Washington	32318	113	1120	4
54	West Virginia	23707	203	681	6
55	Wisconsin	29868	135	813	3
56	Wyoming	30854	342	828	11
72	Puerto Rico			464	6

Value concatenated by custom function

(tc/pivot->wider income "variable" ["estimate" "moe"] {:concat-columns-with vec
                                                        :concat-value-with vector
                                                        :drop-missing? false})

data/us_rent_income.csv [52 6]:

GEOID	NAME	[“income” “estimate”]	[“income” “moe”]	[“rent” “estimate”]	[“rent” “moe”]
1	Alabama	24476	136	747	3
2	Alaska	32940	508	1200	13
4	Arizona	27517	148	972	4
5	Arkansas	23789	165	709	5
6	California	29454	109	1358	3
8	Colorado	32401	109	1125	5
9	Connecticut	35326	195	1123	5
10	Delaware	31560	247	1076	10
11	District of Columbia	43198	681	1424	17
12	Florida	25952	70	1077	3
…	…	…	…	…	…
46	South Dakota	28821	276	696	7
47	Tennessee	25453	102	808	4
48	Texas	28063	110	952	2
49	Utah	27928	239	948	6
50	Vermont	29351	361	945	11
51	Virginia	32545	202	1166	5
53	Washington	32318	113	1120	4
54	West Virginia	23707	203	681	6
55	Wisconsin	29868	135	813	3
56	Wyoming	30854	342	828	11
72	Puerto Rico			464	6

Reshape contact data

(def contacts (tc/dataset "data/contacts.csv"))

contacts

data/contacts.csv [6 3]:

field	value	person_id
name	Jiena McLellan	1
company	Toyota	1
name	John Smith	2
company	google	2
email	john@google.com	2
name	Huxley Ratcliffe	3

(tc/pivot->wider contacts "field" "value" {:drop-missing? false})

data/contacts.csv [3 4]:

person_id	name	company	email
2	John Smith	google	john@google.com
1	Jiena McLellan	Toyota
3	Huxley Ratcliffe

Reshaping

A couple of tidyr examples of more complex reshaping.

World bank

(def world-bank-pop (tc/dataset "data/world_bank_pop.csv.gz"))

(->> world-bank-pop
     (tc/column-names)
     (take 8)
     (tc/select-columns world-bank-pop))

data/world_bank_pop.csv.gz [1056 8]:

country	indicator	2000	2001	2002	2003	2004	2005
ABW	SP.URB.TOTL	4.24440000E+04	4.30480000E+04	4.36700000E+04	4.42460000E+04	4.46690000E+04	4.48890000E+04
ABW	SP.URB.GROW	1.18263237E+00	1.41302122E+00	1.43455953E+00	1.31036044E+00	9.51477684E-01	4.91302715E-01
ABW	SP.POP.TOTL	9.08530000E+04	9.28980000E+04	9.49920000E+04	9.70170000E+04	9.87370000E+04	1.00031000E+05
ABW	SP.POP.GROW	2.05502678E+00	2.22593013E+00	2.22905605E+00	2.10935434E+00	1.75735287E+00	1.30203884E+00
AFG	SP.URB.TOTL	4.43629900E+06	4.64805500E+06	4.89295100E+06	5.15568600E+06	5.42677000E+06	5.69182300E+06
AFG	SP.URB.GROW	3.91222846E+00	4.66283822E+00	5.13467454E+00	5.23045853E+00	5.12439302E+00	4.76864700E+00
AFG	SP.POP.TOTL	2.00937560E+07	2.09664630E+07	2.19799230E+07	2.30648510E+07	2.41189790E+07	2.50707980E+07
AFG	SP.POP.GROW	3.49465874E+00	4.25150411E+00	4.72052846E+00	4.81804112E+00	4.46891840E+00	3.87047016E+00
AGO	SP.URB.TOTL	8.23476600E+06	8.70800000E+06	9.21878700E+06	9.76519700E+06	1.03435060E+07	1.09494240E+07
AGO	SP.URB.GROW	5.43749411E+00	5.58771954E+00	5.70013237E+00	5.75812711E+00	5.75341450E+00	5.69279690E+00
…	…	…	…	…	…	…	…
ZAF	SP.URB.GROW	2.32229180E+00	2.26080492E+00	2.29242659E+00	2.25719919E+00	2.18014731E+00	2.09725981E+00
ZAF	SP.POP.TOTL	4.57283150E+07	4.63850060E+07	4.70261730E+07	4.76487270E+07	4.82473950E+07	4.88205860E+07
ZAF	SP.POP.GROW	1.47499416E+00	1.42585702E+00	1.37280586E+00	1.31515951E+00	1.24859226E+00	1.18102315E+00
ZMB	SP.URB.TOTL	3.66507600E+06	3.78866000E+06	3.94496500E+06	4.10631700E+06	4.27387500E+06	4.44857100E+06
ZMB	SP.URB.GROW	1.50532147E+00	3.31633227E+00	4.04276877E+00	4.00864374E+00	3.99943902E+00	4.00620111E+00
ZMB	SP.POP.TOTL	1.05312210E+07	1.08241250E+07	1.11204090E+07	1.14219840E+07	1.17317460E+07	1.20521560E+07
ZMB	SP.POP.GROW	2.80705843E+00	2.74331654E+00	2.70046295E+00	2.67578507E+00	2.67585813E+00	2.69450644E+00
ZWE	SP.URB.TOTL	4.12598700E+06	4.22551900E+06	4.32330700E+06	4.35604100E+06	4.38192000E+06	4.41384500E+06
ZWE	SP.URB.GROW	2.52373518E+00	2.38368296E+00	2.28785252E+00	7.54299867E-01	5.92336717E-01	7.25920717E-01
ZWE	SP.POP.TOTL	1.22222510E+07	1.23661650E+07	1.25005250E+07	1.26338970E+07	1.27775110E+07	1.29400320E+07
ZWE	SP.POP.GROW	1.29878201E+00	1.17059711E+00	1.08065293E+00	1.06127964E+00	1.13032327E+00	1.26390895E+00

Step 1 - convert years column into values

(def pop2 (tc/pivot->longer world-bank-pop (map str (range 2000 2018)) {:drop-missing? false
                                                                         :target-columns ["year"]
                                                                         :value-column-name "value"}))

pop2

data/world_bank_pop.csv.gz [19008 4]:

country	indicator	year	value
ABW	SP.URB.TOTL	2013	4.43600000E+04
ABW	SP.URB.GROW	2013	6.69503994E-01
ABW	SP.POP.TOTL	2013	1.03187000E+05
ABW	SP.POP.GROW	2013	5.92914005E-01
AFG	SP.URB.TOTL	2013	7.73396400E+06
AFG	SP.URB.GROW	2013	4.19297967E+00
AFG	SP.POP.TOTL	2013	3.17316880E+07
AFG	SP.POP.GROW	2013	3.31522413E+00
AGO	SP.URB.TOTL	2013	1.61194910E+07
AGO	SP.URB.GROW	2013	4.72272270E+00
…	…	…	…
ZAF	SP.URB.GROW	2012	2.23077040E+00
ZAF	SP.POP.TOTL	2012	5.29982130E+07
ZAF	SP.POP.GROW	2012	1.39596592E+00
ZMB	SP.URB.TOTL	2012	5.93201300E+06
ZMB	SP.URB.GROW	2012	4.25944078E+00
ZMB	SP.POP.TOTL	2012	1.46999370E+07
ZMB	SP.POP.GROW	2012	3.00513283E+00
ZWE	SP.URB.TOTL	2012	4.83015300E+06
ZWE	SP.URB.GROW	2012	1.67857380E+00
ZWE	SP.POP.TOTL	2012	1.47108260E+07
ZWE	SP.POP.GROW	2012	2.22830616E+00

Step 2 - separate "indicate" column

(def pop3 (tc/separate-column pop2
                               "indicator" ["area" "variable"]
                               #(rest (clojure.string/split % #"\."))))

pop3

data/world_bank_pop.csv.gz [19008 5]:

country	area	variable	year	value
ABW	URB	TOTL	2013	4.43600000E+04
ABW	URB	GROW	2013	6.69503994E-01
ABW	POP	TOTL	2013	1.03187000E+05
ABW	POP	GROW	2013	5.92914005E-01
AFG	URB	TOTL	2013	7.73396400E+06
AFG	URB	GROW	2013	4.19297967E+00
AFG	POP	TOTL	2013	3.17316880E+07
AFG	POP	GROW	2013	3.31522413E+00
AGO	URB	TOTL	2013	1.61194910E+07
AGO	URB	GROW	2013	4.72272270E+00
…	…	…	…	…
ZAF	URB	GROW	2012	2.23077040E+00
ZAF	POP	TOTL	2012	5.29982130E+07
ZAF	POP	GROW	2012	1.39596592E+00
ZMB	URB	TOTL	2012	5.93201300E+06
ZMB	URB	GROW	2012	4.25944078E+00
ZMB	POP	TOTL	2012	1.46999370E+07
ZMB	POP	GROW	2012	3.00513283E+00
ZWE	URB	TOTL	2012	4.83015300E+06
ZWE	URB	GROW	2012	1.67857380E+00
ZWE	POP	TOTL	2012	1.47108260E+07
ZWE	POP	GROW	2012	2.22830616E+00

Step 3 - Make columns based on "variable" values.

(tc/pivot->wider pop3 "variable" "value" {:drop-missing? false})

data/world_bank_pop.csv.gz [9504 5]:

country	area	year	TOTL	GROW
ABW	URB	2013	4.43600000E+04	0.66950399
ABW	POP	2013	1.03187000E+05	0.59291401
AFG	URB	2013	7.73396400E+06	4.19297967
AFG	POP	2013	3.17316880E+07	3.31522413
AGO	URB	2013	1.61194910E+07	4.72272270
AGO	POP	2013	2.59983400E+07	3.53182419
ALB	URB	2013	1.60350500E+06	1.74363937
ALB	POP	2013	2.89509200E+06	-0.18321138
AND	URB	2013	7.15270000E+04	-2.11923331
AND	POP	2013	8.07880000E+04	-2.01331401
…	…	…	…	…
WSM	POP	2012	1.89194000E+05	0.81144852
XKX	URB	2012
XKX	POP	2012	1.80520000E+06	0.78972659
YEM	URB	2012	8.20982800E+06	4.49478765
YEM	POP	2012	2.49099690E+07	2.67605025
ZAF	URB	2012	3.35330290E+07	2.23077040
ZAF	POP	2012	5.29982130E+07	1.39596592
ZMB	URB	2012	5.93201300E+06	4.25944078
ZMB	POP	2012	1.46999370E+07	3.00513283
ZWE	URB	2012	4.83015300E+06	1.67857380
ZWE	POP	2012	1.47108260E+07	2.22830616

Multi-choice

(def multi (tc/dataset {:id [1 2 3 4]
                         :choice1 ["A" "C" "D" "B"]
                         :choice2 ["B" "B" nil "D"]
                         :choice3 ["C" nil nil nil]}))

multi

_unnamed [4 4]:

:id	:choice1	:choice2	:choice3
1	A	B	C
2	C	B
3	D
4	B	D

Step 1 - convert all choices into rows and add artificial column to all values which are not missing.

(def multi2 (-> multi
                (tc/pivot->longer (complement #{:id}))
                (tc/add-column :checked true)))

multi2

_unnamed [8 4]:

:id	:$column \| :$value	:checked
1	:choice1	A	true
2	:choice1	C	true
3	:choice1	D	true
4	:choice1	B	true
1	:choice2	B	true
2	:choice2	B	true
4	:choice2	D	true
1	:choice3	C	true

Step 2 - Convert back to wide form with actual choices as columns

(-> multi2
    (tc/drop-columns :$column)
    (tc/pivot->wider :$value :checked {:drop-missing? false})
    (tc/order-by :id))

_unnamed [4 5]:

:id	A	C	D	B
1	true	true		true
2		true		true
3			true
4			true	true

Construction

(def construction (tc/dataset "data/construction.csv"))
(def construction-unit-map {"1 unit" "1"
                            "2 to 4 units" "2-4"
                            "5 units or more" "5+"})

construction

data/construction.csv [9 9]:

Year	Month	1 unit	5 units or more	Northeast	Midwest	South	West
2018	January	859	348	114	169	596	339
2018	February	882	400	138	160	655	336
2018	March	862	356	150	154	595	330
2018	April	797	447	144	196	613	304
2018	May	875	364	90	169	673	319
2018	June	867	342	76	170	610	360
2018	July	829	360	108	183	594	310
2018	August	939	286	90	205	649	286
2018	September	835	304	117	175	560	296

Conversion 1 - Group two column types

(-> construction
    (tc/pivot->longer #"^[125NWS].*|Midwest" {:target-columns [:units :region]
                                               :splitter (fn [col-name]
                                                           (if (re-matches #"^[125].*" col-name)
                                                             [(construction-unit-map col-name) nil]
                                                             [nil col-name]))
                                               :value-column-name :n
                                               :drop-missing? false}))

data/construction.csv [63 5]:

Year	Month	:units	:region	:n
2018	January	1		859
2018	February	1		882
2018	March	1		862
2018	April	1		797
2018	May	1		875
2018	June	1		867
2018	July	1		829
2018	August	1		939
2018	September	1		835
2018	January	2-4
…	…	…	…	…
2018	August		South	649
2018	September		South	560
2018	January		West	339
2018	February		West	336
2018	March		West	330
2018	April		West	304
2018	May		West	319
2018	June		West	360
2018	July		West	310
2018	August		West	286
2018	September		West	296

Conversion 2 - Convert to longer form and back and rename columns

(-> construction
    (tc/pivot->longer #"^[125NWS].*|Midwest" {:target-columns [:units :region]
                                               :splitter (fn [col-name]
                                                           (if (re-matches #"^[125].*" col-name)
                                                             [(construction-unit-map col-name) nil]
                                                             [nil col-name]))
                                               :value-column-name :n
                                               :drop-missing? false})
    (tc/pivot->wider [:units :region] :n {:drop-missing? false})
    (tc/rename-columns (zipmap (vals construction-unit-map)
                                (keys construction-unit-map))))

data/construction.csv [9 9]:

Year	Month	1	5 units or more	Northeast	Midwest	South	West
2018	January	859	348	114	169	596	339
2018	February	882	400	138	160	655	336
2018	March	862	356	150	154	595	330
2018	April	797	447	144	196	613	304
2018	May	875	364	90	169	673	319
2018	June	867	342	76	170	610	360
2018	July	829	360	108	183	594	310
2018	August	939	286	90	205	649	286
2018	September	835	304	117	175	560	296

Various operations on stocks, examples taken from gather and spread manuals.

