Class: Daru::DataFrame

Inherits:
Object show all
Extended by:
Gem::Deprecate
Includes:
Maths::Arithmetic::DataFrame, Maths::Statistics::DataFrame
Defined in:
lib/daru/dataframe.rb,
lib/daru/extensions/rserve.rb,
lib/daru/extensions/which_dsl.rb

Overview

rubocop:disable Metrics/ClassLength

Defined Under Namespace

Modules: SetMultiIndexStrategy, SetSingleIndexStrategy

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Methods included from Maths::Statistics::DataFrame

#acf, #correlation, #count, #covariance, #cumsum, #describe, #ema, #max, #mean, #median, #min, #mode, #percent_change, #product, #range, #rolling_count, #rolling_max, #rolling_mean, #rolling_median, #rolling_min, #rolling_std, #rolling_variance, #standardize, #std, #sum, #variance_sample

Methods included from Maths::Arithmetic::DataFrame

#%, #*, #**, #+, #-, #/, #exp, #round, #sqrt

Constructor Details

#initialize(source = {}, opts = {}) ⇒ DataFrame

DataFrame basically consists of an Array of Vector objects. These objects are indexed by row and column by vectors and index Index objects.

Arguments

  • source - Source from the DataFrame is to be initialized. Can be a Hash

of names and vectors (array or Daru::Vector), an array of arrays or array of Daru::Vectors.

Options

:order - An Array/Daru::Index/Daru::MultiIndex containing the order in which Vectors should appear in the DataFrame.

:index - An Array/Daru::Index/Daru::MultiIndex containing the order in which rows of the DataFrame will be named.

:name - A name for the DataFrame.

:clone - Specify as true or false. When set to false, and Vector objects are passed for the source, the Vector objects will not duplicated when creating the DataFrame. Will have no effect if Array is passed in the source, or if the passed Daru::Vectors have different indexes. Default to true.

Usage

df = Daru::DataFrame.new
# =>
# <Daru::DataFrame(0x0)>
# Creates an empty DataFrame with no rows or columns.

df = Daru::DataFrame.new({}, order: [:a, :b])
#<Daru::DataFrame(0x2)>
  a   b
# Creates a DataFrame with no rows and columns :a and :b

df = Daru::DataFrame.new({a: [1,2,3,4], b: [6,7,8,9]}, order: [:b, :a],
  index: [:a, :b, :c, :d], name: :spider_man)

# =>
# <Daru::DataFrame:80766980 @name = spider_man @size = 4>
#             b          a
#  a          6          1
#  b          7          2
#  c          8          3
#  d          9          4

df = Daru::DataFrame.new([[1,2,3,4],[6,7,8,9]], name: :bat_man)

# =>
# #<Daru::DataFrame: bat_man (4x2)>
#             0          1
#  0          1          6
#  1          2          7
#  2          3          8
#  3          4          9

# Dataframe having Index name

df = Daru::DataFrame.new({a: [1,2,3,4], b: [6,7,8,9]}, order: [:b, :a],
  index: Daru::Index.new([:a, :b, :c, :d], name: 'idx_name'),
  name: :spider_man)

# =>
# <Daru::DataFrame:80766980 @name = spider_man @size = 4>
# idx_name            b          a
#        a          6          1
#        b          7          2
#        c          8          3
#        d          9          4

idx = Daru::Index.new [100, 99, 101, 1, 2], name: "s1"
=> #<Daru::Index(5): s1 {100, 99, 101, 1, 2}>

df = Daru::DataFrame.new({b: [11,12,13,14,15], a: [1,2,3,4,5],
  c: [11,22,33,44,55]},
  order: [:a, :b, :c],
  index: idx)
 # =>
 #<Daru::DataFrame(5x3)>
 #   s1   a   b   c
 #  100   1  11  11
 #   99   2  12  22
 #  101   3  13  33
 #    1   4  14  44
 #    2   5  15  55

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# File 'lib/daru/dataframe.rb', line 346

def initialize source={}, opts={} # rubocop:disable Metrics/MethodLength
  vectors, index = opts[:order], opts[:index] # FIXME: just keyword arges after Ruby 2.1
  @data = []
  @name = opts[:name]

  case source
  when [], {}
    create_empty_vectors(vectors, index)
  when Array
    initialize_from_array source, vectors, index, opts
  when Hash
    initialize_from_hash source, vectors, index, opts
  when ->(s) { s.empty? } # TODO: likely want to remove this case
    create_empty_vectors(vectors, index)
  end

  set_size
  validate
  update
end

Dynamic Method Handling

This class handles dynamic methods through the method_missing method

#method_missing(name, *args, &block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2308

def method_missing(name, *args, &block)
  case
  when name =~ /(.+)\=/
    name = name[/(.+)\=/].delete('=')
    name = name.to_sym unless has_vector?(name)
    insert_or_modify_vector [name], args[0]
  when has_vector?(name)
    self[name]
  when has_vector?(name.to_s)
    self[name.to_s]
  else
    super
  end
end

Instance Attribute Details

#dataObject (readonly)

TOREMOVE


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# File 'lib/daru/dataframe.rb', line 246

def data
  @data
end

#indexObject

The index of the rows of the DataFrame


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# File 'lib/daru/dataframe.rb', line 249

def index
  @index
end

#nameObject (readonly)

The name of the DataFrame


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# File 'lib/daru/dataframe.rb', line 252

def name
  @name
end

#sizeObject (readonly)

The number of rows present in the DataFrame


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# File 'lib/daru/dataframe.rb', line 255

def size
  @size
end

#vectorsObject

The vectors (columns) index of the DataFrame


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# File 'lib/daru/dataframe.rb', line 244

def vectors
  @vectors
end

Class Method Details

._load(data) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2233

def self._load data
  h = Marshal.load data
  Daru::DataFrame.new(h[:data],
    index: h[:index],
    order: h[:order],
    name:  h[:name])
end

.crosstab_by_assignation(rows, columns, values) ⇒ Object

Generates a new dataset, using three vectors

  • Rows

  • Columns

  • Values

For example, you have these values

x   y   v
a   a   0
a   b   1
b   a   1
b   b   0

You obtain

id  a   b
 a  0   1
 b  1   0

Useful to process outputs from databases


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# File 'lib/daru/dataframe.rb', line 202

def crosstab_by_assignation rows, columns, values
  raise 'Three vectors should be equal size' if
    rows.size != columns.size || rows.size!=values.size

  data = Hash.new { |h, col|
    h[col] = rows.factors.map { |r| [r, nil] }.to_h
  }
  columns.zip(rows, values).each { |c, r, v| data[c][r] = v }

  # FIXME: in fact, WITHOUT this line you'll obtain more "right"
  # data: with vectors having "rows" as an index...
  data = data.map { |c, r| [c, r.values] }.to_h
  data[:_id] = rows.factors

  DataFrame.new(data)
end

.from_activerecord(relation, *fields) ⇒ Object

Read a dataframe from AR::Relation

USE:

# When Post model is defined as:
class Post < ActiveRecord::Base
  scope :active, -> { where.not(published_at: nil) }
end

# You can load active posts into a dataframe by:
Daru::DataFrame.from_activerecord(Post.active, :title, :published_at)

Parameters:

  • relation (ActiveRecord::Relation)

    An AR::Relation object from which data is loaded

  • fields (Array)

    Field names to be loaded (optional)

Returns:

  • A dataframe containing the data loaded from the relation


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# File 'lib/daru/dataframe.rb', line 103

def from_activerecord relation, *fields
  Daru::IO.from_activerecord relation, *fields
end

.from_csv(path, opts = {}, &block) ⇒ Object

Load data from a CSV file. Specify an optional block to grab the CSV object and pre-condition it (for example use the `convert` or `header_convert` methods).

Arguments

  • path - Local path / Remote URL of the file to load specified as a String.

Options

Accepts the same options as the Daru::DataFrame constructor and CSV.open() and uses those to eventually construct the resulting DataFrame.

Verbose Description

You can specify all the options to the `.from_csv` function that you do to the Ruby `CSV.read()` function, since this is what is used internally.

For example, if the columns in your CSV file are separated by something other that commas, you can use the `:col_sep` option. If you want to convert numeric values to numbers and not keep them as strings, you can use the `:converters` option and set it to `:numeric`.

The `.from_csv` function uses the following defaults for reading CSV files (that are passed into the `CSV.read()` function):

{
  :col_sep           => ',',
  :converters        => :numeric
}

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# File 'lib/daru/dataframe.rb', line 50

def from_csv path, opts={}, &block
  Daru::IO.from_csv path, opts, &block
end

.from_excel(path, opts = {}, &block) ⇒ Object

Read data from an Excel file into a DataFrame.

