abstract struct Crysda::DataCol

• Crysda::DataCol
• Struct
• Value
• Object

Overview

Abstract base struct for column types of Int32?, Int64?, Float64?, Bool?, String?, DataFrame? or Any type

Direct Known Subclasses

• Crysda::AnyCol
• Crysda::BoolCol
• Crysda::DFCol
• Crysda::Float64Col
• Crysda::Int32Col
• Crysda::Int64Col
• Crysda::StringCol

Defined in:

Constructors

• .new(name)

Instance Method Summary

• #*(val : DataCol)
• #*(val : Number)
• #+(val : Number)
• #+(val : DataCol)
• #+(val : Iterable)
• #+(val : String)
• #-(val : Number)
• #-(val : DataCol)
• #-
• #/(val : DataCol)
• #/(val : Number)
• #<(val : Any | DataCol)
• #<=(val : Any | DataCol)
• #==(i : Any)
• #==(other : self)
• #>(val : Any | DataCol)
• #>=(val : Any | DataCol)
• #[](*args, **options)
• #[](*args, **options, &)
• #[]?(*args, **options, &)
• #[]?(*args, **options)
• #as_b

  Returns BoolCol values as an array of Bool?

• #as_f64

  Returns Int32Col, Int64Col, Float64Col values as an array of Float64?

• #as_i

  Returns Int32Col values as an array of Int32?

• #as_i64

  Returns Int32Col or Int64Col values as an array of Int64?

• #as_s

  Returns values as Array of String?

• #compare(left : Int32, right : Int32, null_last = true) : Int32
• #cumsum

  Calculates the cumulative sum of the column values.

• #desc

  Creates a sorting attribute that inverts the order of the argument

• #div(val)
• #equals(other : self)
• #has_nulls?
• #hash(hasher)

  See Object#hash(hasher)

• #is_na

  Maps a column to true for NA values and false otherwise Returns a Bool array with NA values marked as true, while others are marked as false

• #is_not_na

  Maps a column to false for NA values and true otherwise Returns a Bool array with NA values marked as false, while others are marked as true

• #lag(n = 1, default : Any = nil)

  Returns the "previous" column values.

• #lead(n = 1, default : Any = nil)

  Returns the "next" column values.

• #map(&)
• #matching(missing_as = false, &) : Array(Bool)

  Match a text column in a NA-aware manner to create a predicate vector for filtering.

• #max(remove_na = false)

  Calculates the maximum of the column values.

• #mean(remove_na = false)

  Calculates the arithmetic mean of the column values.

• #median(remove_na = false)

  Calculates the median of the column values.

• #min(remove_na = false)

  Calculates the minimum of the column values.

• #minus(val)
• #name : String
• #order(na_last = true)

  #order returns the index each element would have in an ascending list ` y = Float64Col.new("foo", [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1]) y.order # => [8, 1, 3, 9, 2, 6, 7, 0, 4, 5] `

• #pct_change : DataCol

  Calculates the percentage change between the current and a prior column value.

• #plus(val)
• #rank(na_last = true)

  #rank returns the order of each element in an ascending list ` y = Float64Col.new("foo", [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1]) y.rank # => [7, 1, 4, 2, 8, 9, 5, 6, 0, 3] `

• #sd(remove_na = false)

  Calculates the standard deviation of the column values.

• #size(*args, **options)
• #size(*args, **options, &)
• #sum(remove_na = false)

  Calculates the sum of the column values.

• #times(val)
• #to_s(io : IO) : Nil

  Same as #inspect(io).

• #to_s

  Returns a string representation of this object.

• #values : Array

Constructor Detail

Instance Method Detail

y = Float64Col.new("foo", [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1])
y.order # => [8, 1, 3, 9, 2, 6, 7, 0, 4, 5]

y = Float64Col.new("foo", [3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1])
y.rank # => [7, 1, 4, 2, 8, 9, 5, 6, 0, 3]