(def stocks-tidyr (tc/dataset "data/stockstidyr.csv"))

stocks-tidyr

data/stockstidyr.csv [10 4]:

time	X	Y	Z
2009-01-01	1.30989806	-1.89040193	-1.77946880
2009-01-02	-0.29993804	-1.82473090	2.39892513
2009-01-03	0.53647501	-1.03606860	-3.98697977
2009-01-04	-1.88390802	-0.52178390	-2.83065490
2009-01-05	-0.96052361	-2.21683349	1.43715171
2009-01-06	-1.18528966	-2.89350924	3.39784140
2009-01-07	-0.85207056	-2.16794818	-1.20108258
2009-01-08	0.25234172	-0.32854117	-1.53160473
2009-01-09	0.40257136	1.96407898	-6.80878830
2009-01-10	-0.64383500	2.68618382	-2.55909321

Convert to longer form

(def stocks-long (tc/pivot->longer stocks-tidyr ["X" "Y" "Z"] {:value-column-name :price
                                                                :target-columns :stocks}))

stocks-long

data/stockstidyr.csv [30 3]:

time	:stocks	:price
2009-01-01	X	1.30989806
2009-01-02	X	-0.29993804
2009-01-03	X	0.53647501
2009-01-04	X	-1.88390802
2009-01-05	X	-0.96052361
2009-01-06	X	-1.18528966
2009-01-07	X	-0.85207056
2009-01-08	X	0.25234172
2009-01-09	X	0.40257136
2009-01-10	X	-0.64383500
…	…	…
2009-01-10	Y	2.68618382
2009-01-01	Z	-1.77946880
2009-01-02	Z	2.39892513
2009-01-03	Z	-3.98697977
2009-01-04	Z	-2.83065490
2009-01-05	Z	1.43715171
2009-01-06	Z	3.39784140
2009-01-07	Z	-1.20108258
2009-01-08	Z	-1.53160473
2009-01-09	Z	-6.80878830
2009-01-10	Z	-2.55909321

Convert back to wide form

(tc/pivot->wider stocks-long :stocks :price)

data/stockstidyr.csv [10 4]:

time	X	Y	Z
2009-01-01	1.30989806	-1.89040193	-1.77946880
2009-01-02	-0.29993804	-1.82473090	2.39892513
2009-01-03	0.53647501	-1.03606860	-3.98697977
2009-01-04	-1.88390802	-0.52178390	-2.83065490
2009-01-05	-0.96052361	-2.21683349	1.43715171
2009-01-06	-1.18528966	-2.89350924	3.39784140
2009-01-07	-0.85207056	-2.16794818	-1.20108258
2009-01-08	0.25234172	-0.32854117	-1.53160473
2009-01-09	0.40257136	1.96407898	-6.80878830
2009-01-10	-0.64383500	2.68618382	-2.55909321

Convert to wide form on time column (let’s limit values to a couple of rows)

(-> stocks-long
    (tc/select-rows (range 0 30 4))
    (tc/pivot->wider "time" :price {:drop-missing? false}))

data/stockstidyr.csv [3 6]:

:stocks	2009-01-01	2009-01-05	2009-01-09	2009-01-03	2009-01-07
Y				-1.0360686	-2.16794818
X	1.30989806	-0.96052361	0.40257136
Z	-1.77946880	1.43715171	-6.80878830

Join/Concat Datasets

Dataset join and concatenation functions.

Joins accept left-side and right-side datasets and columns selector. Options are the same as in tech.ml.dataset functions.

A column selector can be a map with :left and :right keys to specify column names separate for left and right dataset.

The difference between tech.ml.dataset join functions are: arguments order (first datasets) and possibility to join on multiple columns.

Multiple columns joins create temporary index column from column selection. The method for creating index is based on :hashing option and defaults to identity. Prior to 7.000-beta-50 hash function was used, which caused hash collision for certain cases.

Additionally set operations are defined: intersect and difference.

To concat two datasets rowwise you can choose:

concat - concats rows for matching columns, the number of columns should be equal.
union - like concat but returns unique values
bind - concats rows add missing, empty columns

To add two datasets columnwise use bind. The number of rows should be equal.

Datasets used in examples:

(def ds1 (tc/dataset {:a [1 2 1 2 3 4 nil nil 4]
                       :b (range 101 110)
                       :c (map str "abs tract")}))
(def ds2 (tc/dataset {:a [nil 1 2 5 4 3 2 1 nil]
                      :b (range 110 101 -1)
                      :c (map str "datatable")
                      :d (symbol "X")
                      :e [3 4 5 6 7 nil 8 1 1]}))

ds1
ds2

_unnamed [9 3]:

:a	:b	:c
1	101	a
2	102	b
1	103	s
2	104
3	105	t
4	106	r
	107	a
	108	c
4	109	t

_unnamed [9 5]:

:a	:b	:c	:d	:e
	110	d	X	3
1	109	a	X	4
2	108	t	X	5
5	107	a	X	6
4	106	t	X	7
3	105	a	X
2	104	b	X	8
1	103	l	X	1
	102	e	X	1

Left

(tc/left-join ds1 ds2 :b)

left-outer-join [9 8]:

:b	:a	:c	:right.b	:right.a	:right.c	:d	:e
109	4	t	109	1	a	X	4
108		c	108	2	t	X	5
107		a	107	5	a	X	6
106	4	r	106	4	t	X	7
105	3	t	105	3	a	X
104	2		104	2	b	X	8
103	1	s	103	1	l	X	1
102	2	b	102		e	X	1
101	1	a

(tc/left-join ds2 ds1 :b)

left-outer-join [9 8]:

:b	:a	:c	:d	:e	:right.b	:right.a	:right.c
102		e	X	1	102	2	b
103	1	l	X	1	103	1	s
104	2	b	X	8	104	2
105	3	a	X		105	3	t
106	4	t	X	7	106	4	r
107	5	a	X	6	107		a
108	2	t	X	5	108		c
109	1	a	X	4	109	4	t
110		d	X	3

(tc/left-join ds1 ds2 [:a :b])

left-outer-join [9 8]:

:a	:b	:c	:right.a	:right.b	:right.c	:d	:e
4	106	r	4	106	t	X	7
3	105	t	3	105	a	X
2	104		2	104	b	X	8
1	103	s	1	103	l	X	1
1	101	a
2	102	b
	107	a
	108	c
4	109	t

(tc/left-join ds2 ds1 [:a :b])

left-outer-join [9 8]:

:a	:b	:c	:d	:e	:right.a	:right.b	:right.c
1	103	l	X	1	1	103	s
2	104	b	X	8	2	104
3	105	a	X		3	105	t
4	106	t	X	7	4	106	r
	110	d	X	3
1	109	a	X	4
2	108	t	X	5
5	107	a	X	6
	102	e	X	1

(tc/left-join ds1 ds2 {:left :a :right :e})

left-outer-join [11 8]:

:a	:b	:c	:e	:right.a	:right.b	:right.c	:d
3	105	t	3		110	d	X
4	106	r	4	1	109	a	X
4	109	t	4	1	109	a	X
	107	a		3	105	a	X
	108	c		3	105	a	X
1	101	a	1	1	103	l	X
1	103	s	1	1	103	l	X
1	101	a	1		102	e	X
1	103	s	1		102	e	X
2	102	b
2	104

(tc/left-join ds2 ds1 {:left :e :right :a})

left-outer-join [13 8]:

:e	:a	:b	:c	:d	:right.a	:right.b	:right.c
1	1	103	l	X	1	101	a
1		102	e	X	1	101	a
1	1	103	l	X	1	103	s
1		102	e	X	1	103	s
3		110	d	X	3	105	t
4	1	109	a	X	4	106	r
	3	105	a	X		107	a
	3	105	a	X		108	c
4	1	109	a	X	4	109	t
5	2	108	t	X
6	5	107	a	X
7	4	106	t	X
8	2	104	b	X

Right

(tc/right-join ds1 ds2 :b)

right-outer-join [9 8]:

:b	:a	:c	:right.b	:right.a	:right.c	:d	:e
109	4	t	109	1	a	X	4
108		c	108	2	t	X	5
107		a	107	5	a	X	6
106	4	r	106	4	t	X	7
105	3	t	105	3	a	X
104	2		104	2	b	X	8
103	1	s	103	1	l	X	1
102	2	b	102		e	X	1
			110		d	X	3

(tc/right-join ds2 ds1 :b)

right-outer-join [9 8]:

:b	:a	:c	:d	:e	:right.b	:right.a	:right.c
102		e	X	1	102	2	b
103	1	l	X	1	103	1	s
104	2	b	X	8	104	2
105	3	a	X		105	3	t
106	4	t	X	7	106	4	r
107	5	a	X	6	107		a
108	2	t	X	5	108		c
109	1	a	X	4	109	4	t
					101	1	a

(tc/right-join ds1 ds2 [:a :b])

right-outer-join [9 8]:

:a	:b	:c	:right.a	:right.b	:right.c	:d	:e
4	106	r	4	106	t	X	7
3	105	t	3	105	a	X
2	104		2	104	b	X	8
1	103	s	1	103	l	X	1
				110	d	X	3
			1	109	a	X	4
			2	108	t	X	5
			5	107	a	X	6
				102	e	X	1

(tc/right-join ds2 ds1 [:a :b])

right-outer-join [9 8]:

:a	:b	:c	:d	:e	:right.a	:right.b	:right.c
1	103	l	X	1	1	103	s
2	104	b	X	8	2	104
3	105	a	X		3	105	t
4	106	t	X	7	4	106	r
					1	101	a
					2	102	b
						107	a
						108	c
					4	109	t

(tc/right-join ds1 ds2 {:left :a :right :e})

right-outer-join [13 8]:

:a	:b	:c	:e	:right.a	:right.b	:right.c	:d
3	105	t	3		110	d	X
4	106	r	4	1	109	a	X
4	109	t	4	1	109	a	X
	107	a		3	105	a	X
	108	c		3	105	a	X
1	101	a	1	1	103	l	X
1	103	s	1	1	103	l	X
1	101	a	1		102	e	X
1	103	s	1		102	e	X
			5	2	108	t	X
			6	5	107	a	X
			7	4	106	t	X
			8	2	104	b	X

(tc/right-join ds2 ds1 {:left :e :right :a})

right-outer-join [11 8]:

:e	:a	:b	:c	:d	:right.a	:right.b	:right.c
1	1	103	l	X	1	101	a
1		102	e	X	1	101	a
1	1	103	l	X	1	103	s
1		102	e	X	1	103	s
3		110	d	X	3	105	t
4	1	109	a	X	4	106	r
	3	105	a	X		107	a
	3	105	a	X		108	c
4	1	109	a	X	4	109	t
					2	102	b
					2	104

Inner

(tc/inner-join ds1 ds2 :b)

inner-join [8 7]:

:b	:a	:c	:right.a	:right.c	:d	:e
109	4	t	1	a	X	4
108		c	2	t	X	5
107		a	5	a	X	6
106	4	r	4	t	X	7
105	3	t	3	a	X
104	2		2	b	X	8
103	1	s	1	l	X	1
102	2	b		e	X	1

(tc/inner-join ds2 ds1 :b)

inner-join [8 7]:

:b	:a	:c	:d	:e	:right.a	:right.c
102		e	X	1	2	b
103	1	l	X	1	1	s
104	2	b	X	8	2
105	3	a	X		3	t
106	4	t	X	7	4	r
107	5	a	X	6		a
108	2	t	X	5		c
109	1	a	X	4	4	t

(tc/inner-join ds1 ds2 [:a :b])

inner-join [4 8]:

:a	:b	:c	:right.a	:right.b	:right.c	:d	:e
4	106	r	4	106	t	X	7
3	105	t	3	105	a	X
2	104		2	104	b	X	8
1	103	s	1	103	l	X	1

(tc/inner-join ds2 ds1 [:a :b])

inner-join [4 8]:

:a	:b	:c	:d	:e	:right.a	:right.b	:right.c
1	103	l	X	1	1	103	s
2	104	b	X	8	2	104
3	105	a	X		3	105	t
4	106	t	X	7	4	106	r

(tc/inner-join ds1 ds2 {:left :a :right :e})

inner-join [9 7]:

:a	:b	:c	:right.a	:right.b	:right.c	:d
3	105	t		110	d	X
4	106	r	1	109	a	X
4	109	t	1	109	a	X
	107	a	3	105	a	X
	108	c	3	105	a	X
1	101	a	1	103	l	X
1	103	s	1	103	l	X
1	101	a		102	e	X
1	103	s		102	e	X

(tc/inner-join ds2 ds1 {:left :e :right :a})

inner-join [9 7]:

:e	:a	:b	:c	:d	:right.b	:right.c
1	1	103	l	X	101	a
1		102	e	X	101	a
1	1	103	l	X	103	s
1		102	e	X	103	s
3		110	d	X	105	t
4	1	109	a	X	106	r
	3	105	a	X	107	a
	3	105	a	X	108	c
4	1	109	a	X	109	t

Full

Join keeping all rows

(tc/full-join ds1 ds2 :b)

outer-join [10 7]:

:b	:a	:c	:right.a	:right.c	:d	:e
109	4	t	1	a	X	4
108		c	2	t	X	5
107		a	5	a	X	6
106	4	r	4	t	X	7
105	3	t	3	a	X
104	2		2	b	X	8
103	1	s	1	l	X	1
102	2	b		e	X	1
101	1	a
110				d	X	3

(tc/full-join ds2 ds1 :b)

outer-join [10 7]:

:b	:a	:c	:d	:e	:right.a	:right.c
102		e	X	1	2	b
103	1	l	X	1	1	s
104	2	b	X	8	2
105	3	a	X		3	t
106	4	t	X	7	4	r
107	5	a	X	6		a
108	2	t	X	5		c
109	1	a	X	4	4	t
110		d	X	3
101					1	a

(tc/full-join ds1 ds2 [:a :b])

outer-join [14 8]:

:a	:b	:c	:right.a	:right.b	:right.c	:d	:e
4	106	r	4	106	t	X	7
3	105	t	3	105	a	X
2	104		2	104	b	X	8
1	103	s	1	103	l	X	1
1	101	a
2	102	b
	107	a
	108	c
4	109	t
				110	d	X	3
			1	109	a	X	4
			2	108	t	X	5
			5	107	a	X	6
				102	e	X	1

(tc/full-join ds2 ds1 [:a :b])

outer-join [14 8]:

:a	:b	:c	:d	:e	:right.a	:right.b	:right.c
1	103	l	X	1	1	103	s
2	104	b	X	8	2	104
3	105	a	X		3	105	t
4	106	t	X	7	4	106	r
	110	d	X	3
1	109	a	X	4
2	108	t	X	5
5	107	a	X	6
	102	e	X	1
					1	101	a
					2	102	b
						107	a
						108	c
					4	109	t

(tc/full-join ds1 ds2 {:left :a :right :e})

outer-join [15 8]:

:a	:b	:c	:e	:right.a	:right.b	:right.c	:d
3	105	t	3		110	d	X
4	106	r	4	1	109	a	X
4	109	t	4	1	109	a	X
	107	a		3	105	a	X
	108	c		3	105	a	X
1	101	a	1	1	103	l	X
1	103	s	1	1	103	l	X
1	101	a	1		102	e	X
1	103	s	1		102	e	X
2	102	b
2	104
			5	2	108	t	X
			6	5	107	a	X
			7	4	106	t	X
			8	2	104	b	X

(tc/full-join ds2 ds1 {:left :e :right :a})

outer-join [15 8]:

:e	:a	:b	:c	:d	:right.a	:right.b	:right.c
1	1	103	l	X	1	101	a
1		102	e	X	1	101	a
1	1	103	l	X	1	103	s
1		102	e	X	1	103	s
3		110	d	X	3	105	t
4	1	109	a	X	4	106	r
	3	105	a	X		107	a
	3	105	a	X		108	c
4	1	109	a	X	4	109	t
5	2	108	t	X
6	5	107	a	X
7	4	106	t	X
8	2	104	b	X
					2	102	b
					2	104

Semi

Return rows from ds1 matching ds2

(tc/semi-join ds1 ds2 :b)

_unnamed [8 3]:

:a	:b	:c
4	109	t
	108	c
	107	a
4	106	r
3	105	t
2	104
1	103	s
2	102	b

(tc/semi-join ds2 ds1 :b)

_unnamed [8 5]:

:a	:b	:c	:d	:e
	102	e	X	1
1	103	l	X	1
2	104	b	X	8
3	105	a	X
4	106	t	X	7
5	107	a	X	6
2	108	t	X	5
1	109	a	X	4

(tc/semi-join ds1 ds2 [:a :b])

_unnamed [4 3]:

:a	:b	:c
4	106	r
3	105	t
2	104
1	103	s

(tc/semi-join ds2 ds1 [:a :b])

_unnamed [4 5]:

:a	:b	:c	:d	:e
1	103	l	X	1
2	104	b	X	8
3	105	a	X
4	106	t	X	7

(tc/semi-join ds1 ds2 {:left :a :right :e})

_unnamed [7 3]:

:a	:b	:c
3	105	t
4	106	r
4	109	t
	107	a
	108	c
1	101	a
1	103	s

(tc/semi-join ds2 ds1 {:left :e :right :a})

_unnamed [5 5]:

:a	:b	:c	:d	:e
1	103	l	X	1
	102	e	X	1
	110	d	X	3
1	109	a	X	4
3	105	a	X

Anti

Return rows from ds1 not matching ds2

(tc/anti-join ds1 ds2 :b)

_unnamed [1 3]:

:a	:b	:c
1	101	a

(tc/anti-join ds2 ds1 :b)

_unnamed [1 5]:

:a	:b	:c	:d	:e
	110	d	X	3

(tc/anti-join ds1 ds2 [:a :b])

_unnamed [5 3]:

:a	:b	:c
1	101	a
2	102	b
	107	a
	108	c
4	109	t

(tc/anti-join ds1 ds2 {:left :a :right :e})

_unnamed [2 3]:

:a	:b	:c
2	102	b
2	104

(tc/anti-join ds2 ds1 {:left :e :right :a})

_unnamed [4 5]:

:a	:b	:c	:d	:e
2	108	t	X	5
5	107	a	X	6
4	106	t	X	7
2	104	b	X	8

Hashing

When :hashing option is used, data from join columns are preprocessed by applying join-columns funtion with :result-type set to the value of :hashing. This helps to create custom joining behaviour. Function used for hashing will get vector of row values from join columns.