Arguments

  • path - Path of the file to be read.

Options

*:worksheet_id - ID of the worksheet that is to be read.


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# File 'lib/daru/dataframe.rb', line 63

def from_excel path, opts={}, &block
  Daru::IO.from_excel path, opts, &block
end

.from_html(path, fields = {}) ⇒ Object

Read the table data from a remote html file. Please note that this module works only for static table elements on a HTML page, and won't work in cases where the data is being loaded into the HTML table by Javascript.

By default - all <th> tag elements in the first proper row are considered as the order, and all the <th> tag elements in the first column are considered as the index.

Arguments

  • path [String] - URL of the target HTML file.

  • fields [Hash] -

    :match - A String to match and choose a particular table(s) from multiple tables of a HTML page.

    :order - An Array which would act as the user-defined order, to override the parsed Daru::DataFrame.

    :index - An Array which would act as the user-defined index, to override the parsed Daru::DataFrame.

    :name - A String that manually assigns a name to the scraped Daru::DataFrame, for user's preference.

Returns

An Array of Daru::DataFrames, with each dataframe corresponding to a HTML table on that webpage.

Usage

dfs = Daru::DataFrame.from_html("http://www.moneycontrol.com/", match: "Sun Pharma")
dfs.count
# => 4

dfs.first
#
# => <Daru::DataFrame(5x4)>
#          Company      Price     Change Value (Rs
#     0 Sun Pharma     502.60     -65.05   2,117.87
#     1   Reliance    1356.90      19.60     745.10
#     2 Tech Mahin     379.45     -49.70     650.22
#     3        ITC     315.85       6.75     621.12
#     4       HDFC    1598.85      50.95     553.91

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# File 'lib/daru/dataframe.rb', line 162

def from_html path, fields={}
  Daru::IO.from_html path, fields
end

.from_plaintext(path, fields) ⇒ Object

Read the database from a plaintext file. For this method to work, the data should be present in a plain text file in columns. See spec/fixtures/bank2.dat for an example.

Arguments

  • path - Path of the file to be read.

  • fields - Vector names of the resulting database.

Usage

df = Daru::DataFrame.from_plaintext 'spec/fixtures/bank2.dat', [:v1,:v2,:v3,:v4,:v5,:v6]

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# File 'lib/daru/dataframe.rb', line 119

def from_plaintext path, fields
  Daru::IO.from_plaintext path, fields
end

.from_sql(dbh, query) ⇒ Object

Read a database query and returns a Dataset

USE:

dbh = DBI.connect("DBI:Mysql:database:localhost", "user", "password")
Daru::DataFrame.from_sql(dbh, "SELECT * FROM test")

#Alternatively

require 'dbi'
Daru::DataFrame.from_sql("path/to/sqlite.db", "SELECT * FROM test")

Parameters:

  • dbh (DBI::DatabaseHandle, String)

    A DBI connection OR Path to a SQlite3 database.

  • query (String)

    The query to be executed

Returns:

  • A dataframe containing the data resulting from the query


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# File 'lib/daru/dataframe.rb', line 83

def from_sql dbh, query
  Daru::IO.from_sql dbh, query
end

.rows(source, opts = {}) ⇒ Object

Create DataFrame by specifying rows as an Array of Arrays or Array of Daru::Vector objects.

Raises:


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# File 'lib/daru/dataframe.rb', line 168

def rows source, opts={}
  raise SizeError, 'All vectors must have same length' \
    unless source.all? { |v| v.size == source.first.size }

  opts[:order] ||= guess_order(source)

  if ArrayHelper.array_of?(source, Array) || source.empty?
    DataFrame.new(source.transpose, opts)
  elsif ArrayHelper.array_of?(source, Vector)
    from_vector_rows(source, opts)
  else
    raise ArgumentError, "Can't create DataFrame from #{source}"
  end
end

Instance Method Details

#==(other) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2283

def == other
  self.class == other.class   &&
    @size    == other.size    &&
    @index   == other.index   &&
    @vectors == other.vectors &&
    @vectors.to_a.all? { |v| self[v] == other[v] }
end

#[](*names) ⇒ Object

Access row or vector. Specify name of row/vector followed by axis(:row, :vector). Defaults to :vector. Use of this method is not recommended for accessing rows. Use df.row for accessing row with index ':a'.


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# File 'lib/daru/dataframe.rb', line 392

def [](*names)
  axis = extract_axis(names, :vector)
  dispatch_to_axis axis, :access, *names
end

#[]=(*args) ⇒ Object

Insert a new row/vector of the specified name or modify a previous row. Instead of using this method directly, use df.row = [1,2,3] to set/create a row ':a' to [1,2,3], or df.vector = [1,2,3] for vectors.

In case a Daru::Vector is specified after the equality the sign, the indexes of the vector will be matched against the row/vector indexes of the DataFrame before an insertion is performed. Unmatched indexes will be set to nil.


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# File 'lib/daru/dataframe.rb', line 534

def []=(*args)
  vector = args.pop
  axis = extract_axis(args)
  names = args

  dispatch_to_axis axis, :insert_or_modify, names, vector
end

#_dump(_depth) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2224

def _dump(_depth)
  Marshal.dump(
    data:  @data,
    index: @index.to_a,
    order: @vectors.to_a,
    name:  @name
  )
end

#access_row_tuples_by_indexs(*indexes) ⇒ Array

Returns array of row tuples at given index(s)

Examples:

Using Daru::Index

df = Daru::DataFrame.new({
  a: [1, 2, 3],
  b: ['a', 'a', 'b']
})

df.access_row_tuples_by_indexs(1,2)
# => [[2, "a"], [3, "b"]]

df.index = Daru::Index.new([:one,:two,:three])
df.access_row_tuples_by_indexs(:one,:three)
# => [[1, "a"], [3, "b"]]

Using Daru::MultiIndex

mi_idx = Daru::MultiIndex.from_tuples [
  [:a,:one,:bar],
  [:a,:one,:baz],
  [:b,:two,:bar],
  [:a,:two,:baz],
]
df_mi = Daru::DataFrame.new({
  a: 1..4,
  b: 'a'..'d'
}, index: mi_idx )

df_mi.access_row_tuples_by_indexs(:b, :two, :bar)
# => [[3, "c"]]
df_mi.access_row_tuples_by_indexs(:a)
# => [[1, "a"], [2, "b"], [4, "d"]]

Parameters:

  • indexes (Array)

    index(s) at which row tuples are retrieved

Returns:

  • (Array)

    returns array of row tuples at given index(s)


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# File 'lib/daru/dataframe.rb', line 2398

def access_row_tuples_by_indexs *indexes
  return get_sub_dataframe(indexes, by_position: false).map_rows(&:to_a) if
  @index.is_a?(Daru::MultiIndex)
  positions = @index.pos(*indexes)
  if positions.is_a? Numeric
    row = get_rows_for([positions])
    row.first.is_a?(Array) ? row : [row]
  else
    new_rows = get_rows_for(indexes, by_position: false)
    indexes.map { |index| new_rows.map { |r| r[index] } }
  end
end

#add_row(row, index = nil) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 542

def add_row row, index=nil
  self.row[*(index || @size)] = row
end

#add_vector(n, vector) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 546

def add_vector n, vector
  self[n] = vector
end

#add_vectors_by_split(name, join = '-', sep = Daru::SPLIT_TOKEN) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1314

def add_vectors_by_split(name,join='-',sep=Daru::SPLIT_TOKEN)
  self[name]
    .split_by_separator(sep)
    .each { |k,v| self["#{name}#{join}#{k}".to_sym] = v }
end

#add_vectors_by_split_recode(nm, join = '-', sep = Daru::SPLIT_TOKEN) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2029

def add_vectors_by_split_recode(nm, join='-', sep=Daru::SPLIT_TOKEN)
  self[nm]
    .split_by_separator(sep)
    .each_with_index do |(k, v), i|
      v.rename "#{nm}:#{k}"
      self["#{nm}#{join}#{i + 1}".to_sym] = v
    end
end

#aggregate(options = {}, multi_index_level = -1)) ⇒ Daru::DataFrame

Function to use for aggregating the data.

Note: `GroupBy` class `aggregate` method uses this `aggregate` method internally.

