tablecloth.api.utils

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Clojure only.

->str
as-regular-dataset
column-names
grouped?
iterable-sequence?
map-inst?
mark-as-group
parallel-concat
process-group-data
rank
read-nippy
type?
unmark-group
write-nippy!

->str^clj

(->str v)

source

as-regular-dataset^clj

(as-regular-dataset ds)

Remove grouping tag

Remove grouping tag

source raw docstring

column-names^clj

(column-names ds)

(column-names ds columns-selector)

(column-names ds columns-selector meta-field)

Returns column names, given a selector. 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.

Returns column names, given a selector.
  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.

source raw docstring

grouped?^clj

(grouped? ds)

Is dataset represents grouped dataset (result of group-by)?

Is `dataset` represents grouped dataset (result of `group-by`)?

source raw docstring

iterable-sequence?^clj

(iterable-sequence? xs)

Check if object is sequential, is column or maybe a reader (iterable)?

Check if object is sequential, is column or maybe a reader (iterable)?

source raw docstring

map-inst?^clj

(map-inst? m)

source

mark-as-group^clj

(mark-as-group ds)

Add grouping tag

Add grouping tag

source raw docstring

parallel-concat^clj

(parallel-concat & dss)

source

process-group-data^clj

(process-group-data ds f)

(process-group-data ds f parallel?)

Internal: The passed-in function is applied on all groups

Internal: The passed-in function is applied on all groups

source raw docstring

rank^clj

(rank vs)

(rank vs ties)

(rank vs ties desc?)

Sample ranks. See R docs. Rank uses 0 based indexing.

Possible tie strategies: :average, :first, :last, :random, :min, :max, :dense. :dense is the same as in data.table::frank from R

Sample ranks. See [R docs](https://www.rdocumentation.org/packages/base/versions/3.6.1/topics/rank).
Rank uses 0 based indexing.

Possible tie strategies: `:average`, `:first`, `:last`, `:random`, `:min`, `:max`, `:dense`.
`:dense` is the same as in `data.table::frank` from R

source raw docstring