In the following example we will join columns on value modulo 5.

(tc/left-join ds1 ds2 :b {:hashing (fn [[v]] (mod v 5))})

left-outer-join [16 8]:

:a	:b	:c	:right.a	:right.b	:right.c	:d	:e
3	105	t		110	d	X	3
2	104		1	109	a	X	4
4	109	t	1	109	a	X	4
1	103	s	2	108	t	X	5
	108	c	2	108	t	X	5
2	102	b	5	107	a	X	6
	107	a	5	107	a	X	6
1	101	a	4	106	t	X	7
4	106	r	4	106	t	X	7
3	105	t	3	105	a	X
2	104		2	104	b	X	8
4	109	t	2	104	b	X	8
1	103	s	1	103	l	X	1
	108	c	1	103	l	X	1
2	102	b		102	e	X	1
	107	a		102	e	X	1

Cross

Cross product from selected columns

(tc/cross-join ds1 ds2 [:a :b])

cross-join [81 4]:

:a	:b	:right.a	:right.b
1	101		110
1	101	1	109
1	101	2	108
1	101	5	107
1	101	4	106
1	101	3	105
1	101	2	104
1	101	1	103
1	101		102
2	102		110
…	…	…	…
	108	1	103
	108		102
4	109		110
4	109	1	109
4	109	2	108
4	109	5	107
4	109	4	106
4	109	3	105
4	109	2	104
4	109	1	103
4	109		102

(tc/cross-join ds1 ds2 {:left [:a :b] :right :e})

cross-join [81 3]:

:a	:b	:e
1	101	3
1	101	4
1	101	5
1	101	6
1	101	7
1	101
1	101	8
1	101	1
1	101	1
2	102	3
…	…	…
	108	1
	108	1
4	109	3
4	109	4
4	109	5
4	109	6
4	109	7
4	109
4	109	8
4	109	1
4	109	1

Expand

Similar to cross product but works on a single dataset.

(tc/expand ds2 :a :c :d)

cross-join [36 3]:

:a	:c	:d
	d	X
	a	X
	t	X
	b	X
	l	X
	e	X
1	d	X
1	a	X
1	t	X
1	b	X
…	…	…
4	a	X
4	t	X
4	b	X
4	l	X
4	e	X
3	d	X
3	a	X
3	t	X
3	b	X
3	l	X
3	e	X

Columns can be also bundled (nested) in tuples which are treated as a single entity during cross product.

(tc/expand ds2 [:a :c] [:e :b])

cross-join [81 4]:

:a	:c	:e	:b
	d	3	110
	d	4	109
	d	5	108
	d	6	107
	d	7	106
	d		105
	d	8	104
	d	1	103
	d	1	102
1	a	3	110
…	…	…	…
1	l	1	103
1	l	1	102
	e	3	110
	e	4	109
	e	5	108
	e	6	107
	e	7	106
	e		105
	e	8	104
	e	1	103
	e	1	102

Complete

Same as expand with all other columns preserved (filled with missing values if necessary).

(tc/complete ds2 :a :c :d)

left-outer-join [36 5]:

:a	:c	:d	:b	:e
	d	X	110	3
1	a	X	109	4
2	t	X	108	5
5	a	X	107	6
4	t	X	106	7
3	a	X	105
2	b	X	104	8
1	l	X	103	1
	e	X	102	1
	a	X
…	…	…	…	…
5	e	X
4	d	X
4	a	X
4	b	X
4	l	X
4	e	X
3	d	X
3	t	X
3	b	X
3	l	X
3	e	X

(tc/complete ds2 [:a :c] [:e :b])

left-outer-join [81 5]:

:a	:c	:e	:b	:d
	d	3	110	X
1	a	4	109	X
2	t	5	108	X
5	a	6	107	X
4	t	7	106	X
3	a		105	X
2	b	8	104	X
1	l	1	103	X
	e	1	102	X
	d	4	109
…	…	…	…	…
1	l		105
1	l	8	104
1	l	1	102
	e	3	110
	e	4	109
	e	5	108
	e	6	107
	e	7	106
	e		105
	e	8	104
	e	1	103

asof

(def left-ds (tc/dataset {:a [1 5 10]
                          :left-val ["a" "b" "c"]}))
(def right-ds (tc/dataset {:a [1 2 3 6 7]
                           :right-val [:a :b :c :d :e]}))

left-ds
right-ds

_unnamed [3 2]:

:a	:left-val
1	a
5	b
10	c

_unnamed [5 2]:

:a	:right-val
1	:a
2	:b
3	:c
6	:d
7	:e

(tc/asof-join left-ds right-ds :a)

asof-<= [3 4]:

:a	:left-val	:right.a	:right-val
1	a	1	:a
5	b	6	:d
10	c

(tc/asof-join left-ds right-ds :a {:asof-op :nearest})

asof-nearest [3 4]:

:a	:left-val	:right.a	:right-val
1	a	1	:a
5	b	6	:d
10	c	7	:e

(tc/asof-join left-ds right-ds :a {:asof-op :>=})

asof->= [3 4]:

:a	:left-val	:right.a	:right-val
1	a	1	:a
5	b	3	:c
10	c	7	:e

Concat

contact joins rows from other datasets

(tc/concat ds1)

_unnamed [9 3]:

:a	:b	:c
1	101	a
2	102	b
1	103	s
2	104
3	105	t
4	106	r
	107	a
	108	c
4	109	t

concat-copying ensures all readers are evaluated.

(tc/concat-copying ds1)

_unnamed [9 3]:

:a	:b	:c
1	101	a
2	102	b
1	103	s
2	104
3	105	t
4	106	r
	107	a
	108	c
4	109	t

(tc/concat ds1 (tc/drop-columns ds2 :d))

_unnamed [18 4]:

:a	:b	:c	:e
1	101	a
2	102	b
1	103	s
2	104
3	105	t
4	106	r
	107	a
	108	c
4	109	t
	110	d	3
1	109	a	4
2	108	t	5
5	107	a	6
4	106	t	7
3	105	a
2	104	b	8
1	103	l	1
	102	e	1

(apply tc/concat (repeatedly 3 #(tc/random DS)))

_unnamed [27 4]:

:V1	:V2	:V3	:V4
1	9	1.5	C
2	6	1.5	C
2	8	1.0	B
1	1	0.5	A
2	6	1.5	C
1	5	1.0	B
1	5	1.0	B
1	7	0.5	A
2	6	1.5	C
2	2	1.0	B
…	…	…	…
2	2	1.0	B
1	9	1.5	C
2	4	0.5	A
1	1	0.5	A
2	8	1.0	B
2	4	0.5	A
1	7	0.5	A
1	7	0.5	A
1	9	1.5	C
1	3	1.5	C
2	4	0.5	A

Concat grouped dataset

Concatenation of grouped datasets results also in grouped dataset.

(tc/concat (tc/group-by DS [:V3])
           (tc/group-by DS [:V4]))

_unnamed [6 3]:

:name	:group-id	:data
{:V3 0.5}	0	Group: {:V3 0.5} [3 4]:
{:V3 1.0}	1	Group: {:V3 1.0} [3 4]:
{:V3 1.5}	2	Group: {:V3 1.5} [3 4]:
{:V4 “A”}	3	Group: {:V4 “A”} [3 4]:
{:V4 “B”}	4	Group: {:V4 “B”} [3 4]:
{:V4 “C”}	5	Group: {:V4 “C”} [3 4]:

Union

The same as concat but returns unique rows

(apply tc/union (tc/drop-columns ds2 :d) (repeat 10 ds1))

union [18 4]:

:a	:b	:c	:e
	110	d	3
1	109	a	4
2	108	t	5
5	107	a	6
4	106	t	7
3	105	a
2	104	b	8
1	103	l	1
	102	e	1
1	101	a
2	102	b
1	103	s
2	104
3	105	t
4	106	r
	107	a
	108	c
4	109	t

(apply tc/union (repeatedly 10 #(tc/random DS)))

union [9 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	6	1.5	C
1	1	0.5	A
1	9	1.5	C
2	8	1.0	B
1	5	1.0	B
1	3	1.5	C
2	4	0.5	A
1	7	0.5	A

Bind

bind adds empty columns during concat

(tc/bind ds1 ds2)

_unnamed [18 5]:

:a	:b	:c	:e	:d
1	101	a
2	102	b
1	103	s
2	104
3	105	t
4	106	r
	107	a
	108	c
4	109	t
	110	d	3	X
1	109	a	4	X
2	108	t	5	X
5	107	a	6	X
4	106	t	7	X
3	105	a		X
2	104	b	8	X
1	103	l	1	X
	102	e	1	X

(tc/bind ds2 ds1)

_unnamed [18 5]:

:a	:b	:c	:d	:e
	110	d	X	3
1	109	a	X	4
2	108	t	X	5
5	107	a	X	6
4	106	t	X	7
3	105	a	X
2	104	b	X	8
1	103	l	X	1
	102	e	X	1
1	101	a
2	102	b
1	103	s
2	104
3	105	t
4	106	r
	107	a
	108	c
4	109	t

Append

append concats columns

(tc/append ds1 ds2)

_unnamed [9 8]:

:a	:b	:c	:a	:b	:c	:d	:e
1	101	a		110	d	X	3
2	102	b	1	109	a	X	4
1	103	s	2	108	t	X	5
2	104		5	107	a	X	6
3	105	t	4	106	t	X	7
4	106	r	3	105	a	X
	107	a	2	104	b	X	8
	108	c	1	103	l	X	1
4	109	t		102	e	X	1

Intersection

(tc/intersect (tc/select-columns ds1 :b)
              (tc/select-columns ds2 :b))

intersection [8 1]:

:b
109
108
107
106
105
104
103
102

Difference

(tc/difference (tc/select-columns ds1 :b)
               (tc/select-columns ds2 :b))

difference [1 1]:

:b
101

(tc/difference (tc/select-columns ds2 :b)
               (tc/select-columns ds1 :b))

difference [1 1]:

:b
110

Split into train/test

In ML world very often you need to test given model and prepare collection of train and test datasets. split creates new dataset with two additional columns:

:$split-name - with :train, :test, :split-2, … values
:$split-id - id of splitted group (for k-fold and repeating)

split-type can be one of the following:

:kfold (default) - k-fold strategy, :k defines number of folds (defaults to 5), produces k splits
:bootstrap - :ratio defines ratio of observations put into result (defaults to 1.0), produces 1 split
:holdout - split into two or more parts with given ratio(s) (defaults to 2/3), produces 1 split
:holdouts - splits into two parts for ascending ratio. Range of rations is given by steps option
:loo - leave one out, produces the same number of splits as number of observations

:holdout can accept also probabilites or ratios and can split to more than 2 subdatasets

Additionally you can provide:

:seed - for random number generator
:shuffle? - turn on/off shuffle of the rows (default: true)
:repeats - repeat procedure :repeats times
:partition-selector - same as in group-by for stratified splitting to reflect dataset structure in splits.
:split-names names of subdatasets different than default, ie. [:train :test :split-2 ...]
:split-col-name - a column where name of split is stored, either :train or :test values (default: :$split-name)
:split-id-col-name - a column where id of the train/test pair is stored (default: :$split-id)

In case of grouped dataset each group is processed separately.

(def for-splitting (tc/dataset (map-indexed (fn [id v] {:id id
                                                        :partition v
                                                        :group (rand-nth [:g1 :g2 :g3])})
                                            (concat (repeat 20 :a) (repeat 5 :b)))))

for-splitting

_unnamed [25 3]:

:id	:partition	:group
0	:a	:g3
1	:a	:g1
2	:a	:g2
3	:a	:g2
4	:a	:g2
5	:a	:g1
6	:a	:g1
7	:a	:g3
8	:a	:g2
9	:a	:g2
…	…	…
14	:a	:g1
15	:a	:g3
16	:a	:g1
17	:a	:g3
18	:a	:g1
19	:a	:g3
20	:b	:g2
21	:b	:g1
22	:b	:g2
23	:b	:g2
24	:b	:g2

k-Fold

Returns k=5 maps

(-> for-splitting
    (tc/split)
    (tc/head 30))

_unnamed, (splitted) [30 5]:

:id	:partition	:group	:$split-name \| :$split-id
17	:a	:g3	:train	0
19	:a	:g3	:train	0
16	:a	:g1	:train	0
15	:a	:g3	:train	0
21	:b	:g1	:train	0
3	:a	:g2	:train	0
0	:a	:g3	:train	0
4	:a	:g2	:train	0
20	:b	:g2	:train	0
12	:a	:g3	:train	0
13	:a	:g3	:train	0
2	:a	:g2	:train	0
23	:b	:g2	:train	0
1	:a	:g1	:train	0
11	:a	:g1	:train	0
6	:a	:g1	:train	0
10	:a	:g1	:train	0
7	:a	:g3	:train	0
5	:a	:g1	:train	0
14	:a	:g1	:train	0
9	:a	:g2	:test	0
22	:b	:g2	:test	0
18	:a	:g1	:test	0
24	:b	:g2	:test	0
8	:a	:g2	:test	0
9	:a	:g2	:train	1
22	:b	:g2	:train	1
18	:a	:g1	:train	1
24	:b	:g2	:train	1
8	:a	:g2	:train	1

Partition according to :k column to reflect it’s distribution

(-> for-splitting
    (tc/split :kfold {:partition-selector :partition})
    (tc/head 30))

_unnamed, (splitted) [30 5]:

:id	:partition	:group	:$split-name \| :$split-id
15	:a	:g3	:train	0
18	:a	:g1	:train	0
6	:a	:g1	:train	0
2	:a	:g2	:train	0
1	:a	:g1	:train	0
14	:a	:g1	:train	0
10	:a	:g1	:train	0
9	:a	:g2	:train	0
11	:a	:g1	:train	0
5	:a	:g1	:train	0
16	:a	:g1	:train	0
17	:a	:g3	:train	0
0	:a	:g3	:train	0
8	:a	:g2	:train	0
19	:a	:g3	:train	0
3	:a	:g2	:train	0
12	:a	:g3	:test	0
4	:a	:g2	:test	0
7	:a	:g3	:test	0
13	:a	:g3	:test	0
12	:a	:g3	:train	1
4	:a	:g2	:train	1
7	:a	:g3	:train	1
13	:a	:g3	:train	1
1	:a	:g1	:train	1
14	:a	:g1	:train	1
10	:a	:g1	:train	1
9	:a	:g2	:train	1
11	:a	:g1	:train	1
5	:a	:g1	:train	1

Bootstrap

(tc/split for-splitting :bootstrap)

_unnamed, (splitted) [32 5]:

:id	:partition	:group	:$split-name \| :$split-id
12	:a	:g3	:train	0
9	:a	:g2	:train	0
5	:a	:g1	:train	0
17	:a	:g3	:train	0
0	:a	:g3	:train	0
7	:a	:g3	:train	0
1	:a	:g1	:train	0
13	:a	:g3	:train	0
22	:b	:g2	:train	0
0	:a	:g3	:train	0
…	…	…	…	…
0	:a	:g3	:train	0
22	:b	:g2	:train	0
4	:a	:g2	:train	0
2	:a	:g2	:train	0
6	:a	:g1	:test	0
8	:a	:g2	:test	0
10	:a	:g1	:test	0
19	:a	:g3	:test	0
21	:b	:g1	:test	0
23	:b	:g2	:test	0
24	:b	:g2	:test	0

with repeats, to get 100 splits

(-> for-splitting
    (tc/split :bootstrap {:repeats 100})
    (:$split-id)
    (distinct)
    (count))