Examples:

df = Daru::DataFrame.new(
   {col: [:a, :b, :c, :d, :e], num: [52,12,07,17,01]})
=> #<Daru::DataFrame(5x2)>
     col num
   0   a  52
   1   b  12
   2   c   7
   3   d  17
   4   e   1

 df.aggregate(num_100_times: ->(df) { (df.num*100).first })
=> #<Daru::DataFrame(5x1)>
            num_100_ti
          0       5200
          1       1200
          2        700
          3       1700
          4        100

When we have duplicate index :

idx = Daru::CategoricalIndex.new [:a, :b, :a, :a, :c]
df = Daru::DataFrame.new({num: [52,12,07,17,01]}, index: idx)
=> #<Daru::DataFrame(5x1)>
     num
   a  52
   b  12
   a   7
   a  17
   c   1

df.aggregate(num: :mean)
=> #<Daru::DataFrame(3x1)>
                   num
          a 25.3333333
          b         12
          c          1

Parameters:

  • options (Hash) (defaults to: {})

    options for column, you want in resultant dataframe

Returns:


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# File 'lib/daru/dataframe.rb', line 2458

def aggregate(options={}, multi_index_level=-1)
  if block_given?
    positions_tuples, new_index = yield(@index) # note: use of yield is private for now
  else
    positions_tuples, new_index = group_index_for_aggregation(@index, multi_index_level)
  end

  colmn_value = aggregate_by_positions_tuples(options, positions_tuples)

  Daru::DataFrame.new(colmn_value, index: new_index, order: options.keys)
end

#all?(axis = :vector, &block) ⇒ Boolean

Works like Array#all?

Examples:

Using all?

df = Daru::DataFrame.new({a: [1,2,3,4,5], b: ['a', 'b', 'c', 'd', 'e']})
df.all?(:row) do |row|
  row[:a] < 10
end #=> true

Parameters:

  • axis (Symbol) (defaults to: :vector)

    (:vector) The axis to iterate over. Can be :vector or :row. A Daru::Vector object is yielded in the block.

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1371

def all? axis=:vector, &block
  if %i[vector column].include?(axis)
    @data.all?(&block)
  elsif axis == :row
    each_row.all?(&block)
  else
    raise ArgumentError, "Unidentified axis #{axis}"
  end
end

#any?(axis = :vector, &block) ⇒ Boolean

Works like Array#any?.

Examples:

Using any?

df = Daru::DataFrame.new({a: [1,2,3,4,5], b: ['a', 'b', 'c', 'd', 'e']})
df.any?(:row) do |row|
  row[:a] < 3 and row[:b] == 'b'
end #=> true

Parameters:

  • axis (Symbol) (defaults to: :vector)

    (:vector) The axis to iterate over. Can be :vector or :row. A Daru::Vector object is yielded in the block.

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1349

def any? axis=:vector, &block
  if %i[vector column].include?(axis)
    @data.any?(&block)
  elsif axis == :row
    each_row do |row|
      return true if yield(row)
    end
    false
  else
    raise ArgumentError, "Unidentified axis #{axis}"
  end
end

#apply_method(method, keys: nil, by_position: true) ⇒ Object Also known as: apply_method_on_sub_df


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# File 'lib/daru/dataframe.rb', line 1026

def apply_method(method, keys: nil, by_position: true)
  df = keys ? get_sub_dataframe(keys, by_position: by_position) : self

  case method
  when Symbol then df.send(method)
  when Proc   then method.call(df)
  when Array  then method.map(&:to_proc).map { |proc| proc.call(df) } # works with Array of both Symbol and/or Proc
  else raise
  end
end

#at(*positions) ⇒ Daru::Vector, Daru::DataFrame

Retrive vectors by positions

Examples:

df = Daru::DataFrame.new({
  a: [1, 2, 3],
  b: ['a', 'b', 'c']
})
df.at 0
# => #<Daru::Vector(3)>
#       a
#   0   1
#   1   2
#   2   3

Parameters:

  • positions (Array<Integer>)

    of vectors to retrive

Returns:


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# File 'lib/daru/dataframe.rb', line 472

def at *positions
  if AXES.include? positions.last
    axis = positions.pop
    return row_at(*positions) if axis == :row
  end

  original_positions = positions
  positions = coerce_positions(*positions, ncols)
  validate_positions(*positions, ncols)

  if positions.is_a? Integer
    @data[positions].dup
  else
    Daru::DataFrame.new positions.map { |pos| @data[pos].dup },
      index: @index,
      order: @vectors.at(*original_positions),
      name: @name
  end
end

#bootstrap(n = nil) ⇒ Daru::DataFrame

Creates a DataFrame with the random data, of n size. If n not given, uses original number of rows.

Returns:


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# File 'lib/daru/dataframe.rb', line 1120

def bootstrap(n=nil)
  n ||= nrows
  Daru::DataFrame.new({}, order: @vectors).tap do |df_boot|
    n.times do
      df_boot.add_row(row[rand(n)])
    end
    df_boot.update
  end
end

#clone(*vectors_to_clone) ⇒ Object

Returns a 'view' of the DataFrame, i.e the object ID's of vectors are preserved.

Arguments

vectors_to_clone - Names of vectors to clone. Optional. Will return a view of the whole data frame otherwise.


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# File 'lib/daru/dataframe.rb', line 611

def clone *vectors_to_clone
  vectors_to_clone.flatten! if ArrayHelper.array_of?(vectors_to_clone, Array)
  vectors_to_clone = @vectors.to_a if vectors_to_clone.empty?

  h = vectors_to_clone.map { |vec| [vec, self[vec]] }.to_h
  Daru::DataFrame.new(h, clone: false, order: vectors_to_clone, name: @name)
end

#clone_only_validObject

Returns a 'shallow' copy of DataFrame if missing data is not present, or a full copy of only valid data if missing data is present.


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# File 'lib/daru/dataframe.rb', line 621

def clone_only_valid
  if include_values?(*Daru::MISSING_VALUES)
    reject_values(*Daru::MISSING_VALUES)
  else
    clone
  end
end

#clone_structureObject

Only clone the structure of the DataFrame.


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# File 'lib/daru/dataframe.rb', line 600

def clone_structure
  Daru::DataFrame.new([], order: @vectors.dup, index: @index.dup, name: @name)
end

#collect(axis = :vector, &block) ⇒ Object

Iterate over a row or vector and return results in a Daru::Vector. Specify axis with :vector or :row. Default to :vector.

Description

The #collect iterator works similar to #map, the only difference being that it returns a Daru::Vector comprising of the results of each block run. The resultant Vector has the same index as that of the axis over which collect has iterated. It also accepts the optional axis argument.

Arguments

  • axis - The axis to iterate over. Can be :vector (or :column)

or :row. Default to :vector.


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# File 'lib/daru/dataframe.rb', line 865

def collect axis=:vector, &block
  dispatch_to_axis_pl axis, :collect, &block
end

#collect_matrix::Matrix

Generate a matrix, based on vector names of the DataFrame.

:nocov: FIXME: Even not trying to cover this: I can't get, how it is expected to work.… – zverok

Returns:


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# File 'lib/daru/dataframe.rb', line 1072

def collect_matrix
  return to_enum(:collect_matrix) unless block_given?

  vecs = vectors.to_a
  rows = vecs.collect { |row|
    vecs.collect { |col|
      yield row,col
    }
  }

  Matrix.rows(rows)
end

#collect_row_with_index(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1046

def collect_row_with_index &block
  return to_enum(:collect_row_with_index) unless block_given?

  Daru::Vector.new(each_row_with_index.map(&block), index: @index)
end

#collect_rows(&block) ⇒ Object

Retrieves a Daru::Vector, based on the result of calculation performed on each row.


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# File 'lib/daru/dataframe.rb', line 1040

def collect_rows &block
  return to_enum(:collect_rows) unless block_given?

  Daru::Vector.new(each_row.map(&block), index: @index)
end

#collect_vector_with_index(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1060

def collect_vector_with_index &block
  return to_enum(:collect_vector_with_index) unless block_given?

  Daru::Vector.new(each_vector_with_index.map(&block), index: @vectors)
end

#collect_vectors(&block) ⇒ Object

Retrives a Daru::Vector, based on the result of calculation performed on each vector.


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# File 'lib/daru/dataframe.rb', line 1054

def collect_vectors &block
  return to_enum(:collect_vectors) unless block_given?

  Daru::Vector.new(each_vector.map(&block), index: @vectors)
end

#compute(text, &block) ⇒ Object

Returns a vector, based on a string with a calculation based on vector.

The calculation will be eval'ed, so you can put any variable or expression valid on ruby.

For example:

a = Daru::Vector.new [1,2]
b = Daru::Vector.new [3,4]
ds = Daru::DataFrame.new({:a => a,:b => b})
ds.compute("a+b")
=> Vector [4,6]

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# File 'lib/daru/dataframe.rb', line 1239

def compute text, &block
  return instance_eval(&block) if block_given?
  instance_eval(text)
end

#concat(other_df) ⇒ Object

Concatenate another DataFrame along corresponding columns. If columns do not exist in both dataframes, they are filled with nils


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# File 'lib/daru/dataframe.rb', line 1526

def concat other_df
  vectors = (@vectors.to_a + other_df.vectors.to_a).uniq

  data = vectors.map do |v|
    get_vector_anyways(v).dup.concat(other_df.get_vector_anyways(v))
  end

  Daru::DataFrame.new(data, order: vectors)
end

#create_sql(table, charset = 'UTF8') ⇒ Object

Create a sql, basen on a given Dataset

Arguments

  • table - String specifying name of the table that will created in SQL.

  • charset - Character set. Default is “UTF8”.

Examples:


ds = Daru::DataFrame.new({
 :id   => Daru::Vector.new([1,2,3,4,5]),
 :name => Daru::Vector.new(%w{Alex Peter Susan Mary John})
})
ds.create_sql('names')
 #=>"CREATE TABLE names (id INTEGER,\n name VARCHAR (255)) CHARACTER SET=UTF8;"

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# File 'lib/daru/dataframe.rb', line 2054

def create_sql(table,charset='UTF8')
  sql    = "CREATE TABLE #{table} ("
  fields = vectors.to_a.collect do |f|
    v = self[f]
    f.to_s + ' ' + v.db_type
  end

  sql + fields.join(",\n ")+") CHARACTER SET=#{charset};"
end

#delete_row(index) ⇒ Object

Delete a row

Raises:

  • (IndexError)

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# File 'lib/daru/dataframe.rb', line 1104

def delete_row index
  idx = named_index_for index

  raise IndexError, "Index #{index} does not exist." unless @index.include? idx
  @index = Daru::Index.new(@index.to_a - [idx])
  each_vector do |vector|
    vector.delete_at idx
  end

  set_size
end

#delete_vector(vector) ⇒ Object

Delete a vector

Raises:

  • (IndexError)

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# File 'lib/daru/dataframe.rb', line 1087

def delete_vector vector
  raise IndexError, "Vector #{vector} does not exist." unless @vectors.include?(vector)

  @data.delete_at @vectors[vector]
  @vectors = Daru::Index.new @vectors.to_a - [vector]

  self
end

#delete_vectors(*vectors) ⇒ Object

Deletes a list of vectors


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# File 'lib/daru/dataframe.rb', line 1097

def delete_vectors *vectors
  Array(vectors).each { |vec| delete_vector vec }

  self
end

#dup(vectors_to_dup = nil) ⇒ Object

Duplicate the DataFrame entirely.

Arguments

  • vectors_to_dup - An Array specifying the names of Vectors to

be duplicated. Will duplicate the entire DataFrame if not specified.


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# File 'lib/daru/dataframe.rb', line 590

def dup vectors_to_dup=nil
  vectors_to_dup = @vectors.to_a unless vectors_to_dup

  src = vectors_to_dup.map { |vec| @data[@vectors.pos(vec)].dup }
  new_order = Daru::Index.new(vectors_to_dup)

  Daru::DataFrame.new src, order: new_order, index: @index.dup, name: @name, clone: true
end

#dup_only_valid(vecs = nil) ⇒ Object

Creates a new duplicate dataframe containing only rows without a single missing value.


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# File 'lib/daru/dataframe.rb', line 631

def dup_only_valid vecs=nil
  rows_with_nil = @data.map { |vec| vec.indexes(*Daru::MISSING_VALUES) }
                       .inject(&:concat)
                       .uniq

  row_indexes = @index.to_a
  (vecs.nil? ? self : dup(vecs)).row[*(row_indexes - rows_with_nil)]
end

#each(axis = :vector, &block) ⇒ Object

Iterate over each row or vector of the DataFrame. Specify axis by passing :vector or :row as the argument. Default to :vector.

Description

`#each` works exactly like Array#each. The default mode for `each` is to iterate over the columns of the DataFrame. To iterate over rows you must pass the axis, i.e `:row` as an argument.

Arguments

  • axis - The axis to iterate over. Can be :vector (or :column)

or :row. Default to :vector.


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# File 'lib/daru/dataframe.rb', line 846

def each axis=:vector, &block
  dispatch_to_axis axis, :each, &block
end

#each_index(&block) ⇒ Object

Iterate over each index of the DataFrame.


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# File 'lib/daru/dataframe.rb', line 780

def each_index &block
  return to_enum(:each_index) unless block_given?

  @index.each(&block)

  self
end

#each_rowObject

Iterate over each row


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# File 'lib/daru/dataframe.rb', line 813

def each_row
  return to_enum(:each_row) unless block_given?

  @index.size.times do |pos|
    yield row_at(pos)
  end

  self
end

#each_row_with_indexObject


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# File 'lib/daru/dataframe.rb', line 823

def each_row_with_index
  return to_enum(:each_row_with_index) unless block_given?

  @index.each do |index|
    yield access_row(index), index
  end

  self
end

#each_vector(&block) ⇒ Object Also known as: each_column

Iterate over each vector


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# File 'lib/daru/dataframe.rb', line 789

def each_vector(&block)
  return to_enum(:each_vector) unless block_given?

  @data.each(&block)

  self
end

#each_vector_with_indexObject Also known as: each_column_with_index

Iterate over each vector alongwith the name of the vector


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# File 'lib/daru/dataframe.rb', line 800

def each_vector_with_index
  return to_enum(:each_vector_with_index) unless block_given?

  @vectors.each do |vector|
    yield @data[@vectors[vector]], vector
  end

  self
end

#filter(axis = :vector, &block) ⇒ Object

Retain vectors or rows if the block returns a truthy value.

Description

For filtering out certain rows/vectors based on their values, use the #filter method. By default it iterates over vectors and keeps those vectors for which the block returns true. It accepts an optional axis argument which lets you specify whether you want to iterate over vectors or rows.

Arguments

  • axis - The axis to map over. Can be :vector (or :column) or :row.

Default to :vector.

Usage

# Filter vectors

df.filter do |vector|
  vector.type == :numeric and vector.median < 50
end

# Filter rows

df.filter(:row) do |row|
  row[:a] + row[:d] < 100
end

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# File 'lib/daru/dataframe.rb', line 954

def filter axis=:vector, &block
  dispatch_to_axis_pl axis, :filter, &block
end

#filter_rowsObject

Iterates over each row and retains it in a new DataFrame if the block returns true for that row.


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# File 'lib/daru/dataframe.rb', line 1149

def filter_rows
  return to_enum(:filter_rows) unless block_given?

  keep_rows = @index.map { |index| yield access_row(index) }

  where keep_rows
end

#filter_vector(vec, &block) ⇒ Object

creates a new vector with the data of a given field which the block returns true


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# File 'lib/daru/dataframe.rb', line 1143

def filter_vector vec, &block
  Daru::Vector.new(each_row.select(&block).map { |row| row[vec] })
end

#filter_vectors(&block) ⇒ Object

Iterates over each vector and retains it in a new DataFrame if the block returns true for that vector.


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# File 'lib/daru/dataframe.rb', line 1159

def filter_vectors &block
  return to_enum(:filter_vectors) unless block_given?

  dup.tap { |df| df.keep_vector_if(&block) }
end

#get_sub_dataframe(keys, by_position: true) ⇒ Daru::Dataframe

Extract a dataframe given row indexes or positions

Parameters:

  • keys (Array)

    can be positions (if by_position is true) or indexes (if by_position if false)

Returns:

  • (Daru::Dataframe)

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# File 'lib/daru/dataframe.rb', line 573

def get_sub_dataframe(keys, by_position: true)
  return Daru::DataFrame.new({}) if keys == []

  keys = @index.pos(*keys) unless by_position

  sub_df = row_at(*keys)
  sub_df = sub_df.to_df.transpose if sub_df.is_a?(Daru::Vector)

  sub_df
end

#get_vector_anyways(v) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1520

def get_vector_anyways(v)
  @vectors.include?(v) ? self[v].to_a : [nil] * size
end

#group_by(*vectors) ⇒ Object

Group elements by vector to perform operations on them. Returns a Daru::Core::GroupBy object.See the Daru::Core::GroupBy docs for a detailed list of possible operations.

Arguments

  • vectors - An Array contatining names of vectors to group by.

Usage

df = Daru::DataFrame.new({
  a: %w{foo bar foo bar   foo bar foo foo},
  b: %w{one one two three two two one three},
  c:   [1  ,2  ,3  ,1    ,3  ,6  ,3  ,8],
  d:   [11 ,22 ,33 ,44   ,55 ,66 ,77 ,88]
})
df.group_by([:a,:b,:c]).groups
#=> {["bar", "one", 2]=>[1],
# ["bar", "three", 1]=>[3],
# ["bar", "two", 6]=>[5],
# ["foo", "one", 1]=>[0],
# ["foo", "one", 3]=>[6],
# ["foo", "three", 8]=>[7],
# ["foo", "two", 3]=>[2, 4]}

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# File 'lib/daru/dataframe.rb', line 1496

def group_by *vectors
  vectors.flatten!
  missing = vectors - @vectors.to_a
  unless missing.empty?
    raise(ArgumentError, "Vector(s) missing: #{missing.join(', ')}")
  end

  vectors = [@vectors.first] if vectors.empty?