100

Holdout

with small ratio

(tc/split for-splitting :holdout {:ratio 0.2})

_unnamed, (splitted) [25 5]:

:id	:partition	:group	:$split-name \| :$split-id
5	:a	:g1	:train	0
1	:a	:g1	:train	0
9	:a	:g2	:train	0
12	:a	:g3	:train	0
3	:a	:g2	:train	0
18	:a	:g1	:test	0
11	:a	:g1	:test	0
17	:a	:g3	:test	0
16	:a	:g1	:test	0
20	:b	:g2	:test	0
…	…	…	…	…
24	:b	:g2	:test	0
4	:a	:g2	:test	0
19	:a	:g3	:test	0
21	:b	:g1	:test	0
23	:b	:g2	:test	0
22	:b	:g2	:test	0
10	:a	:g1	:test	0
15	:a	:g3	:test	0
14	:a	:g1	:test	0
6	:a	:g1	:test	0
7	:a	:g3	:test	0

you can split to more than two subdatasets with holdout

(tc/split for-splitting :holdout {:ratio [0.1 0.2 0.3 0.15 0.25]})

_unnamed, (splitted) [25 5]:

:id	:partition	:group	:$split-name \| :$split-id
22	:b	:g2	:train	0
16	:a	:g1	:train	0
24	:b	:g2	:test	0
2	:a	:g2	:test	0
20	:b	:g2	:test	0
3	:a	:g2	:test	0
13	:a	:g3	:test	0
19	:a	:g3	:split-2	0
7	:a	:g3	:split-2	0
14	:a	:g1	:split-2	0
…	…	…	…	…
8	:a	:g2	:split-3	0
1	:a	:g1	:split-3	0
23	:b	:g2	:split-3	0
0	:a	:g3	:split-4	0
11	:a	:g1	:split-4	0
5	:a	:g1	:split-4	0
9	:a	:g2	:split-4	0
18	:a	:g1	:split-4	0
17	:a	:g3	:split-4	0
6	:a	:g1	:split-4	0
21	:b	:g1	:split-4	0

you can use also proportions with custom names

(tc/split for-splitting :holdout {:ratio [5 3 11 2]
                                  :split-names ["small" "smaller" "big" "the rest"]})

_unnamed, (splitted) [25 5]:

:id	:partition	:group	:$split-name \| :$split-id
10	:a	:g1	small	0
16	:a	:g1	small	0
17	:a	:g3	small	0
11	:a	:g1	small	0
12	:a	:g3	small	0
21	:b	:g1	smaller	0
6	:a	:g1	smaller	0
4	:a	:g2	smaller	0
18	:a	:g1	big	0
20	:b	:g2	big	0
…	…	…	…	…
22	:b	:g2	big	0
3	:a	:g2	big	0
13	:a	:g3	big	0
19	:a	:g3	big	0
24	:b	:g2	big	0
9	:a	:g2	big	0
8	:a	:g2	big	0
5	:a	:g1	the rest	0
23	:b	:g2	the rest	0
14	:a	:g1	the rest	0
7	:a	:g3	the rest	0

Holdouts

With ratios from 5% to 95% of the dataset with step 1.5 generates 15 splits with ascending rows in train dataset.

(-> (tc/split for-splitting :holdouts {:steps [0.05 0.95 1.5]
                                       :shuffle? false})
    (tc/group-by [:$split-id :$split-name]))

_unnamed [30 3]:

Leave One Out

(-> for-splitting
    (tc/split :loo)
    (tc/head 30))

_unnamed, (splitted) [30 5]:

:id	:partition	:group	:$split-name \| :$split-id
9	:a	:g2	:train	0
11	:a	:g1	:train	0
21	:b	:g1	:train	0
1	:a	:g1	:train	0
10	:a	:g1	:train	0
22	:b	:g2	:train	0
23	:b	:g2	:train	0
15	:a	:g3	:train	0
5	:a	:g1	:train	0
16	:a	:g1	:train	0
6	:a	:g1	:train	0
24	:b	:g2	:train	0
3	:a	:g2	:train	0
8	:a	:g2	:train	0
2	:a	:g2	:train	0
4	:a	:g2	:train	0
17	:a	:g3	:train	0
13	:a	:g3	:train	0
18	:a	:g1	:train	0
7	:a	:g3	:train	0
20	:b	:g2	:train	0
14	:a	:g1	:train	0
19	:a	:g3	:train	0
12	:a	:g3	:train	0
0	:a	:g3	:test	0
0	:a	:g3	:train	1
11	:a	:g1	:train	1
21	:b	:g1	:train	1
1	:a	:g1	:train	1
10	:a	:g1	:train	1

(-> for-splitting
    (tc/split :loo)
    (tc/row-count))

625

Grouped dataset with partitioning

(-> for-splitting
    (tc/group-by :group)
    (tc/split :bootstrap {:partition-selector :partition :seed 11 :ratio 0.8}))

_unnamed [3 3]:

:name	:group-id	:data
:g3	0	Group: :g3, (splitted) [8 5]:
:g1	1	Group: :g1, (splitted) [11 5]:
:g2	2	Group: :g2, (splitted) [11 5]:

Split as a sequence

To get a sequence of pairs, use split->seq function

(-> for-splitting
    (tc/split->seq :kfold {:partition-selector :partition})
    (first))

{:train Group: 0 [20 3]:

:id	:partition	:group
17	:a	:g3
11	:a	:g1
9	:a	:g2
12	:a	:g3
5	:a	:g1
19	:a	:g3
14	:a	:g1
4	:a	:g2
7	:a	:g3
16	:a	:g1
8	:a	:g2
10	:a	:g1
2	:a	:g2
0	:a	:g3
15	:a	:g3
18	:a	:g1
23	:b	:g2
21	:b	:g1
20	:b	:g2
24	:b	:g2

, :test Group: 0 [5 3]:

:id	:partition	:group
13	:a	:g3
1	:a	:g1
6	:a	:g1
3	:a	:g2
22	:b	:g2

}

(-> for-splitting
    (tc/group-by :group)
    (tc/split->seq :bootstrap {:partition-selector :partition :seed 11 :ratio 0.8 :repeats 2})
    (first))

[:g3 ({:train Group: 0 [6 3]:]

Pipeline

tablecloth.pipeline exports special versions of API which create functions operating only on dataset. This creates the possibility to chain operations and compose them easily.

There are two ways to create pipelines:

functional, as a composition of functions
declarative, separating task declarations and concrete parametrization.

Pipeline operations are prepared to work with metamorph library. That means that result of the pipeline is wrapped into a map and dataset is stored under :metamorph/data key.

Warning: Duplicated metamorph pipeline functions are removed from tablecloth.pipeline namespace.

Functions

This API doesn’t provide any statistical, numerical or date/time functions. Use below namespaces:

Namespace	functions
`tech.v3.datatype.functional`	primitive oprations, reducers, statistics
`tech.v3.datatype.datetime`	date/time converters and operations

Other examples

Stocks

(defonce stocks (tc/dataset "https://raw.githubusercontent.com/techascent/tech.ml.dataset/master/test/data/stocks.csv" {:key-fn keyword}))

stocks

https://raw.githubusercontent.com/techascent/tech.ml.dataset/master/test/data/stocks.csv [560 3]:

:symbol	:date	:price
MSFT	2000-01-01	39.81
MSFT	2000-02-01	36.35
MSFT	2000-03-01	43.22
MSFT	2000-04-01	28.37
MSFT	2000-05-01	25.45
MSFT	2000-06-01	32.54
MSFT	2000-07-01	28.40
MSFT	2000-08-01	28.40
MSFT	2000-09-01	24.53
MSFT	2000-10-01	28.02
…	…	…
AAPL	2009-05-01	135.81
AAPL	2009-06-01	142.43
AAPL	2009-07-01	163.39
AAPL	2009-08-01	168.21
AAPL	2009-09-01	185.35
AAPL	2009-10-01	188.50
AAPL	2009-11-01	199.91
AAPL	2009-12-01	210.73
AAPL	2010-01-01	192.06
AAPL	2010-02-01	204.62
AAPL	2010-03-01	223.02

(-> stocks
    (tc/group-by (fn [row]
                    {:symbol (:symbol row)
                     :year (tech.v3.datatype.datetime/long-temporal-field :years (:date row))}))
    (tc/aggregate #(tech.v3.datatype.functional/mean (% :price)))
    (tc/order-by [:symbol :year]))

_unnamed [51 3]:

:symbol	:year	summary
AAPL	2000	21.74833333
AAPL	2001	10.17583333
AAPL	2002	9.40833333
AAPL	2003	9.34750000
AAPL	2004	18.72333333
AAPL	2005	48.17166667
AAPL	2006	72.04333333
AAPL	2007	133.35333333
AAPL	2008	138.48083333
AAPL	2009	150.39333333
…	…	…
MSFT	2000	29.67333333
MSFT	2001	25.34750000
MSFT	2002	21.82666667
MSFT	2003	20.93416667
MSFT	2004	22.67416667
MSFT	2005	23.84583333
MSFT	2006	24.75833333
MSFT	2007	29.28416667
MSFT	2008	25.20833333
MSFT	2009	22.87250000
MSFT	2010	28.50666667

(-> stocks
    (tc/group-by (juxt :symbol #(tech.v3.datatype.datetime/long-temporal-field :years (% :date))))
    (tc/aggregate #(tech.v3.datatype.functional/mean (% :price)))
    (tc/rename-columns {:$group-name-0 :symbol
                         :$group-name-1 :year}))

_unnamed [51 3]:

:symbol	:year	summary
MSFT	2000	29.67333333
MSFT	2001	25.34750000
MSFT	2002	21.82666667
MSFT	2003	20.93416667
MSFT	2004	22.67416667
MSFT	2005	23.84583333
MSFT	2006	24.75833333
MSFT	2007	29.28416667
MSFT	2008	25.20833333
MSFT	2009	22.87250000
…	…	…
AAPL	2000	21.74833333
AAPL	2001	10.17583333
AAPL	2002	9.40833333
AAPL	2003	9.34750000
AAPL	2004	18.72333333
AAPL	2005	48.17166667
AAPL	2006	72.04333333
AAPL	2007	133.35333333
AAPL	2008	138.48083333
AAPL	2009	150.39333333
AAPL	2010	206.56666667

data.table

Below you can find comparizon between functionality of data.table and Clojure dataset API. I leave it without comments, please refer original document explaining details:

Introduction to data.table

library(data.table)

data.table 1.14.8 using 8 threads (see ?getDTthreads).  Latest news: r-datatable.com

library(knitr)

flights <- fread("https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv")

kable(head(flights))

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	1	1	14	13	AA	JFK	LAX	359	2475	9
2014	1	1	-3	13	AA	JFK	LAX	363	2475	11
2014	1	1	2	9	AA	JFK	LAX	351	2475	19
2014	1	1	-8	-26	AA	LGA	PBI	157	1035	7
2014	1	1	2	1	AA	JFK	LAX	350	2475	13
2014	1	1	4	0	AA	EWR	LAX	339	2454	18

Clojure

(require '[tech.v3.datatype.functional :as dfn]
         '[tech.v3.datatype.argops :as aops]
         '[tech.v3.datatype :as dtype])

(defonce flights (tc/dataset "https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv"))

(tc/head flights 6)

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [6 11]:

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	1	1	14	13	AA	JFK	LAX	359	2475	9
2014	1	1	-3	13	AA	JFK	LAX	363	2475	11
2014	1	1	2	9	AA	JFK	LAX	351	2475	19
2014	1	1	-8	-26	AA	LGA	PBI	157	1035	7
2014	1	1	2	1	AA	JFK	LAX	350	2475	13
2014	1	1	4	0	AA	EWR	LAX	339	2454	18

Basics

Shape of loaded data

dim(flights)

[1] 253316     11

Clojure

(tc/shape flights)

[253316 11]

What is `data.table`?

DT = data.table(
  ID = c("b","b","b","a","a","c"),
  a = 1:6,
  b = 7:12,
  c = 13:18
)

kable(DT)

ID	a	b	c
b	1	7	13
b	2	8	14
b	3	9	15
a	4	10	16
a	5	11	17
c	6	12	18

class(DT$ID)

[1] "character"

Clojure

(def DT (tc/dataset {:ID ["b" "b" "b" "a" "a" "c"]
                      :a (range 1 7)
                      :b (range 7 13)
                      :c (range 13 19)}))

DT

_unnamed [6 4]:

:ID	:a	:b	:c
b	1	7	13
b	2	8	14
b	3	9	15
a	4	10	16
a	5	11	17
c	6	12	18

(-> :ID DT meta :datatype)

:string

Get all the flights with “JFK” as the origin airport in the month of June.

ans <- flights[origin == "JFK" & month == 6L]
kable(head(ans))

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	6	1	-9	-5	AA	JFK	LAX	324	2475	8
2014	6	1	-10	-13	AA	JFK	LAX	329	2475	12
2014	6	1	18	-1	AA	JFK	LAX	326	2475	7
2014	6	1	-6	-16	AA	JFK	LAX	320	2475	10
2014	6	1	-4	-45	AA	JFK	LAX	326	2475	18
2014	6	1	-6	-23	AA	JFK	LAX	329	2475	14

Clojure

(-> flights
    (tc/select-rows (fn [row] (and (= (get row "origin") "JFK")
                                   (= (get row "month") 6))))
    (tc/head 6))

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [6 11]:

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	6	1	-9	-5	AA	JFK	LAX	324	2475	8
2014	6	1	-10	-13	AA	JFK	LAX	329	2475	12
2014	6	1	18	-1	AA	JFK	LAX	326	2475	7
2014	6	1	-6	-16	AA	JFK	LAX	320	2475	10
2014	6	1	-4	-45	AA	JFK	LAX	326	2475	18
2014	6	1	-6	-23	AA	JFK	LAX	329	2475	14

Get the first two rows from `flights`.

ans <- flights[1:2]
kable(ans)

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	1	1	14	13	AA	JFK	LAX	359	2475	9
2014	1	1	-3	13	AA	JFK	LAX	363	2475	11

Clojure

(tc/select-rows flights (range 2))

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [2 11]:

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	1	1	14	13	AA	JFK	LAX	359	2475	9
2014	1	1	-3	13	AA	JFK	LAX	363	2475	11

Sort `flights` first by column `origin` in ascending order, and then by `dest` in descending order

ans <- flights[order(origin, -dest)]
kable(head(ans))

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	1	5	6	49	EV	EWR	XNA	195	1131	8
2014	1	6	7	13	EV	EWR	XNA	190	1131	8
2014	1	7	-6	-13	EV	EWR	XNA	179	1131	8
2014	1	8	-7	-12	EV	EWR	XNA	184	1131	8
2014	1	9	16	7	EV	EWR	XNA	181	1131	8
2014	1	13	66	66	EV	EWR	XNA	188	1131	9

Clojure

(-> flights
    (tc/order-by ["origin" "dest"] [:asc :desc])
    (tc/head 6))

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [6 11]:

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	6	3	-6	-38	EV	EWR	XNA	154	1131	6
2014	1	20	-9	-17	EV	EWR	XNA	177	1131	8
2014	3	19	-6	10	EV	EWR	XNA	201	1131	6
2014	2	3	231	268	EV	EWR	XNA	184	1131	12
2014	4	25	-8	-32	EV	EWR	XNA	159	1131	6
2014	2	19	21	10	EV	EWR	XNA	176	1131	8

Select `arr_delay` column, but return it as a vector

ans <- flights[, arr_delay]
head(ans)

[1]  13  13   9 -26   1   0

Clojure

(take 6 (flights "arr_delay"))

(13 13 9 -26 1 0)

Select `arr_delay` column, but return as a data.table instead

ans <- flights[, list(arr_delay)]
kable(head(ans))

arr_delay
13
13
9
-26
1
0

Clojure

(-> flights
    (tc/select-columns "arr_delay")
    (tc/head 6))