  Daru::Core::GroupBy.new(self, vectors)
end

#group_by_and_aggregate(*group_by_keys, **aggregation_map) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2470

def group_by_and_aggregate(*group_by_keys, **aggregation_map)
  group_by(*group_by_keys).aggregate(aggregation_map)
end

#has_missing_data?Boolean Also known as: flawed?

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1261

def has_missing_data?
  @data.any? { |vec| vec.include_values?(*Daru::MISSING_VALUES) }
end

#has_vector?(vector) ⇒ Boolean

Check if a vector is present

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1336

def has_vector? vector
  @vectors.include? vector
end

#head(quantity = 10) ⇒ Object Also known as: first

The first ten elements of the DataFrame

Parameters:

  • quantity (Fixnum) (defaults to: 10)

    (10) The number of elements to display from the top.


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# File 'lib/daru/dataframe.rb', line 1384

def head quantity=10
  row.at 0..(quantity-1)
end

#include_values?(*values) ⇒ true, false

Check if any of given values occur in the data frame

Examples:

df = Daru::DataFrame.new({
  a: [1,    2,          3,   nil,        Float::NAN, nil, 1,   7],
  b: [:a,  :b,          nil, Float::NAN, nil,        3,   5,   8],
  c: ['a',  Float::NAN, 3,   4,          3,          5,   nil, 7]
}, index: 11..18)
df.include_values? nil
# => true

Parameters:

  • values (Array)

    to check for

Returns:

  • (true, false)

    true if any of the given values occur in the dataframe, false otherwise


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# File 'lib/daru/dataframe.rb', line 1280

def include_values?(*values)
  @data.any? { |vec| vec.include_values?(*values) }
end

#insert_vector(n, name, source) ⇒ Object

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 550

def insert_vector n, name, source
  raise ArgumentError unless source.is_a? Array
  vector = Daru::Vector.new(source, index: @index, name: @name)
  @data << vector
  @vectors = @vectors.add name
  ordr = @vectors.dup.to_a
  elmnt = ordr.pop
  ordr = ordr.insert n, elmnt
  self.order=ordr
end

#inspect(spacing = Daru.spacing, threshold = Daru.max_rows) ⇒ Object

Pretty print in a nice table format for the command line (irb/pry/iruby)


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# File 'lib/daru/dataframe.rb', line 2264

def inspect spacing=Daru.spacing, threshold=Daru.max_rows
  name_part = @name ? ": #{@name} " : ''
  spacing = [headers.to_a.map(&:length).max, spacing].max

  "#<#{self.class}#{name_part}(#{nrows}x#{ncols})>#{$INPUT_RECORD_SEPARATOR}" +
    Formatters::Table.format(
      each_row.lazy,
      row_headers: row_headers,
      headers: headers,
      threshold: threshold,
      spacing: spacing
    )
end

#interact_code(vector_names, full) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2327

def interact_code vector_names, full
  dfs = vector_names.zip(full).map do |vec_name, f|
    self[vec_name].contrast_code(full: f).each.to_a
  end

  all_vectors = recursive_product(dfs)
  Daru::DataFrame.new all_vectors,
    order: all_vectors.map(&:name)
end

#join(other_df, opts = {}) ⇒ Daru::DataFrame

Join 2 DataFrames with SQL style joins. Currently supports inner, left outer, right outer and full outer joins.

Examples:

Inner Join

left = Daru::DataFrame.new({
  :id   => [1,2,3,4],
  :name => ['Pirate', 'Monkey', 'Ninja', 'Spaghetti']
})
right = Daru::DataFrame.new({
  :id => [1,2,3,4],
  :name => ['Rutabaga', 'Pirate', 'Darth Vader', 'Ninja']
})
left.join(right, how: :inner, on: [:name])
#=>
##<Daru::DataFrame:82416700 @name = 74c0811b-76c6-4c42-ac93-e6458e82afb0 @size = 2>
#                 id_1       name       id_2
#         0          1     Pirate          2
#         1          3      Ninja          4

Parameters:

  • other_df (Daru::DataFrame)

    Another DataFrame on which the join is to be performed.

  • opts (Hash) (defaults to: {})

    Options Hash

  • :how (Hash)

    a customizable set of options

  • :on (Hash)

    a customizable set of options

  • :indicator (Hash)

    a customizable set of options

Returns:


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# File 'lib/daru/dataframe.rb', line 1977

def join(other_df,opts={})
  Daru::Core::Merge.join(self, other_df, opts)
end

#keep_row_ifObject


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# File 'lib/daru/dataframe.rb', line 1130

def keep_row_if
  @index
    .reject { |idx| yield access_row(idx) }
    .each { |idx| delete_row idx }
end

#keep_vector_ifObject


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# File 'lib/daru/dataframe.rb', line 1136

def keep_vector_if
  @vectors.each do |vector|
    delete_vector(vector) unless yield(@data[@vectors[vector]], vector)
  end
end

#map(axis = :vector, &block) ⇒ Object

Map over each vector or row of the data frame according to the argument specified. Will return an Array of the resulting elements. To map over each row/vector and get a DataFrame, see #recode.

Description

The #map iterator works like Array#map. The value returned by each run of the block is added to an Array and the Array is returned. This method also accepts an axis argument, like #each. The default is :vector.

Arguments

  • axis - The axis to map over. Can be :vector (or :column) or :row.

Default to :vector.


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# File 'lib/daru/dataframe.rb', line 885

def map axis=:vector, &block
  dispatch_to_axis_pl axis, :map, &block
end

#map!(axis = :vector, &block) ⇒ Object

Destructive map. Modifies the DataFrame. Each run of the block must return a Daru::Vector. You can specify the axis to map over as the argument. Default to :vector.

Arguments

  • axis - The axis to map over. Can be :vector (or :column) or :row.

Default to :vector.


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# File 'lib/daru/dataframe.rb', line 897

def map! axis=:vector, &block
  if %i[vector column].include?(axis)
    map_vectors!(&block)
  elsif axis == :row
    map_rows!(&block)
  end
end

#map_rows(&block) ⇒ Object

Map each row


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# File 'lib/daru/dataframe.rb', line 1004

def map_rows &block
  return to_enum(:map_rows) unless block_given?

  each_row.map(&block)
end

#map_rows!Object


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# File 'lib/daru/dataframe.rb', line 1016

def map_rows!
  return to_enum(:map_rows!) unless block_given?

  index.dup.each do |i|
    row[i] = should_be_vector!(yield(row[i]))
  end

  self
end

#map_rows_with_index(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1010

def map_rows_with_index &block
  return to_enum(:map_rows_with_index) unless block_given?

  each_row_with_index.map(&block)
end

#map_vectors(&block) ⇒ Object

Map each vector and return an Array.


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# File 'lib/daru/dataframe.rb', line 979

def map_vectors &block
  return to_enum(:map_vectors) unless block_given?

  @data.map(&block)
end

#map_vectors!Object

Destructive form of #map_vectors


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# File 'lib/daru/dataframe.rb', line 986

def map_vectors!
  return to_enum(:map_vectors!) unless block_given?

  vectors.dup.each do |n|
    self[n] = should_be_vector!(yield(self[n]))
  end

  self
end

#map_vectors_with_index(&block) ⇒ Object

Map vectors alongwith the index.


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# File 'lib/daru/dataframe.rb', line 997

def map_vectors_with_index &block
  return to_enum(:map_vectors_with_index) unless block_given?

  each_vector_with_index.map(&block)
end

#merge(other_df) ⇒ Daru::DataFrame

Merge vectors from two DataFrames. In case of name collision, the vectors names are changed to x_1, x_2 .…

Returns:


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# File 'lib/daru/dataframe.rb', line 1932

def merge other_df # rubocop:disable Metrics/AbcSize
  unless nrows == other_df.nrows
    raise ArgumentError,
      "Number of rows must be equal in this: #{nrows} and other: #{other_df.nrows}"
  end

  new_fields = (@vectors.to_a + other_df.vectors.to_a)
  new_fields = ArrayHelper.recode_repeated(new_fields)
  DataFrame.new({}, order: new_fields).tap do |df_new|
    (0...nrows).each do |i|
      df_new.add_row row[i].to_a + other_df.row[i].to_a
    end
    df_new.index = @index if @index == other_df.index
    df_new.update
  end
end

#missing_values_rows(missing_values = [nil]) ⇒ Object Also known as: vector_missing_values

Return a vector with the number of missing values in each row.