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [6 1]:

arr_delay
13
13
9
-26
1
0

Select both `arr_delay` and `dep_delay` columns

ans <- flights[, .(arr_delay, dep_delay)]
kable(head(ans))

arr_delay	dep_delay
13	14
13	-3
9	2
-26	-8
1	2
0	4

Clojure

(-> flights
    (tc/select-columns ["arr_delay" "dep_delay"])
    (tc/head 6))

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [6 2]:

arr_delay	dep_delay
13	14
13	-3
9	2
-26	-8
1	2
0	4

Select both `arr_delay` and `dep_delay` columns and rename them to `delay_arr` and `delay_dep`

ans <- flights[, .(delay_arr = arr_delay, delay_dep = dep_delay)]
kable(head(ans))

delay_arr	delay_dep
13	14
13	-3
9	2
-26	-8
1	2
0	4

Clojure

(-> flights
    (tc/select-columns {"arr_delay" "delay_arr"
                         "dep_delay" "delay_arr"})
    (tc/head 6))

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [6 2]:

delay_arr	delay_arr
13	14
13	-3
9	2
-26	-8
1	2
0	4

How many trips have had total delay < 0?

ans <- flights[, sum( (arr_delay + dep_delay) < 0 )]
ans

[1] 141814

Clojure

(->> (dfn/+ (flights "arr_delay") (flights "dep_delay"))
     (aops/argfilter #(< % 0.0))
     (dtype/ecount))

or pure Clojure functions (much, much slower)

(->> (map + (flights "arr_delay") (flights "dep_delay"))
     (filter neg?)
     (count))

Calculate the average arrival and departure delay for all flights with “JFK” as the origin airport in the month of June

ans <- flights[origin == "JFK" & month == 6L,
               .(m_arr = mean(arr_delay), m_dep = mean(dep_delay))]
kable(ans)

m_arr	m_dep
5.839349	9.807884

Clojure

(-> flights
    (tc/select-rows (fn [row] (and (= (get row "origin") "JFK")
                                   (= (get row "month") 6))))
    (tc/aggregate {:m_arr #(dfn/mean (% "arr_delay"))
                    :m_dep #(dfn/mean (% "dep_delay"))}))

_unnamed [1 2]:

:m_arr	:m_dep
5.83934932	9.80788411

How many trips have been made in 2014 from “JFK” airport in the month of June?

ans <- flights[origin == "JFK" & month == 6L, length(dest)]
ans

[1] 8422

ans <- flights[origin == "JFK" & month == 6L, .N]
ans

[1] 8422

Clojure

(-> flights
    (tc/select-rows (fn [row] (and (= (get row "origin") "JFK")
                                   (= (get row "month") 6))))
    (tc/row-count))

deselect columns using - or !

ans <- flights[, !c("arr_delay", "dep_delay")]
kable(head(ans))

year	month	day	carrier	origin	dest	air_time	distance	hour
2014	1	1	AA	JFK	LAX	359	2475	9
2014	1	1	AA	JFK	LAX	363	2475	11
2014	1	1	AA	JFK	LAX	351	2475	19
2014	1	1	AA	LGA	PBI	157	1035	7
2014	1	1	AA	JFK	LAX	350	2475	13
2014	1	1	AA	EWR	LAX	339	2454	18

ans <- flights[, -c("arr_delay", "dep_delay")]
kable(head(ans))

year	month	day	carrier	origin	dest	air_time	distance	hour
2014	1	1	AA	JFK	LAX	359	2475	9
2014	1	1	AA	JFK	LAX	363	2475	11
2014	1	1	AA	JFK	LAX	351	2475	19
2014	1	1	AA	LGA	PBI	157	1035	7
2014	1	1	AA	JFK	LAX	350	2475	13
2014	1	1	AA	EWR	LAX	339	2454	18

Clojure

(-> flights
    (tc/select-columns (complement #{"arr_delay" "dep_delay"}))
    (tc/head 6))

https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv [6 9]:

year	month	day	carrier	origin	dest	air_time	distance	hour
2014	1	1	AA	JFK	LAX	359	2475	9
2014	1	1	AA	JFK	LAX	363	2475	11
2014	1	1	AA	JFK	LAX	351	2475	19
2014	1	1	AA	LGA	PBI	157	1035	7
2014	1	1	AA	JFK	LAX	350	2475	13
2014	1	1	AA	EWR	LAX	339	2454	18

Aggregations

How can we get the number of trips corresponding to each origin airport?

ans <- flights[, .(.N), by = .(origin)]
kable(ans)

origin	N
JFK	81483
LGA	84433
EWR	87400

Clojure

(-> flights
    (tc/group-by ["origin"])
    (tc/aggregate {:N tc/row-count}))

_unnamed [3 2]:

origin	:N
JFK	81483
LGA	84433
EWR	87400

How can we calculate the number of trips for each origin airport for carrier code “AA”?

ans <- flights[carrier == "AA", .N, by = origin]
kable(ans)

origin	N
JFK	11923
LGA	11730
EWR	2649

Clojure

(-> flights
    (tc/select-rows #(= (get % "carrier") "AA"))
    (tc/group-by ["origin"])
    (tc/aggregate {:N tc/row-count}))

_unnamed [3 2]:

origin	:N
JFK	11923
LGA	11730
EWR	2649

How can we get the total number of trips for each `origin`, `dest` pair for carrier code “AA”?

ans <- flights[carrier == "AA", .N, by = .(origin, dest)]
kable(head(ans))

origin	dest	N
JFK	LAX	3387
LGA	PBI	245
EWR	LAX	62
JFK	MIA	1876
JFK	SEA	298
EWR	MIA	848

Clojure

(-> flights
    (tc/select-rows #(= (get % "carrier") "AA"))
    (tc/group-by ["origin" "dest"])
    (tc/aggregate {:N tc/row-count})
    (tc/head 6))

_unnamed [6 3]:

origin	dest	:N
JFK	LAX	3387
LGA	PBI	245
EWR	LAX	62
JFK	MIA	1876
JFK	SEA	298
EWR	MIA	848

How can we get the average arrival and departure delay for each `orig`,`dest` pair for each month for carrier code “AA”?

ans <- flights[carrier == "AA",
        .(mean(arr_delay), mean(dep_delay)),
        by = .(origin, dest, month)]
kable(head(ans,10))

origin	dest	month	V1	V2
JFK	LAX	1	6.590361	14.2289157
LGA	PBI	1	-7.758621	0.3103448
EWR	LAX	1	1.366667	7.5000000
JFK	MIA	1	15.720670	18.7430168
JFK	SEA	1	14.357143	30.7500000
EWR	MIA	1	11.011236	12.1235955
JFK	SFO	1	19.252252	28.6396396
JFK	BOS	1	12.919643	15.2142857
JFK	ORD	1	31.586207	40.1724138
JFK	IAH	1	28.857143	14.2857143

Clojure

(-> flights
    (tc/select-rows #(= (get % "carrier") "AA"))
    (tc/group-by ["origin" "dest" "month"])
    (tc/aggregate [#(dfn/mean (% "arr_delay"))
                    #(dfn/mean (% "dep_delay"))])
    (tc/head 10))

_unnamed [10 5]:

origin	dest	month	:summary-0	:summary-1
JFK	LAX	1	6.59036145	14.22891566
LGA	PBI	1	-7.75862069	0.31034483
EWR	LAX	1	1.36666667	7.50000000
JFK	MIA	1	15.72067039	18.74301676
JFK	SEA	1	14.35714286	30.75000000
EWR	MIA	1	11.01123596	12.12359551
JFK	SFO	1	19.25225225	28.63963964
JFK	BOS	1	12.91964286	15.21428571
JFK	ORD	1	31.58620690	40.17241379
JFK	IAH	1	28.85714286	14.28571429

So how can we directly order by all the grouping variables?

ans <- flights[carrier == "AA",
        .(mean(arr_delay), mean(dep_delay)),
        keyby = .(origin, dest, month)]
kable(head(ans,10))

origin	dest	month	V1	V2
EWR	DFW	1	6.427673	10.012579
EWR	DFW	2	10.536765	11.345588
EWR	DFW	3	12.865031	8.079755
EWR	DFW	4	17.792683	12.920732
EWR	DFW	5	18.487805	18.682927
EWR	DFW	6	37.005952	38.744048
EWR	DFW	7	20.250000	21.154762
EWR	DFW	8	16.936046	22.069767
EWR	DFW	9	5.865031	13.055215
EWR	DFW	10	18.813665	18.894410

Clojure

(-> flights
    (tc/select-rows #(= (get % "carrier") "AA"))
    (tc/group-by ["origin" "dest" "month"])
    (tc/aggregate [#(dfn/mean (% "arr_delay"))
                    #(dfn/mean (% "dep_delay"))])
    (tc/order-by ["origin" "dest" "month"])
    (tc/head 10))

_unnamed [10 5]:

origin	dest	month	:summary-0	:summary-1
EWR	DFW	1	6.42767296	10.01257862
EWR	DFW	2	10.53676471	11.34558824
EWR	DFW	3	12.86503067	8.07975460
EWR	DFW	4	17.79268293	12.92073171
EWR	DFW	5	18.48780488	18.68292683
EWR	DFW	6	37.00595238	38.74404762
EWR	DFW	7	20.25000000	21.15476190
EWR	DFW	8	16.93604651	22.06976744
EWR	DFW	9	5.86503067	13.05521472
EWR	DFW	10	18.81366460	18.89440994

Can `by` accept expressions as well or does it just take columns?

ans <- flights[, .N, .(dep_delay>0, arr_delay>0)]
kable(ans)

dep_delay	arr_delay	N
TRUE	TRUE	72836
FALSE	TRUE	34583
FALSE	FALSE	119304
TRUE	FALSE	26593

Clojure

(-> flights
    (tc/group-by (fn [row]
                    {:dep_delay (pos? (get row "dep_delay"))
                     :arr_delay (pos? (get row "arr_delay"))}))
    (tc/aggregate {:N tc/row-count}))

_unnamed [4 3]:

:dep_delay	:arr_delay	:N
true	true	72836
false	true	34583
false	false	119304
true	false	26593

Do we have to compute `mean()` for each column individually?

kable(DT)

ID	a	b	c
b	1	7	13
b	2	8	14
b	3	9	15
a	4	10	16
a	5	11	17
c	6	12	18

DT[, print(.SD), by = ID]

Empty data.table (0 rows and 1 cols): ID

kable(DT[, lapply(.SD, mean), by = ID])

ID	a	b	c
b	2.0	8.0	14.0
a	4.5	10.5	16.5
c	6.0	12.0	18.0

Clojure

DT

(tc/group-by DT :ID {:result-type :as-map})

_unnamed [6 4]:

:ID	:a	:b	:c
b	1	7	13
b	2	8	14
b	3	9	15
a	4	10	16
a	5	11	17
c	6	12	18

{“b” Group: b [3 4]:

:ID	:a	:b	:c
b	1	7	13
b	2	8	14
b	3	9	15

, “a” Group: a [2 4]:

:ID	:a	:b	:c
a	4	10	16
a	5	11	17

, “c” Group: c [1 4]:

:ID	:a	:b	:c
c	6	12	18

}

(-> DT
    (tc/group-by [:ID])
    (tc/aggregate-columns (complement #{:ID}) dfn/mean))

_unnamed [3 4]:

:ID	:a	:b	:c
b	2.0	8.0	14.0
a	4.5	10.5	16.5
c	6.0	12.0	18.0

How can we specify just the columns we would like to compute the `mean()` on?

kable(head(flights[carrier == "AA",                         ## Only on trips with carrier "AA"
                   lapply(.SD, mean),                       ## compute the mean
                   by = .(origin, dest, month),             ## for every 'origin,dest,month'
                   .SDcols = c("arr_delay", "dep_delay")])) ## for just those specified in .SDcols

origin	dest	month	arr_delay	dep_delay
JFK	LAX	1	6.590361	14.2289157
LGA	PBI	1	-7.758621	0.3103448
EWR	LAX	1	1.366667	7.5000000
JFK	MIA	1	15.720670	18.7430168
JFK	SEA	1	14.357143	30.7500000
EWR	MIA	1	11.011236	12.1235955

Clojure

(-> flights
    (tc/select-rows #(= (get % "carrier") "AA"))
    (tc/group-by ["origin" "dest" "month"])
    (tc/aggregate-columns ["arr_delay" "dep_delay"] dfn/mean)
    (tc/head 6))

_unnamed [6 5]:

origin	dest	month	arr_delay	dep_delay
JFK	LAX	1	6.59036145	14.22891566
LGA	PBI	1	-7.75862069	0.31034483
EWR	LAX	1	1.36666667	7.50000000
JFK	MIA	1	15.72067039	18.74301676
JFK	SEA	1	14.35714286	30.75000000
EWR	MIA	1	11.01123596	12.12359551

How can we return the first two rows for each month?

ans <- flights[, head(.SD, 2), by = month]
kable(head(ans))

month	year	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
1	2014	1	14	13	AA	JFK	LAX	359	2475	9
1	2014	1	-3	13	AA	JFK	LAX	363	2475	11
2	2014	1	-1	1	AA	JFK	LAX	358	2475	8
2	2014	1	-5	3	AA	JFK	LAX	358	2475	11
3	2014	1	-11	36	AA	JFK	LAX	375	2475	8
3	2014	1	-3	14	AA	JFK	LAX	368	2475	11

Clojure

(-> flights
    (tc/group-by ["month"])
    (tc/head 2) ;; head applied on each group
    (tc/ungroup)
    (tc/head 6))

_unnamed [6 11]:

year	month	day	dep_delay	arr_delay	carrier	origin	dest	air_time	distance	hour
2014	1	1	14	13	AA	JFK	LAX	359	2475	9
2014	1	1	-3	13	AA	JFK	LAX	363	2475	11
2014	2	1	-1	1	AA	JFK	LAX	358	2475	8
2014	2	1	-5	3	AA	JFK	LAX	358	2475	11
2014	3	1	-11	36	AA	JFK	LAX	375	2475	8
2014	3	1	-3	14	AA	JFK	LAX	368	2475	11

How can we concatenate columns a and b for each group in ID?

kable(DT[, .(val = c(a,b)), by = ID])

ID	val
b	1
b	2
b	3
b	7
b	8
b	9
a	4
a	5
a	10
a	11
c	6
c	12

Clojure

(-> DT
    (tc/pivot->longer [:a :b] {:value-column-name :val})
    (tc/drop-columns [:$column :c]))

_unnamed [12 2]:

:ID	:val
b	1
b	2
b	3
a	4
a	5
c	6
b	7
b	8
b	9
a	10
a	11
c	12

What if we would like to have all the values of column `a` and `b` concatenated, but returned as a list column?

kable(DT[, .(val = list(c(a,b))), by = ID])

ID	val
b	1, 2, 3, 7, 8, 9
a	4, 5, 10, 11
c	6, 12

Clojure

(-> DT
    (tc/pivot->longer [:a :b] {:value-column-name :val})
    (tc/drop-columns [:$column :c])
    (tc/fold-by :ID))

_unnamed [3 2]:

:ID	:val
b	[1 2 3 7 8 9]
a	[4 5 10 11]
c	[6 12]

API tour

Below snippets are taken from A data.table and dplyr tour written by Atrebas (permission granted).

I keep structure and subtitles but I skip data.table and dplyr examples.

Example data

(def DS (tc/dataset {:V1 (take 9 (cycle [1 2]))
                      :V2 (range 1 10)
                      :V3 (take 9 (cycle [0.5 1.0 1.5]))
                      :V4 (take 9 (cycle ["A" "B" "C"]))}))

(tc/dataset? DS)
(class DS)

true
tech.v3.dataset.impl.dataset.Dataset

DS

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Basic Operations

Filter rows

Filter rows using indices

(tc/select-rows DS [2 3])

_unnamed [2 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
2	4	0.5	A

Discard rows using negative indices

In Clojure API we have separate function for that: drop-rows.