Arguments

  • missing_values - An Array of the values that should be

treated as 'missing'. The default missing value is nil.


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# File 'lib/daru/dataframe.rb', line 1250

def missing_values_rows missing_values=[nil]
  number_of_missing = each_row.map do |row|
    row.indexes(*missing_values).size
  end

  Daru::Vector.new number_of_missing, index: @index, name: "#{@name}_missing_rows"
end

#ncolsObject

The number of vectors


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# File 'lib/daru/dataframe.rb', line 1331

def ncols
  @vectors.size
end

#nest(*tree_keys, &_block) ⇒ Object

Return a nested hash using vector names as keys and an array constructed of hashes with other values. If block provided, is used to provide the values, with parameters row of dataset, current last hash on hierarchy and name of the key to include


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# File 'lib/daru/dataframe.rb', line 1288

def nest *tree_keys, &_block
  tree_keys = tree_keys[0] if tree_keys[0].is_a? Array

  each_row.each_with_object({}) do |row, current|
    # Create tree
    *keys, last = tree_keys
    current = keys.inject(current) { |c, f| c[row[f]] ||= {} }
    name = row[last]

    if block_given?
      current[name] = yield(row, current, name)
    else
      current[name] ||= []
      current[name].push(row.to_h.delete_if { |key,_value| tree_keys.include? key })
    end
  end
end

#nrowsObject

The number of rows


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# File 'lib/daru/dataframe.rb', line 1326

def nrows
  @index.size
end

#numeric_vector_namesObject


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# File 'lib/daru/dataframe.rb', line 1739

def numeric_vector_names
  @vectors.select { |v| self[v].numeric? }
end

#numeric_vectorsObject

Return the indexes of all the numeric vectors. Will include vectors with nils alongwith numbers.


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# File 'lib/daru/dataframe.rb', line 1732

def numeric_vectors
  # FIXME: Why _with_index ?..
  each_vector_with_index
    .select { |vec, _i| vec.numeric? }
    .map(&:last)
end

#one_to_many(parent_fields, pattern) ⇒ Object

Creates a new dataset for one to many relations on a dataset, based on pattern of field names.

for example, you have a survey for number of children with this structure:

id, name, child_name_1, child_age_1, child_name_2, child_age_2

with

ds.one_to_many([:id], "child_%v_%n"

the field of first parameters will be copied verbatim to new dataset, and fields which responds to second pattern will be added one case for each different %n.

Examples:

cases=[
  ['1','george','red',10,'blue',20,nil,nil],
  ['2','fred','green',15,'orange',30,'white',20],
  ['3','alfred',nil,nil,nil,nil,nil,nil]
]
ds=Daru::DataFrame.rows(cases, order:
  [:id, :name,
   :car_color1, :car_value1,
   :car_color2, :car_value2,
   :car_color3, :car_value3])
ds.one_to_many([:id],'car_%v%n').to_matrix
#=> Matrix[
#   ["red", "1", 10],
#   ["blue", "1", 20],
#   ["green", "2", 15],
#   ["orange", "2", 30],
#   ["white", "2", 20]
#   ]

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# File 'lib/daru/dataframe.rb', line 2012

def one_to_many(parent_fields, pattern)
  vars, numbers = one_to_many_components(pattern)

  DataFrame.new([], order: [*parent_fields, '_col_id', *vars]).tap do |ds|
    each_row do |row|
      verbatim = parent_fields.map { |f| [f, row[f]] }.to_h
      numbers.each do |n|
        generated = one_to_many_row row, n, vars, pattern
        next if generated.values.all?(&:nil?)

        ds.add_row(verbatim.merge(generated).merge('_col_id' => n))
      end
    end
    ds.update
  end
end

#only_numerics(opts = {}) ⇒ Object

Return a DataFrame of only the numerical Vectors. If clone: false is specified as option, only a view of the Vectors will be returned. Defaults to clone: true.


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# File 'lib/daru/dataframe.rb', line 1746

def only_numerics opts={}
  cln = opts[:clone] == false ? false : true
  arry = numeric_vectors.map { |v| self[v] }

  order = Index.new(numeric_vectors)
  Daru::DataFrame.new(arry, clone: cln, order: order, index: @index)
end

#order=(order_array) ⇒ Object

Reorder the vectors in a dataframe

Examples:

df = Daru::DataFrame({
  a: [1, 2, 3],
  b: [4, 5, 6]
}, order: [:a, :b])
df.order = [:b, :a]
df
# => #<Daru::DataFrame(3x2)>
#       b   a
#   0   4   1
#   1   5   2
#   2   6   3

Parameters:

  • order_array (Array)

    new order of the vectors

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 1221

def order=(order_array)
  raise ArgumentError, 'Invalid order' unless
    order_array.sort == vectors.to_a.sort
  initialize(to_h, order: order_array)
end

#pivot_table(opts = {}) ⇒ Object

Pivots a data frame on specified vectors and applies an aggregate function to quickly generate a summary.

Options

:index - Keys to group by on the pivot table row index. Pass vector names contained in an Array.

:vectors - Keys to group by on the pivot table column index. Pass vector names contained in an Array.

:agg - Function to aggregate the grouped values. Default to :mean. Can use any of the statistics functions applicable on Vectors that can be found in the Daru::Statistics::Vector module.

:values - Columns to aggregate. Will consider all numeric columns not specified in :index or :vectors. Optional.

Usage

df = Daru::DataFrame.new({
  a: ['foo'  ,  'foo',  'foo',  'foo',  'foo',  'bar',  'bar',  'bar',  'bar'],
  b: ['one'  ,  'one',  'one',  'two',  'two',  'one',  'one',  'two',  'two'],
  c: ['small','large','large','small','small','large','small','large','small'],
  d: [1,2,2,3,3,4,5,6,7],
  e: [2,4,4,6,6,8,10,12,14]
})
df.pivot_table(index: [:a], vectors: [:b], agg: :sum, values: :e)

#=>
# #<Daru::DataFrame:88342020 @name = 08cdaf4e-b154-4186-9084-e76dd191b2c9 @size = 2>
#            [:e, :one] [:e, :two]
#     [:bar]         18         26
#     [:foo]         10         12

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 1911

def pivot_table opts={}
  raise ArgumentError, 'Specify grouping index' if Array(opts[:index]).empty?

  index               = opts[:index]
  vectors             = opts[:vectors] || []
  aggregate_function  = opts[:agg] || :mean
  values              = prepare_pivot_values index, vectors, opts
  raise IndexError, 'No numeric vectors to aggregate' if values.empty?

  grouped = group_by(index)
  return grouped.send(aggregate_function) if vectors.empty?

  super_hash = make_pivot_hash grouped, vectors, values, aggregate_function

  pivot_dataframe super_hash
end

#plot(*args, **options, &b) ⇒ Object

this method is overwritten: see Daru::DataFrame#plotting_library=


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# File 'lib/daru/dataframe.rb', line 383

def plot(*args, **options, &b)
  init_plotting_library

  plot(*args, **options, &b)
end

#plotting_library=(lib) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 367

def plotting_library= lib
  case lib
  when :gruff, :nyaplot
    @plotting_library = lib
    if Daru.send("has_#{lib}?".to_sym)
      extend Module.const_get(
        "Daru::Plotting::DataFrame::#{lib.to_s.capitalize}Library"
      )
    end
  else
    raise ArgumentError, "Plotting library #{lib} not supported. "\
      'Supported libraries are :nyaplot and :gruff'
  end
end

#recast(opts = {}) ⇒ Object

Change dtypes of vectors by supplying a hash of :vector_name => :new_dtype

Usage

df = Daru::DataFrame.new({a: [1,2,3], b: [1,2,3], c: [1,2,3]})
df.recast a: :nmatrix, c: :nmatrix

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# File 'lib/daru/dataframe.rb', line 2246

def recast opts={}
  opts.each do |vector_name, dtype|
    self[vector_name].cast(dtype: dtype)
  end
end

#recode(axis = :vector, &block) ⇒ Object

Maps over the DataFrame and returns a DataFrame. Each run of the block must return a Daru::Vector object. You can specify the axis to map over. Default to :vector.

Description

Recode works similarly to #map, but an important difference between the two is that recode returns a modified Daru::DataFrame instead of an Array. For this reason, #recode expects that every run of the block to return a Daru::Vector.

Just like map and each, recode also accepts an optional axis argument.

Arguments

  • axis - The axis to map over. Can be :vector (or :column) or :row.

Default to :vector.


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# File 'lib/daru/dataframe.rb', line 922

def recode axis=:vector, &block
  dispatch_to_axis_pl axis, :recode, &block
end

#recode_rowsObject


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# File 'lib/daru/dataframe.rb', line 968

def recode_rows
  block_given? or return to_enum(:recode_rows)

  dup.tap do |df|
    df.each_row_with_index do |r, i|
      df.row[i] = should_be_vector!(yield(r))
    end
  end
end

#recode_vectorsObject


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# File 'lib/daru/dataframe.rb', line 958

def recode_vectors
  block_given? or return to_enum(:recode_vectors)

  dup.tap do |df|
    df.each_vector_with_index do |v, i|
      df[*i] = should_be_vector!(yield(v))
    end
  end
end

#reindex(new_index) ⇒ Object

Change the index of the DataFrame and preserve the labels of the previous indexing. New index can be Daru::Index or any of its subclasses.

Examples:

Reindexing DataFrame

df = Daru::DataFrame.new({a: [1,2,3,4], b: [11,22,33,44]},
  index: ['a','b','c','d'])
#=>
##<Daru::DataFrame:83278130 @name = b19277b8-c548-41da-ad9a-2ad8c060e273 @size = 4>
#                    a          b
#         a          1         11
#         b          2         22
#         c          3         33
#         d          4         44
df.reindex Daru::Index.new(['b', 0, 'a', 'g'])
#=>
##<Daru::DataFrame:83177070 @name = b19277b8-c548-41da-ad9a-2ad8c060e273 @size = 4>
#                    a          b
#         b          2         22
#         0        nil        nil
#         a          1         11
#         g        nil        nil

Parameters:

  • new_index (Daru::Index)

    The new Index for reindexing the DataFrame.


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# File 'lib/daru/dataframe.rb', line 1620

def reindex new_index
  unless new_index.is_a?(Daru::Index)
    raise ArgumentError, 'Must pass the new index of type Index or its '\
      "subclasses, not #{new_index.class}"
  end

  cl = Daru::DataFrame.new({}, order: @vectors, index: new_index, name: @name)
  new_index.each_with_object(cl) do |idx, memo|
    memo.row[idx] = @index.include?(idx) ? row[idx] : [nil]*ncols
  end
end

#reindex_vectors(new_vectors) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1508

def reindex_vectors new_vectors
  unless new_vectors.is_a?(Daru::Index)
    raise ArgumentError, 'Must pass the new index of type Index or its '\
      "subclasses, not #{new_vectors.class}"
  end

  cl = Daru::DataFrame.new({}, order: new_vectors, index: @index, name: @name)
  new_vectors.each_with_object(cl) do |vec, memo|
    memo[vec] = @vectors.include?(vec) ? self[vec] : [nil]*nrows
  end
end

#reject_values(*values) ⇒ Daru::DataFrame

Returns a dataframe in which rows with any of the mentioned values are ignored.

Examples:

df = Daru::DataFrame.new({
  a: [1,    2,          3,   nil,        Float::NAN, nil, 1,   7],
  b: [:a,  :b,          nil, Float::NAN, nil,        3,   5,   8],
  c: ['a',  Float::NAN, 3,   4,          3,          5,   nil, 7]
}, index: 11..18)
df.reject_values nil, Float::NAN
# => #<Daru::DataFrame(2x3)>
#       a   b   c
#   11   1   a   a
#   18   7   8   7

Parameters:

  • values (Array)

    to reject to form the new dataframe

Returns:

  • (Daru::DataFrame)

    Data Frame with only rows which doesn't contain the mentioned values


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# File 'lib/daru/dataframe.rb', line 657

def reject_values(*values)
  positions =
    size.times.to_a - @data.flat_map { |vec| vec.positions(*values) }
  # Handle the case when positions size is 1 and #row_at wouldn't return a df
  if positions.size == 1
    pos = positions.first
    row_at(pos..pos)
  else
    row_at(*positions)
  end
end

#rename(new_name) ⇒ Object Also known as: name=

Rename the DataFrame.


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# File 'lib/daru/dataframe.rb', line 2171

def rename new_name
  @name = new_name
  self
end

#rename_vectors(name_map) ⇒ Object

Renames the vectors

Arguments

  • name_map - A hash where the keys are the exising vector names and

    the values are the new names.  If a vector is renamed
    to a vector name that is already in use, the existing
    one is overwritten.
    

Usage

df = Daru::DataFrame.new({ a: [1,2,3,4], b: [:a,:b,:c,:d], c: [11,22,33,44] })
df.rename_vectors :a => :alpha, :c => :gamma
df.vectors.to_a #=> [:alpha, :b, :gamma]

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# File 'lib/daru/dataframe.rb', line 1704

def rename_vectors name_map
  existing_targets = name_map.reject { |k,v| k == v }.values & vectors.to_a
  delete_vectors(*existing_targets)

  new_names = vectors.to_a.map { |v| name_map[v] ? name_map[v] : v }
  self.vectors = Daru::Index.new new_names
end

#rename_vectors!(name_map) ⇒ Object

Renames the vectors and returns itself

Arguments

  • name_map - A hash where the keys are the exising vector names and

    the values are the new names.  If a vector is renamed
    to a vector name that is already in use, the existing
    one is overwritten.
    

Usage

df = Daru::DataFrame.new({ a: [1,2,3,4], b: [:a,:b,:c,:d], c: [11,22,33,44] })
df.rename_vectors! :a => :alpha, :c => :gamma # df

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# File 'lib/daru/dataframe.rb', line 1725

def rename_vectors! name_map
  rename_vectors(name_map)
  self
end

#replace_values(old_values, new_value) ⇒ Daru::DataFrame

Replace specified values with given value

Examples:

df = Daru::DataFrame.new({
  a: [1,    2,          3,   nil,        Float::NAN, nil, 1,   7],
  b: [:a,  :b,          nil, Float::NAN, nil,        3,   5,   8],
  c: ['a',  Float::NAN, 3,   4,          3,          5,   nil, 7]
}, index: 11..18)
df.replace_values nil, Float::NAN
# => #<Daru::DataFrame(8x3)>
#       a   b   c
#   11   1   a   a
#   12   2   b NaN
#   13   3 NaN   3
#   14 NaN NaN   4
#   15 NaN NaN   3
#   16 NaN   3   5
#   17   1   5 NaN
#   18   7   8   7

Parameters:

  • old_values (Array)

    values to replace with new value

  • new_value (object)

    new value to replace with

Returns:

  • (Daru::DataFrame)

    Data Frame itself with old values replace with new value


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# File 'lib/daru/dataframe.rb', line 691

def replace_values old_values, new_value
  @data.each { |vec| vec.replace_values old_values, new_value }
  self
end

#reset_indexObject


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# File 'lib/daru/dataframe.rb', line 1632

def reset_index
  index_df = index.to_df
  names = index.name
  names = [names] unless names.instance_of?(Array)
  new_vectors = names + vectors.to_a
  self.index = index_df.index
  names.each do |name|
    self[name] = index_df[name]
  end
  self.order = new_vectors
  self
end

#respond_to_missing?(name, include_private = false) ⇒ Boolean

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 2323

def respond_to_missing?(name, include_private=false)
  name.to_s.end_with?('=') || has_vector?(name) || super
end

#rolling_fillna(direction = :forward) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 736

def rolling_fillna(direction=:forward)
  dup.rolling_fillna!(direction)
end

#rolling_fillna!(direction = :forward) ⇒ Object

Rolling fillna replace all Float::NAN and NIL values with the preceeding or following value

Examples:

df = Daru::DataFrame.new({
 a: [1,    2,          3,   nil,        Float::NAN, nil, 1,   7],
 b: [:a,  :b,          nil, Float::NAN, nil,        3,   5,   nil],
 c: ['a',  Float::NAN, 3,   4,          3,          5,   nil, 7]
})

=> #<Daru::DataFrame(8x3)>
     a   b   c
 0   1   a   a
 1   2   b NaN
 2   3 nil   3
 3 nil NaN   4
 4 NaN nil   3
 5 nil   3   5
 6   1   5 nil
 7   7 nil   7

2.3.3 :068 > df.rolling_fillna(:forward)
=> #<Daru::DataFrame(8x3)>
     a   b   c
 0   1   a   a
 1   2   b   a
 2   3   b   3
 3   3   b   4
 4   3   b   3
 5   3   3   5
 6   1   5   5
 7   7   5   7

Parameters:

  • direction (Symbol) (defaults to: :forward)

    (:forward, :backward) whether replacement value is preceeding or following


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# File 'lib/daru/dataframe.rb', line 731

def rolling_fillna!(direction=:forward)
  @data.each { |vec| vec.rolling_fillna!(direction) }
  self
end

#rowObject

Access a row or set/create a row. Refer #[] and #[]= docs for details.