(tc/drop-rows DS (range 2 7))

_unnamed [4 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
2	8	1.0	B
1	9	1.5	C

Filter rows using a logical expression

(tc/select-rows DS (comp #(> % 5) :V2))

_unnamed [4 4]:

:V1	:V2	:V3	:V4
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

(tc/select-rows DS (comp #{"A" "C"} :V4))

_unnamed [6 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	3	1.5	C
2	4	0.5	A
2	6	1.5	C
1	7	0.5	A
1	9	1.5	C

Filter rows using multiple conditions

(tc/select-rows DS #(and (= (:V1 %) 1)
                          (= (:V4 %) "A")))

_unnamed [2 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
1	7	0.5	A

Filter unique rows

(tc/unique-by DS)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

(tc/unique-by DS [:V1 :V4])

_unnamed [6 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C

Discard rows with missing values

(tc/drop-missing DS)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

Other filters

(tc/random DS 3) ;; 3 random rows

_unnamed [3 4]:

:V1	:V2	:V3	:V4
2	4	0.5	A
2	8	1.0	B
2	2	1.0	B

(tc/random DS (/ (tc/row-count DS) 2)) ;; fraction of random rows

_unnamed [5 4]:

:V1	:V2	:V3	:V4
1	9	1.5	C
2	2	1.0	B
1	7	0.5	A
2	2	1.0	B
2	8	1.0	B

(tc/by-rank DS :V1 zero?) ;; take top n entries

_unnamed [4 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
2	4	0.5	A
2	6	1.5	C
2	8	1.0	B

Convenience functions

(tc/select-rows DS (comp (partial re-matches #"^B") str :V4))

_unnamed [3 4]:

:V1	:V2	:V3	:V4
2	2	1.0	B
1	5	1.0	B
2	8	1.0	B

(tc/select-rows DS (comp #(<= 3 % 5) :V2))

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B

(tc/select-rows DS (comp #(< 3 % 5) :V2))

_unnamed [1 4]:

:V1	:V2	:V3	:V4
2	4	0.5	A

(tc/select-rows DS (comp #(<= 3 % 5) :V2))

_unnamed [3 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B

Last example skipped.

Sort rows

Sort rows by column

(tc/order-by DS :V3)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	4	0.5	A
1	7	0.5	A
2	2	1.0	B
1	5	1.0	B
2	8	1.0	B
1	3	1.5	C
2	6	1.5	C
1	9	1.5	C

Sort rows in decreasing order

(tc/order-by DS :V3 :desc)

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	3	1.5	C
2	6	1.5	C
1	9	1.5	C
1	5	1.0	B
2	2	1.0	B
2	8	1.0	B
1	7	0.5	A
2	4	0.5	A
1	1	0.5	A

Sort rows based on several columns

(tc/order-by DS [:V1 :V2] [:asc :desc])

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	9	1.5	C
1	7	0.5	A
1	5	1.0	B
1	3	1.5	C
1	1	0.5	A
2	8	1.0	B
2	6	1.5	C
2	4	0.5	A
2	2	1.0	B

Select columns

Select one column using an index (not recommended)

(nth (tc/columns DS :as-seq) 2) ;; as column (iterable)

#tech.v3.dataset.column<float64>[9]
:V3
[0.5000, 1.000, 1.500, 0.5000, 1.000, 1.500, 0.5000, 1.000, 1.500]

(tc/dataset [(nth (tc/columns DS :as-seq) 2)])

_unnamed [9 1]:

:V3
0.5
1.0
1.5
0.5
1.0
1.5
0.5
1.0
1.5

Select one column using column name

(tc/select-columns DS :V2) ;; as dataset

_unnamed [9 1]:

:V2
1
2
3
4
5
6
7
8
9

(tc/select-columns DS [:V2]) ;; as dataset

_unnamed [9 1]:

:V2
1
2
3
4
5
6
7
8
9

(DS :V2) ;; as column (iterable)

#tech.v3.dataset.column<int64>[9]
:V2
[1, 2, 3, 4, 5, 6, 7, 8, 9]

Select several columns

(tc/select-columns DS [:V2 :V3 :V4])

_unnamed [9 3]:

:V2	:V3	:V4
1	0.5	A
2	1.0	B
3	1.5	C
4	0.5	A
5	1.0	B
6	1.5	C
7	0.5	A
8	1.0	B
9	1.5	C

Exclude columns

(tc/select-columns DS (complement #{:V2 :V3 :V4}))

_unnamed [9 1]:

:V1
1
2
1
2
1
2
1
2
1

(tc/drop-columns DS [:V2 :V3 :V4])

_unnamed [9 1]:

:V1
1
2
1
2
1
2
1
2
1

Other seletions

(->> (range 1 3)
     (map (comp keyword (partial format "V%d")))
     (tc/select-columns DS))

_unnamed [9 2]:

:V1	:V2
1	1
2	2
1	3
2	4
1	5
2	6
1	7
2	8
1	9

(tc/reorder-columns DS :V4)

_unnamed [9 4]:

:V4	:V1	:V2	:V3
A	1	1	0.5
B	2	2	1.0
C	1	3	1.5
A	2	4	0.5
B	1	5	1.0
C	2	6	1.5
A	1	7	0.5
B	2	8	1.0
C	1	9	1.5

(tc/select-columns DS #(clojure.string/starts-with? (name %) "V"))

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1	1	0.5	A
2	2	1.0	B
1	3	1.5	C
2	4	0.5	A
1	5	1.0	B
2	6	1.5	C
1	7	0.5	A
2	8	1.0	B
1	9	1.5	C

(tc/select-columns DS #(clojure.string/ends-with? (name %) "3"))

_unnamed [9 1]:

:V3
0.5
1.0
1.5
0.5
1.0
1.5
0.5
1.0
1.5

(tc/select-columns DS #"..2") ;; regex converts to string using `str` function

_unnamed [9 1]:

:V2
1
2
3
4
5
6
7
8
9

(tc/select-columns DS #{:V1 "X"})

_unnamed [9 1]:

:V1
1
2
1
2
1
2
1
2
1

(tc/select-columns DS #(not (clojure.string/starts-with? (name %) "V2")))

_unnamed [9 3]:

:V1	:V3	:V4
1	0.5	A
2	1.0	B
1	1.5	C
2	0.5	A
1	1.0	B
2	1.5	C
1	0.5	A
2	1.0	B
1	1.5	C

Summarise data

Summarise one column

(reduce + (DS :V1)) ;; using pure Clojure, as value

(tc/aggregate-columns DS :V1 dfn/sum) ;; as dataset

_unnamed [1 1]:

:V1
13.0

(tc/aggregate DS {:sumV1 #(dfn/sum (% :V1))})

_unnamed [1 1]:

:sumV1
13.0

Summarize several columns

(tc/aggregate DS [#(dfn/sum (% :V1))
                   #(dfn/standard-deviation (% :V3))])

_unnamed [1 2]:

:summary-0	:summary-1
13.0	0.4330127

(tc/aggregate-columns DS [:V1 :V3] [dfn/sum
                                     dfn/standard-deviation])

_unnamed [1 2]:

:V1	:V3
13.0	0.4330127

Summarise several columns and assign column names

(tc/aggregate DS {:sumv1 #(dfn/sum (% :V1))
                   :sdv3 #(dfn/standard-deviation (% :V3))})

_unnamed [1 2]:

:sumv1	:sdv3
13.0	0.4330127

Summarise a subset of rows

(-> DS
    (tc/select-rows (range 4))
    (tc/aggregate-columns :V1 dfn/sum))

_unnamed [1 1]:

:V1
6.0

Additional helpers

(-> DS
    (tc/first)
    (tc/select-columns :V3)) ;; select first row from `:V3` column

_unnamed [1 1]:

:V3
0.5

(-> DS
    (tc/last)
    (tc/select-columns :V3)) ;; select last row from `:V3` column

_unnamed [1 1]:

:V3
1.5

(-> DS
    (tc/select-rows 4)
    (tc/select-columns :V3)) ;; select forth row from `:V3` column

_unnamed [1 1]:

:V3
1.0

(-> DS
    (tc/select :V3 4)) ;; select forth row from `:V3` column

_unnamed [1 1]:

:V3
1.0

(-> DS
    (tc/unique-by :V4)
    (tc/aggregate tc/row-count)) ;; number of unique rows in `:V4` column, as dataset

_unnamed [1 1]:

summary
3

(-> DS
    (tc/unique-by :V4)
    (tc/row-count)) ;; number of unique rows in `:V4` column, as value

(-> DS
    (tc/unique-by)
    (tc/row-count)) ;; number of unique rows in dataset, as value

Add/update/delete columns

Modify a column

(tc/map-columns DS :V1 [:V1] #(dfn/pow % 2))

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1.0	1	0.5	A
4.0	2	1.0	B
1.0	3	1.5	C
4.0	4	0.5	A
1.0	5	1.0	B
4.0	6	1.5	C
1.0	7	0.5	A
4.0	8	1.0	B
1.0	9	1.5	C

(def DS (tc/add-column DS :V1 (dfn/pow (DS :V1) 2)))

DS

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1.0	1	0.5	A
4.0	2	1.0	B
1.0	3	1.5	C
4.0	4	0.5	A
1.0	5	1.0	B
4.0	6	1.5	C
1.0	7	0.5	A
4.0	8	1.0	B
1.0	9	1.5	C

Add one column

(tc/map-columns DS :v5 [:V1] dfn/log)

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:v5
1.0	1	0.5	A	0.00000000
4.0	2	1.0	B	1.38629436
1.0	3	1.5	C	0.00000000
4.0	4	0.5	A	1.38629436
1.0	5	1.0	B	0.00000000
4.0	6	1.5	C	1.38629436
1.0	7	0.5	A	0.00000000
4.0	8	1.0	B	1.38629436
1.0	9	1.5	C	0.00000000

(def DS (tc/add-column DS :v5 (dfn/log (DS :V1))))

DS

_unnamed [9 5]:

:V1	:V2	:V3	:V4	:v5
1.0	1	0.5	A	0.00000000
4.0	2	1.0	B	1.38629436
1.0	3	1.5	C	0.00000000
4.0	4	0.5	A	1.38629436
1.0	5	1.0	B	0.00000000
4.0	6	1.5	C	1.38629436
1.0	7	0.5	A	0.00000000
4.0	8	1.0	B	1.38629436
1.0	9	1.5	C	0.00000000

Add several columns

(def DS (tc/add-columns DS {:v6 (dfn/sqrt (DS :V1))
                                       :v7 "X"}))

DS

_unnamed [9 7]:

:V1	:V2	:V3	:V4	:v5	:v6	:v7
1.0	1	0.5	A	0.00000000	1.0	X
4.0	2	1.0	B	1.38629436	2.0	X
1.0	3	1.5	C	0.00000000	1.0	X
4.0	4	0.5	A	1.38629436	2.0	X
1.0	5	1.0	B	0.00000000	1.0	X
4.0	6	1.5	C	1.38629436	2.0	X
1.0	7	0.5	A	0.00000000	1.0	X
4.0	8	1.0	B	1.38629436	2.0	X
1.0	9	1.5	C	0.00000000	1.0	X

Create one column and remove the others

(tc/dataset {:v8 (dfn/+ (DS :V3) 1)})

_unnamed [9 1]:

:v8
1.5
2.0
2.5
1.5
2.0
2.5
1.5
2.0
2.5

Remove one column

(def DS (tc/drop-columns DS :v5))

DS

_unnamed [9 6]:

:V1	:V2	:V3	:V4	:v6	:v7
1.0	1	0.5	A	1.0	X
4.0	2	1.0	B	2.0	X
1.0	3	1.5	C	1.0	X
4.0	4	0.5	A	2.0	X
1.0	5	1.0	B	1.0	X
4.0	6	1.5	C	2.0	X
1.0	7	0.5	A	1.0	X
4.0	8	1.0	B	2.0	X
1.0	9	1.5	C	1.0	X

Remove several columns

(def DS (tc/drop-columns DS [:v6 :v7]))

DS

_unnamed [9 4]:

:V1	:V2	:V3	:V4
1.0	1	0.5	A
4.0	2	1.0	B
1.0	3	1.5	C
4.0	4	0.5	A
1.0	5	1.0	B
4.0	6	1.5	C
1.0	7	0.5	A
4.0	8	1.0	B
1.0	9	1.5	C

Remove columns using a vector of colnames

We use set here.

(def DS (tc/select-columns DS (complement #{:V3})))

DS

_unnamed [9 3]:

:V1	:V2	:V4
1.0	1	A
4.0	2	B
1.0	3	C
4.0	4	A
1.0	5	B
4.0	6	C
1.0	7	A
4.0	8	B
1.0	9	C

Replace values for rows matching a condition

(def DS (tc/map-columns DS :V2 [:V2] #(if (< % 4.0) 0.0 %)))

DS

_unnamed [9 3]:

:V1	:V2	:V4
1.0	0.0	A
4.0	0.0	B
1.0	0.0	C
4.0	4.0	A
1.0	5.0	B
4.0	6.0	C
1.0	7.0	A
4.0	8.0	B
1.0	9.0	C

by

By group

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate {:sumV2 #(dfn/sum (% :V2))}))

_unnamed [3 2]:

:V4	:sumV2
A	11.0
B	13.0
C	15.0

By several groups

(-> DS
    (tc/group-by [:V4 :V1])
    (tc/aggregate {:sumV2 #(dfn/sum (% :V2))}))

_unnamed [6 3]:

:V4	:V1	:sumV2
A	1.0	7.0
B	4.0	8.0
C	1.0	9.0
A	4.0	4.0
B	1.0	5.0
C	4.0	6.0

Calling function in by

(-> DS
    (tc/group-by (fn [row]
                    (clojure.string/lower-case (:V4 row))))
    (tc/aggregate {:sumV1 #(dfn/sum (% :V1))}))

_unnamed [3 2]:

:$group-name	:sumV1
a	6.0
b	9.0
c	6.0

Assigning column name in by

(-> DS
    (tc/group-by (fn [row]
                    {:abc (clojure.string/lower-case (:V4 row))}))
    (tc/aggregate {:sumV1 #(dfn/sum (% :V1))}))

_unnamed [3 2]:

:abc	:sumV1
a	6.0
b	9.0
c	6.0

(-> DS
    (tc/group-by (fn [row]
                    (clojure.string/lower-case (:V4 row))))
    (tc/aggregate {:sumV1 #(dfn/sum (% :V1))} {:add-group-as-column :abc}))

_unnamed [3 2]:

:$group-name	:sumV1
a	6.0
b	9.0
c	6.0

Using a condition in by

(-> DS
    (tc/group-by #(= (:V4 %) "A"))
    (tc/aggregate #(dfn/sum (% :V1))))

_unnamed [2 2]:

:$group-name	summary
true	6.0
false	15.0

By on a subset of rows

(-> DS
    (tc/select-rows (range 5))
    (tc/group-by :V4)
    (tc/aggregate {:sumV1 #(dfn/sum (% :V1))}))

_unnamed [3 2]:

:$group-name	:sumV1
A	5.0
B	5.0
C	1.0

Count number of observations for each group

(-> DS
    (tc/group-by :V4)
    (tc/aggregate tc/row-count))

_unnamed [3 2]:

:$group-name	summary
A	3
B	3
C	3

Add a column with number of observations for each group

(-> DS
    (tc/group-by [:V1])
    (tc/add-column :n tc/row-count)
    (tc/ungroup))

_unnamed [9 4]:

:V1	:V2	:V4	:n
1.0	0.0	A	5
1.0	0.0	C	5
1.0	5.0	B	5
1.0	7.0	A	5
1.0	9.0	C	5
4.0	0.0	B	4
4.0	4.0	A	4
4.0	6.0	C	4
4.0	8.0	B	4

Retrieve the first/last/nth observation for each group

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate-columns :V2 first))

_unnamed [3 2]:

:V4	:V2
A	0.0
B	0.0
C	0.0

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate-columns :V2 last))

_unnamed [3 2]:

:V4	:V2
A	7.0
B	8.0
C	9.0

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate-columns :V2 #(nth % 1)))

_unnamed [3 2]:

:V4	:V2
A	4.0
B	5.0
C	6.0

Going further

Advanced columns manipulation

Summarise all the columns

;; custom max function which works on every type
(tc/aggregate-columns DS :all (fn [col] (first (sort #(compare %2 %1) col))))

_unnamed [1 3]:

:V1	:V2	:V4
4.0	9.0	C

Summarise several columns

(tc/aggregate-columns DS [:V1 :V2] dfn/mean)

_unnamed [1 2]:

:V1	:V2
2.33333333	4.33333333

Summarise several columns by group

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate-columns [:V1 :V2] dfn/mean))

_unnamed [3 3]:

:V4	:V1	:V2
A	2.0	3.66666667
B	3.0	4.33333333
C	2.0	5.00000000

Summarise with more than one function by group

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate-columns [:V1 :V2] (fn [col]
                                       {:sum (dfn/sum col)
                                        :mean (dfn/mean col)})))

_unnamed [3 5]:

:V4	:V1-sum	:V1-mean	:V2-sum	:V2-mean
A	6.0	2.0	11.0	3.66666667
B	9.0	3.0	13.0	4.33333333
C	6.0	2.0	15.0	5.00000000

Summarise using a condition

(-> DS
    (tc/select-columns :type/numerical)
    (tc/aggregate-columns :all dfn/mean))

_unnamed [1 2]:

:V1	:V2
2.33333333	4.33333333

Modify all the columns

(tc/update-columns DS :all reverse)

_unnamed [9 3]:

:V1	:V2	:V4
1.0	9.0	C
4.0	8.0	B
1.0	7.0	A
4.0	6.0	C
1.0	5.0	B
4.0	4.0	A
1.0	0.0	C
4.0	0.0	B
1.0	0.0	A

Modify several columns (dropping the others)

(-> DS
    (tc/select-columns [:V1 :V2])
    (tc/update-columns :all dfn/sqrt))

_unnamed [9 2]:

:V1	:V2
1.0	0.00000000
2.0	0.00000000
1.0	0.00000000
2.0	2.00000000
1.0	2.23606798
2.0	2.44948974
1.0	2.64575131
2.0	2.82842712
1.0	3.00000000

(-> DS
    (tc/select-columns (complement #{:V4}))
    (tc/update-columns :all dfn/exp))

_unnamed [9 2]:

:V1	:V2
2.71828183	1.00000000
54.59815003	1.00000000
2.71828183	1.00000000
54.59815003	54.59815003
2.71828183	148.41315910
54.59815003	403.42879349
2.71828183	1096.63315843
54.59815003	2980.95798704
2.71828183	8103.08392758

Modify several columns (keeping the others)

(def DS (tc/update-columns DS [:V1 :V2] dfn/sqrt))

DS

_unnamed [9 3]:

:V1	:V2	:V4
1.0	0.00000000	A
2.0	0.00000000	B
1.0	0.00000000	C
2.0	2.00000000	A
1.0	2.23606798	B
2.0	2.44948974	C
1.0	2.64575131	A
2.0	2.82842712	B
1.0	3.00000000	C

(def DS (tc/update-columns DS (complement #{:V4}) #(dfn/pow % 2)))

DS

_unnamed [9 3]:

:V1	:V2	:V4
1.0	0.0	A
4.0	0.0	B
1.0	0.0	C
4.0	4.0	A
1.0	5.0	B
4.0	6.0	C
1.0	7.0	A
4.0	8.0	B
1.0	9.0	C

Modify columns using a condition (dropping the others)

(-> DS
    (tc/select-columns :type/numerical)
    (tc/update-columns :all #(dfn/- % 1)))

_unnamed [9 2]:

:V1	:V2
0.0	-1.0
3.0	-1.0
0.0	-1.0
3.0	3.0
0.0	4.0
3.0	5.0
0.0	6.0
3.0	7.0
0.0	8.0

Modify columns using a condition (keeping the others)

(def DS (tc/convert-types DS :type/numerical :int32))

DS

_unnamed [9 3]:

:V1	:V2	:V4
1	0	A
4	0	B
1	0	C
4	4	A
1	5	B
4	5	C
1	7	A
4	8	B
1	9	C

Use a complex expression

(-> DS
    (tc/group-by [:V4])
    (tc/head 2)
    (tc/add-column :V2 "X")
    (tc/ungroup))

_unnamed [6 3]:

:V1	:V2	:V4
1	X	A
4	X	A
4	X	B
1	X	B
1	X	C
4	X	C

Use multiple expressions

(tc/dataset (let [x (dfn/+ (DS :V1) (dfn/sum (DS :V2)))]
               (println (seq (DS :V1)))
               (println (tc/info (tc/select-columns DS :V1)))
               {:A (range 1 (inc (tc/row-count DS)))
                :B x}))

(1 4 1 4 1 4 1 4 1) _unnamed: descriptive-stats [1 11]:

:col-name	:datatype	:n-valid	:n-missing	:min	:mean	:max	:standard-deviation	:skew	:first	:last
:V1	:int32	9	0	1.0	2.33333333	4.0	1.58113883	0.27105237	1	1

_unnamed [9 2]:

:A	:B
1	39.0
2	42.0
3	39.0
4	42.0
5	39.0
6	42.0
7	39.0
8	42.0
9	39.0

Chain expressions

Expression chaining using >

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate {:V1sum #(dfn/sum (% :V1))})
    (tc/select-rows #(>= (:V1sum %) 5)))

_unnamed [3 2]:

:V4	:V1sum
A	6.0
B	9.0
C	6.0

(-> DS
    (tc/group-by [:V4])
    (tc/aggregate {:V1sum #(dfn/sum (% :V1))})
    (tc/order-by :V1sum :desc))

_unnamed [3 2]:

:V4	:V1sum
B	9.0
A	6.0
C	6.0

Indexing and Keys

Set the key/index (order)

(def DS (tc/order-by DS :V4))

DS

_unnamed [9 3]:

:V1	:V2	:V4
1	0	A
4	4	A
1	7	A
4	0	B
1	5	B
4	8	B
1	0	C
4	5	C
1	9	C

Select the matching rows

(tc/select-rows DS #(= (:V4 %) "A"))

_unnamed [3 3]:

:V1	:V2	:V4
1	0	A
4	4	A
1	7	A

(tc/select-rows DS (comp #{"A" "C"} :V4))

_unnamed [6 3]:

:V1	:V2	:V4
1	0	A
4	4	A
1	7	A
1	0	C
4	5	C
1	9	C

Select the first matching row

(-> DS
    (tc/select-rows #(= (:V4 %) "B"))
    (tc/first))

_unnamed [1 3]:

:V1	:V2	:V4
4	0	B

(-> DS
    (tc/unique-by :V4)
    (tc/select-rows (comp #{"B" "C"} :V4)))

_unnamed [2 3]:

:V1	:V2	:V4
4	0	B
1	0	C

Select the last matching row

(-> DS
    (tc/select-rows #(= (:V4 %) "A"))
    (tc/last))

_unnamed [1 3]:

:V1	:V2	:V4
1	7	A

Nomatch argument

(tc/select-rows DS (comp #{"A" "D"} :V4))

_unnamed [3 3]:

:V1	:V2	:V4
1	0	A
4	4	A
1	7	A

Apply a function on the matching rows

(-> DS
    (tc/select-rows (comp #{"A" "C"} :V4))
    (tc/aggregate-columns :V1 (fn [col]
                                 {:sum (dfn/sum col)})))

_unnamed [1 1]:

:V1-sum
12.0

Modify values for matching rows

(def DS (-> DS
            (tc/map-columns :V1 [:V1 :V4] #(if (= %2 "A") 0 %1))
            (tc/order-by :V4)))

DS

_unnamed [9 3]:

:V1	:V2	:V4
0	0	A
0	4	A
0	7	A
4	0	B
1	5	B
4	8	B
1	0	C
4	5	C
1	9	C

Use keys in by

(-> DS
    (tc/select-rows (comp (complement #{"B"}) :V4))
    (tc/group-by [:V4])
    (tc/aggregate-columns :V1 dfn/sum))

_unnamed [2 2]:

:V4	:V1
A	0.0
C	6.0

Set keys/indices for multiple columns (ordered)

(tc/order-by DS [:V4 :V1])

_unnamed [9 3]:

:V1	:V2	:V4
0	0	A
0	4	A
0	7	A
1	5	B
4	0	B
4	8	B
1	0	C
1	9	C
4	5	C

Subset using multiple keys/indices

(-> DS
    (tc/select-rows #(and (= (:V1 %) 1)
                           (= (:V4 %) "C"))))

_unnamed [2 3]:

:V1	:V2	:V4
1	0	C
1	9	C

(-> DS
    (tc/select-rows #(and (= (:V1 %) 1)
                           (#{"B" "C"} (:V4 %)))))

_unnamed [3 3]:

:V1	:V2	:V4
1	5	B
1	0	C
1	9	C

(-> DS
    (tc/select-rows #(and (= (:V1 %) 1)
                           (#{"B" "C"} (:V4 %))) {:result-type :as-indexes}))

(4 6 8)

set*() modifications

Replace values

There is no mutating operations tech.ml.dataset or easy way to set value.

(def DS (tc/update-columns DS :V2 #(map-indexed (fn [idx v]
                                                   (if (zero? idx) 3 v)) %)))

DS

_unnamed [9 3]:

:V1	:V2	:V4
0	3	A
0	4	A
0	7	A
4	0	B
1	5	B
4	8	B
1	0	C
4	5	C
1	9	C

Reorder rows

(def DS (tc/order-by DS [:V4 :V1] [:asc :desc]))

DS

_unnamed [9 3]:

:V1	:V2	:V4
0	3	A
0	4	A
0	7	A
4	0	B
4	8	B
1	5	B
4	5	C
1	0	C
1	9	C

Modify colnames

(def DS (tc/rename-columns DS {:V2 "v2"}))

DS

_unnamed [9 3]:

:V1	v2	:V4
0	3	A
0	4	A
0	7	A
4	0	B
4	8	B
1	5	B
4	5	C
1	0	C
1	9	C

(def DS (tc/rename-columns DS {"v2" :V2})) ;; revert back

Reorder columns

(def DS (tc/reorder-columns DS :V4 :V1 :V2))

DS

_unnamed [9 3]:

:V4	:V1	:V2
A	0	3
A	0	4
A	0	7
B	4	0
B	4	8
B	1	5
C	4	5
C	1	0
C	1	9

Advanced use of by

Select first/last/… row by group

(-> DS
    (tc/group-by :V4)
    (tc/first)
    (tc/ungroup))

_unnamed [3 3]:

:V4	:V1	:V2
A	0	3
B	4	0
C	4	5

(-> DS
    (tc/group-by :V4)
    (tc/select-rows [0 2])
    (tc/ungroup))

_unnamed [6 3]:

:V4	:V1	:V2
A	0	3
A	0	7
B	4	0
B	1	5
C	4	5
C	1	9

(-> DS
    (tc/group-by :V4)
    (tc/tail 2)
    (tc/ungroup))

_unnamed [6 3]:

:V4	:V1	:V2
A	0	4
A	0	7
B	4	8
B	1	5
C	1	0
C	1	9

Select rows using a nested query

(-> DS
    (tc/group-by :V4)
    (tc/order-by :V2)
    (tc/first)
    (tc/ungroup))

_unnamed [3 3]:

:V4	:V1	:V2
A	0	3
B	4	0
C	1	0

Add a group counter column

(-> DS
    (tc/group-by [:V4 :V1])
    (tc/ungroup {:add-group-id-as-column :Grp}))

_unnamed [9 4]:

:Grp	:V4	:V1	:V2
0	A	0	3
0	A	0	4
0	A	0	7
1	B	4	0
1	B	4	8
2	B	1	5
3	C	4	5
4	C	1	0
4	C	1	9

Get row number of first (and last) observation by group

(-> DS
    (tc/add-column :row-id (range))
    (tc/select-columns [:V4 :row-id])
    (tc/group-by :V4)
    (tc/ungroup))

_unnamed [9 2]:

:V4	:row-id
A	0
A	1
A	2
B	3
B	4
B	5
C	6
C	7
C	8

(-> DS
    (tc/add-column :row-id (range))
    (tc/select-columns [:V4 :row-id])
    (tc/group-by :V4)
    (tc/first)
    (tc/ungroup))

_unnamed [3 2]:

:V4	:row-id
A	0
B	3
C	6

(-> DS
    (tc/add-column :row-id (range))
    (tc/select-columns [:V4 :row-id])
    (tc/group-by :V4)
    (tc/select-rows [0 2])
    (tc/ungroup))

_unnamed [6 2]:

:V4	:row-id
A	0
A	2
B	3
B	5
C	6
C	8

Handle list-columns by group

(-> DS
    (tc/select-columns [:V1 :V4])
    (tc/fold-by :V4))

_unnamed [3 2]:

:V4	:V1
A	[0 0 0]
B	[4 4 1]
C	[4 1 1]

(-> DS    
    (tc/group-by :V4)
    (tc/unmark-group))

_unnamed [3 3]:

:name	:group-id	:data
A	0	Group: A [3 3]:
B	1	Group: B [3 3]:
C	2	Group: C [3 3]:

Grouping sets (multiple by at once)

Not available.

Miscellaneous

Read / Write data

Write data to a csv file

(tc/write! DS "DF.csv")

Write data to a tab-delimited file

(tc/write! DS "DF.txt" {:separator \tab})

(tc/write! DS "DF.tsv")

Read a csv / tab-delimited file

(tc/dataset "DF.csv" {:key-fn keyword})

DF.csv [9 3]:

:V4	:V1	:V2
A	0	3
A	0	4
A	0	7
B	4	0
B	4	8
B	1	5
C	4	5
C	1	0
C	1	9

(tc/dataset "DF.txt" {:key-fn keyword})

DF.txt [9 1]:

:V4 V1 V2
A 0 3
A 0 4
A 0 7
B 4 0
B 4 8
B 1 5
C 4 5
C 1 0
C 1 9

(tc/dataset "DF.tsv" {:key-fn keyword})

DF.tsv [9 3]:

:V4	:V1	:V2
A	0	3
A	0	4
A	0	7
B	4	0
B	4	8
B	1	5
C	4	5
C	1	0
C	1	9

Read a csv file selecting / droping columns

(tc/dataset "DF.csv" {:key-fn keyword
                       :column-whitelist ["V1" "V4"]})

DF.csv [9 2]:

:V4	:V1
A	0
A	0
A	0
B	4
B	4
B	1
C	4
C	1
C	1

(tc/dataset "DF.csv" {:key-fn keyword
                       :column-blacklist ["V4"]})

DF.csv [9 2]:

:V1	:V2
0	3
0	4
0	7
4	0
4	8
1	5
4	5
1	0
1	9

Read and rbind several files

(apply tc/concat (map tc/dataset ["DF.csv" "DF.csv"]))

DF.csv [18 3]:

V4	V1	V2
A	0	3
A	0	4
A	0	7
B	4	0
B	4	8
B	1	5
C	4	5
C	1	0
C	1	9
A	0	3
A	0	4
A	0	7
B	4	0
B	4	8
B	1	5
C	4	5
C	1	0
C	1	9

Reshape data

Melt data (from wide to long)

(def mDS (tc/pivot->longer DS [:V1 :V2] {:target-columns :variable
                                          :value-column-name :value}))

mDS

_unnamed [18 3]:

:V4	:variable	:value
A	:V1	0
A	:V1	0
A	:V1	0
B	:V1	4
B	:V1	4
B	:V1	1
C	:V1	4
C	:V1	1
C	:V1	1
A	:V2	3
A	:V2	4
A	:V2	7
B	:V2	0
B	:V2	8
B	:V2	5
C	:V2	5
C	:V2	0
C	:V2	9

Cast data (from long to wide)

(-> mDS
    (tc/pivot->wider :variable :value {:fold-fn vec})
    (tc/update-columns ["V1" "V2"] (partial map count)))

_unnamed [3 3]:

:V4	:V1	:V2
A	[0 0 0]	[3 4 7]
B	[4 4 1]	[0 8 5]
C	[4 1 1]	[5 0 9]

(-> mDS
    (tc/pivot->wider :variable :value {:fold-fn vec})
    (tc/update-columns ["V1" "V2"] (partial map dfn/sum)))

_unnamed [3 3]:

:V4	:V1	:V2
A	[0 0 0]	[3 4 7]
B	[4 4 1]	[0 8 5]
C	[4 1 1]	[5 0 9]

(-> mDS
    (tc/map-columns :value #(str (> % 5))) ;; coerce to strings
    (tc/pivot->wider :value :variable {:fold-fn vec})
    (tc/update-columns ["true" "false"] (partial map #(if (sequential? %) (count %) 1))))

_unnamed [3 3]:

:V4	false	true
A	5	1
B	5	1
C	5	1

Split

(tc/group-by DS :V4 {:result-type :as-map})

{“A” Group: A [3 3]:

:V4	:V1	:V2
A	0	3
A	0	4
A	0	7

, “B” Group: B [3 3]:

:V4	:V1	:V2
B	4	0
B	4	8
B	1	5

, “C” Group: C [3 3]:

:V4	:V1	:V2
C	4	5
C	1	0
C	1	9

}

Split and transpose a vector/column

(-> {:a ["A:a" "B:b" "C:c"]}
    (tc/dataset)
    (tc/separate-column :a [:V1 :V2] ":"))

_unnamed [3 2]:

:V1	:V2
A	a
B	b
C	c

Other

Skipped

Join/Bind data sets

(def x (tc/dataset {"Id" ["A" "B" "C" "C"]
                     "X1" [1 3 5 7]
                     "XY" ["x2" "x4" "x6" "x8"]}))
(def y (tc/dataset {"Id" ["A" "B" "B" "D"]
                     "Y1" [1 3 5 7]
                     "XY" ["y1" "y3" "y5" "y7"]}))

x y

_unnamed [4 3]:

Id	X1	XY
A	1	x2
B	3	x4
C	5	x6
C	7	x8

_unnamed [4 3]:

Id	Y1	XY
A	1	y1
B	3	y3
B	5	y5
D	7	y7

Join

Join matching rows from y to x

(tc/left-join x y "Id")

left-outer-join [5 6]:

Id	X1	XY	right.Id	Y1	right.XY
A	1	x2	A	1	y1
B	3	x4	B	3	y3
B	3	x4	B	5	y5
C	5	x6
C	7	x8

Join matching rows from x to y

(tc/right-join x y "Id")

right-outer-join [4 6]:

Id	X1	XY	right.Id	Y1	right.XY
A	1	x2	A	1	y1
B	3	x4	B	3	y3
B	3	x4	B	5	y5
			D	7	y7

Join matching rows from both x and y

(tc/inner-join x y "Id")

inner-join [3 5]:

Id	X1	XY	Y1	right.XY
A	1	x2	1	y1
B	3	x4	3	y3
B	3	x4	5	y5

Join keeping all the rows

(tc/full-join x y "Id")

outer-join [6 5]:

Id	X1	XY	Y1	right.XY
A	1	x2	1	y1
B	3	x4	3	y3
B	3	x4	5	y5
C	5	x6
C	7	x8
D			7	y7

Return rows from x matching y

(tc/semi-join x y "Id")

_unnamed [2 3]:

Id	X1	XY
A	1	x2
B	3	x4

Return rows from x not matching y

(tc/anti-join x y "Id")

_unnamed [2 3]:

Id	X1	XY
C	5	x6
C	7	x8

More joins

Select columns while joining

(tc/right-join (tc/select-columns x ["Id" "X1"])
                (tc/select-columns y ["Id" "XY"])
                "Id")

right-outer-join [4 4]:

Id	X1	right.Id	XY
A	1	A	y1
B	3	B	y3
B	3	B	y5
		D	y7

(tc/right-join (tc/select-columns x ["Id" "XY"])
                (tc/select-columns y ["Id" "XY"])
                "Id")

right-outer-join [4 4]:

Id	XY	right.Id	right.XY
A	x2	A	y1
B	x4	B	y3
B	x4	B	y5
		D	y7

Aggregate columns while joining

(-> y
    (tc/group-by ["Id"])
    (tc/aggregate {"sumY1" #(dfn/sum (% "Y1"))})
    (tc/right-join x "Id")
    (tc/add-column "X1Y1" (fn [ds] (dfn/* (ds "sumY1")
                                                    (ds "X1"))))
    (tc/select-columns ["right.Id" "X1Y1"]))

right-outer-join [4 2]:

right.Id	X1Y1
A	1.0
B	24.0
C
C

Update columns while joining

(-> x
    (tc/select-columns ["Id" "X1"])
    (tc/map-columns "SqX1" "X1" (fn [x] (* x x)))
    (tc/right-join y "Id")
    (tc/drop-columns ["X1" "Id"]))

right-outer-join [4 4]:

SqX1	right.Id	Y1	XY
1	A	1	y1
9	B	3	y3
9	B	5	y5
	D	7	y7

Adds a list column with rows from y matching x (nest-join)

(-> (tc/left-join x y "Id")
    (tc/drop-columns ["right.Id"])
    (tc/fold-by (tc/column-names x)))

_unnamed [4 5]:

Id	X1	XY	Y1	right.XY
A	1	x2	[1]	[“y1”]
B	3	x4	[3 5]	[“y3” “y5”]
C	5	x6	[]	[]
C	7	x8	[]	[]

Some joins are skipped

Cross join

(def cjds (tc/dataset {:V1 [[2 1 1]]
                        :V2 [[3 2]]}))

cjds

_unnamed [1 2]:

:V1	:V2
[2 1 1]	[3 2]

(reduce #(tc/unroll %1 %2) cjds (tc/column-names cjds))

_unnamed [6 2]:

:V1	:V2
2	3
2	2
1	3
1	2
1	3
1	2

(-> (reduce #(tc/unroll %1 %2) cjds (tc/column-names cjds))
    (tc/unique-by))

_unnamed [4 2]:

:V1	:V2
2	3
2	2
1	3
1	2

Bind

(def x (tc/dataset {:V1 [1 2 3]}))
(def y (tc/dataset {:V1 [4 5 6]}))
(def z (tc/dataset {:V1 [7 8 9]
                     :V2 [0 0 0]}))

x y z

_unnamed [3 1]:

:V1
1
2
3

_unnamed [3 1]:

:V1
4
5
6

_unnamed [3 2]:

:V1	:V2
7	0
8	0
9	0

Bind rows

(tc/bind x y)

_unnamed [6 1]:

:V1
1
2
3
4
5
6

(tc/bind x z)

_unnamed [6 2]:

:V1	:V2
1
2
3
7	0
8	0
9	0

Bind rows using a list

(->> [x y]
     (map-indexed #(tc/add-column %2 :id (repeat %1)))
     (apply tc/bind))

_unnamed [6 2]:

:V1	:id
1	0
2	0
3	0
4	1
5	1
6	1

Bind columns

(tc/append x y)

_unnamed [3 2]:

:V1	:V1
1	4
2	5
3	6

Set operations

(def x (tc/dataset {:V1 [1 2 2 3 3]}))
(def y (tc/dataset {:V1 [2 2 3 4 4]}))

x y

_unnamed [5 1]:

:V1
1
2
2
3
3

_unnamed [5 1]:

:V1
2
2
3
4
4

Intersection

(tc/intersect x y)

intersection [4 1]:

:V1
2
2
3
3

Difference

(tc/difference x y)

difference [1 1]:

:V1
1

Union

(tc/union x y)

union [4 1]:

:V1
1
2
3
4

(tc/concat x y)

_unnamed [10 1]:

:V1
1
2
2
3
3
2
2
3
4
4

Equality not implemented

V1 and V3 multiplied	V4 as lowercase	:V1	:V2	:V3	:V4
0.5	a	1	1	0.5	A
0.5	a	1	7	0.5	A
2.0	b	2	2	1.0	B
2.0	b	2	8	1.0	B
1.5	c	1	3	1.5	C
1.5	c	1	9	1.5	C
1.0	a	2	4	0.5	A
1.0	b	1	5	1.0	B
3.0	c	2	6	1.5	C

:abc-0	:abc-1	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

v1	v3	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	X
2	2	1.0	B	X
1	3	1.5	C	X
2	4	0.5	A	X
1	5	1.0	B	X
2	6	1.5	C	X
1	7	0.5	A	X
2	8	1.0	B	X
1	9	1.5	C	X

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	2	1.0	B	2
1	3	1.5	C	1
2	4	0.5	A	2
1	5	1.0	B	1
2	6	1.5	C	2
1	7	0.5	A	1
2	8	1.0	B	2
1	9	1.5	C	1

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	9
2	2	1.0	B	9
1	3	1.5	C	9
2	4	0.5	A	9
1	5	1.0	B	9
2	6	1.5	C	9
1	7	0.5	A	9
2	8	1.0	B	9
1	9	1.5	C	9

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	5
1	3	1.5	C	5
1	5	1.0	B	5
1	7	0.5	A	5
1	9	1.5	C	5
2	2	1.0	B	4
2	4	0.5	A	4
2	6	1.5	C	4
2	8	1.0	B	4

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	2	1.0	B	:b
1	3	1.5	C	:r
2	4	0.5	A	:b
1	5	1.0	B	:r
2	6	1.5	C	:b
1	7	0.5	A	:r
2	8	1.0	B	:b
1	9	1.5	C	:r

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	4	0.5	A	:b
1	7	0.5	A
2	2	1.0	B	:r
1	5	1.0	B	:b
2	8	1.0	B
1	3	1.5	C	:r
2	6	1.5	C	:b
1	9	1.5	C

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	4	0.5	A	2
1	7	0.5	A	3
2	2	1.0	B	1
1	5	1.0	B	2
2	8	1.0	B	3
1	3	1.5	C	1
2	6	1.5	C	2
1	9	1.5	C	3

V1 and V3 multiplied	V4 as lowercase	:V1	:V2	:V3	:V4
0.5	a	1	1	0.5	A
0.5	a	1	7	0.5	A
2.0	b	2	2	1.0	B
2.0	b	2	8	1.0	B
1.5	c	1	3	1.5	C
1.5	c	1	9	1.5	C
1.0	a	2	4	0.5	A
1.0	b	1	5	1.0	B
3.0	c	2	6	1.5	C

:abc-0	:abc-1	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

v1	v3	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	X
2	2	1.0	B	X
1	3	1.5	C	X
2	4	0.5	A	X
1	5	1.0	B	X
2	6	1.5	C	X
1	7	0.5	A	X
2	8	1.0	B	X
1	9	1.5	C	X

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	2	1.0	B	2
1	3	1.5	C	1
2	4	0.5	A	2
1	5	1.0	B	1
2	6	1.5	C	2
1	7	0.5	A	1
2	8	1.0	B	2
1	9	1.5	C	1

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	9
2	2	1.0	B	9
1	3	1.5	C	9
2	4	0.5	A	9
1	5	1.0	B	9
2	6	1.5	C	9
1	7	0.5	A	9
2	8	1.0	B	9
1	9	1.5	C	9

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	5
1	3	1.5	C	5
1	5	1.0	B	5
1	7	0.5	A	5
1	9	1.5	C	5
2	2	1.0	B	4
2	4	0.5	A	4
2	6	1.5	C	4
2	8	1.0	B	4

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	2	1.0	B	:b
1	3	1.5	C	:r
2	4	0.5	A	:b
1	5	1.0	B	:r
2	6	1.5	C	:b
1	7	0.5	A	:r
2	8	1.0	B	:b
1	9	1.5	C	:r

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	4	0.5	A	:b
1	7	0.5	A
2	2	1.0	B	:r
1	5	1.0	B	:b
2	8	1.0	B
1	3	1.5	C	:r
2	6	1.5	C	:b
1	9	1.5	C

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	4	0.5	A	2
1	7	0.5	A	3
2	2	1.0	B	1
1	5	1.0	B	2
2	8	1.0	B	3
1	3	1.5	C	1
2	6	1.5	C	2
1	9	1.5	C	3

Dataset (data frame) manipulation API for the tech.ml.dataset library

GenerateMe

2023-11-26

Introduction

Functionality

Dataset

Dataset creation

Saving

Nippy

Dataset related functions

Columns and rows

Single entry

Printing

Group-by

Grouping

Ungrouping

Other functions

Columns

Names

Select

Drop

Rename

Add or update

Update

Map

Reorder

Type conversion

Rows

Select

Drop

Map rows

Other

Aggregate

Column

Crosstab

Order

Unique

Strategies

Missing

Select

Drop

Replace

Inject

Join/Separate Columns

Join

Tidyr examples

Separate

Tidyr examples

Array column conversion

Fold/Unroll Rows

Fold-by

Unroll

Reshape

Longer

Wider

Reshaping

Join/Concat Datasets

Left

Right

Inner

Full

Semi

Anti

Hashing

Cross

Expand

Complete

asof

Concat

Concat grouped dataset

Union

Bind

Append

Intersection

Difference

Split into train/test

k-Fold

Bootstrap

Holdout

Holdouts

What is `data.table`?

Get the first two rows from `flights`.

Sort `flights` first by column `origin` in ascending order, and then by `dest` in descending order

Select `arr_delay` column, but return it as a vector

Select `arr_delay` column, but return as a data.table instead

Select both `arr_delay` and `dep_delay` columns

Select both `arr_delay` and `dep_delay` columns and rename them to `delay_arr` and `delay_dep`

How can we get the total number of trips for each `origin`, `dest` pair for carrier code “AA”?

How can we get the average arrival and departure delay for each `orig`,`dest` pair for each month for carrier code “AA”?

Can `by` accept expressions as well or does it just take columns?

Do we have to compute `mean()` for each column individually?

How can we specify just the columns we would like to compute the `mean()` on?

What if we would like to have all the values of column `a` and `b` concatenated, but returned as a list column?

V1 and V3 multiplied	V4 as lowercase	:V1	:V2	:V3	:V4
0.5	a	1	1	0.5	A
0.5	a	1	7	0.5	A
2.0	b	2	2	1.0	B
2.0	b	2	8	1.0	B
1.5	c	1	3	1.5	C
1.5	c	1	9	1.5	C
1.0	a	2	4	0.5	A
1.0	b	1	5	1.0	B
3.0	c	2	6	1.5	C

:abc-0	:abc-1	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

v1	v3	:V1	:V2	:V3	:V4
1	0.5	1	1	0.5	A
1	0.5	1	7	0.5	A
2	1.0	2	2	1.0	B
2	1.0	2	8	1.0	B
1	1.5	1	3	1.5	C
1	1.5	1	9	1.5	C
2	0.5	2	4	0.5	A
1	1.0	1	5	1.0	B
2	1.5	2	6	1.5	C

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	X
2	2	1.0	B	X
1	3	1.5	C	X
2	4	0.5	A	X
1	5	1.0	B	X
2	6	1.5	C	X
1	7	0.5	A	X
2	8	1.0	B	X
1	9	1.5	C	X

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	2	1.0	B	2
1	3	1.5	C	1
2	4	0.5	A	2
1	5	1.0	B	1
2	6	1.5	C	2
1	7	0.5	A	1
2	8	1.0	B	2
1	9	1.5	C	1

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	9
2	2	1.0	B	9
1	3	1.5	C	9
2	4	0.5	A	9
1	5	1.0	B	9
2	6	1.5	C	9
1	7	0.5	A	9
2	8	1.0	B	9
1	9	1.5	C	9

:V1	:V2	:V3	:V4	:row-count
1	1	0.5	A	5
1	3	1.5	C	5
1	5	1.0	B	5
1	7	0.5	A	5
1	9	1.5	C	5
2	2	1.0	B	4
2	4	0.5	A	4
2	6	1.5	C	4
2	8	1.0	B	4

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	2	1.0	B	:b
1	3	1.5	C	:r
2	4	0.5	A	:b
1	5	1.0	B	:r
2	6	1.5	C	:b
1	7	0.5	A	:r
2	8	1.0	B	:b
1	9	1.5	C	:r

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	:r
2	4	0.5	A	:b
1	7	0.5	A
2	2	1.0	B	:r
1	5	1.0	B	:b
2	8	1.0	B
1	3	1.5	C	:r
2	6	1.5	C	:b
1	9	1.5	C

:V1	:V2	:V3	:V4	:V5
1	1	0.5	A	1
2	4	0.5	A	2
1	7	0.5	A	3
2	2	1.0	B	1
1	5	1.0	B	2
2	8	1.0	B	3
1	3	1.5	C	1
2	6	1.5	C	2
1	9	1.5	C	3