Usage

df.row[:a] # access row named ':a'
df.row[:b] = [1,2,3] # set row ':b' to [1,2,3]

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# File 'lib/daru/dataframe.rb', line 566

def row
  Daru::Accessors::DataFrameByRow.new(self)
end

#row_at(*positions) ⇒ Daru::Vector, Daru::DataFrame

Retrive rows by positions

Examples:

df = Daru::DataFrame.new({
  a: [1, 2, 3],
  b: ['a', 'b', 'c']
})
df.row_at 1, 2
# => #<Daru::DataFrame(2x2)>
#       a   b
#   1   2   b
#   2   3   c

Parameters:

  • positions (Array<Integer>)

    of rows to retrive

Returns:


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# File 'lib/daru/dataframe.rb', line 410

def row_at *positions
  original_positions = positions
  positions = coerce_positions(*positions, nrows)
  validate_positions(*positions, nrows)

  if positions.is_a? Integer
    row = get_rows_for([positions])
    Daru::Vector.new row, index: @vectors
  else
    new_rows = get_rows_for(original_positions)
    Daru::DataFrame.new new_rows, index: @index.at(*original_positions), order: @vectors
  end
end

#save(filename) ⇒ Object

Use marshalling to save dataframe to a file.


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# File 'lib/daru/dataframe.rb', line 2220

def save filename
  Daru::IO.save self, filename
end

#set_at(positions, vector) ⇒ Object

Set vectors by positions

Examples:

df = Daru::DataFrame.new({
  a: [1, 2, 3],
  b: ['a', 'b', 'c']
})
df.set_at [0], ['x', 'y', 'z']
df
#=> #<Daru::DataFrame(3x2)>
#       a   b
#   0   x   a
#   1   y   b
#   2   z   c

Parameters:

  • positions (Array<Integer>)

    positions of vectors to set

  • vector (Array, Daru::Vector)

    vector to be assigned

Raises:


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# File 'lib/daru/dataframe.rb', line 507

def set_at positions, vector
  if positions.last == :row
    positions.pop
    return set_row_at(positions, vector)
  end

  validate_positions(*positions, ncols)
  vector =
    if vector.is_a? Daru::Vector
      vector.reindex @index
    else
      Daru::Vector.new vector
    end

  raise SizeError, 'Vector length should match index length' if
    vector.size != @index.size

  positions.each { |pos| @data[pos] = vector }
end

#set_index(new_index_col, opts = {}) ⇒ Object

Set a particular column as the new DF

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 1581

def set_index new_index_col, opts={}
  if new_index_col.respond_to?(:to_a)
    strategy = SetMultiIndexStrategy
    new_index_col = new_index_col.to_a
  else
    strategy = SetSingleIndexStrategy
  end

  uniq_size = strategy.uniq_size(self, new_index_col)
  raise ArgumentError, 'All elements in new index must be unique.' if
    @size != uniq_size

  self.index = strategy.new_index(self, new_index_col)
  strategy.delete_vector(self, new_index_col) unless opts[:keep]
  self
end

#set_row_at(positions, vector) ⇒ Object

Set rows by positions

Examples:

df = Daru::DataFrame.new({
  a: [1, 2, 3],
  b: ['a', 'b', 'c']
})
df.set_row_at [0, 1], ['x', 'x']
df
#=> #<Daru::DataFrame(3x2)>
#       a   b
#   0   x   x
#   1   x   x
#   2   3   c

Parameters:

  • positions (Array<Integer>)

    positions of rows to set

  • vector (Array, Daru::Vector)

    vector to be assigned

Raises:


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# File 'lib/daru/dataframe.rb', line 439

def set_row_at positions, vector
  validate_positions(*positions, nrows)
  vector =
    if vector.is_a? Daru::Vector
      vector.reindex @vectors
    else
      Daru::Vector.new vector
    end

  raise SizeError, 'Vector length should match row length' if
    vector.size != @vectors.size

  @data.each_with_index do |vec, pos|
    vec.set_at(positions, vector.at(pos))
  end
  @index = @data[0].index
  set_size
end

#shapeObject

Return the number of rows and columns of the DataFrame in an Array.


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# File 'lib/daru/dataframe.rb', line 1321

def shape
  [nrows, ncols]
end

#sort(vector_order, opts = {}) ⇒ Object

Non-destructive version of #sort!


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# File 'lib/daru/dataframe.rb', line 1873

def sort vector_order, opts={}
  dup.sort! vector_order, opts
end

#sort!(vector_order, opts = {}) ⇒ Object

Sorts a dataframe (ascending/descending) in the given pripority sequence of vectors, with or without a block.

Examples:

Sort a dataframe with a vector sequence.


df = Daru::DataFrame.new({a: [1,2,1,2,3], b: [5,4,3,2,1]})

df.sort [:a, :b]
# =>
# <Daru::DataFrame:30604000 @name = d6a9294e-2c09-418f-b646-aa9244653444 @size = 5>
#                   a          b
#        2          1          3
#        0          1          5
#        3          2          2
#        1          2          4
#        4          3          1

Sort a dataframe without a block. Here nils will be handled automatically.


df = Daru::DataFrame.new({a: [-3,nil,-1,nil,5], b: [4,3,2,1,4]})

df.sort([:a])
# =>
# <Daru::DataFrame:14810920 @name = c07fb5c7-2201-458d-b679-6a1f7ebfe49f @size = 5>
#                    a          b
#         1        nil          3
#         3        nil          1
#         0         -3          4
#         2         -1          2
#         4          5          4

Sort a dataframe with a block with nils handled automatically.


df = Daru::DataFrame.new({a: [nil,-1,1,nil,-1,1], b: ['aaa','aa',nil,'baaa','x',nil] })

df.sort [:b], by: {b: lambda { |a| a.length } }
# NoMethodError: undefined method `length' for nil:NilClass
# from (pry):8:in `block in __pry__'

df.sort [:b], by: {b: lambda { |a| a.length } }, handle_nils: true

# =>
# <Daru::DataFrame:28469540 @name = 5f986508-556f-468b-be0c-88cc3534445c @size = 6>
#                    a          b
#         2          1        nil
#         5          1        nil
#         4         -1          x
#         1         -1         aa
#         0        nil        aaa
#         3        nil       baaa

Sort a dataframe with a block with nils handled manually.


df = Daru::DataFrame.new({a: [nil,-1,1,nil,-1,1], b: ['aaa','aa',nil,'baaa','x',nil] })

# To print nils at the bottom one can use lambda { |a| (a.nil?)[1]:[0,a.length] }
df.sort [:b], by: {b: lambda { |a| (a.nil?)?[1]:[0,a.length] } }, handle_nils: true

# =>
#<Daru::DataFrame:22214180 @name = cd7703c7-1dca-4560-840b-5ea51a852ef9 @size = 6>
#                 a          b
#      4         -1          x
#      1         -1         aa
#      0        nil        aaa
#      3        nil       baaa
#      2          1        nil
#      5          1        nil

Parameters:

  • vector_order (Array)

    The order of vector names in which the DataFrame should be sorted.

  • opts (Hash) (defaults to: {})

    opts The options to sort with.

Options Hash (opts):

  • :ascending (TrueClass, FalseClass, Array) — default: true

    Sort in ascending or descending order. Specify Array corresponding to order for multiple sort orders.

  • :by (Hash) — default: lambda{|a| a }

    Specify attributes of objects to to be used for sorting, for each vector name in order as a hash of vector name and lambda expressions. In case a lambda for a vector is not specified, the default will be used.

  • :handle_nils (TrueClass, FalseClass, Array) — default: false

    Handle nils automatically or not when a block is provided. If set to True, nils will appear at top after sorting.

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 1849

def sort! vector_order, opts={}
  raise ArgumentError, 'Required atleast one vector name' if vector_order.empty?

  # To enable sorting with categorical data,
  # map categories to integers preserving their order
  old = convert_categorical_vectors vector_order
  block = sort_prepare_block vector_order, opts

  order = @index.size.times.sort(&block)
  new_index = @index.reorder order

  # To reverse map mapping of categorical data to integers
  restore_categorical_vectors old

  @data.each do |vector|
    vector.reorder! order
  end

  self.index = new_index

  self
end

#split_by_category(cat_name) ⇒ Array

Split the dataframe into many dataframes based on category vector

Examples:

df = Daru::DataFrame.new({
  a: [1, 2, 3],
  b: ['a', 'a', 'b']
})
df.to_category :b
df.split_by_category :b
# => [#<Daru::DataFrame: a (2x1)>
#       a
#   0   1
#   1   2,
# #<Daru::DataFrame: b (1x1)>
#       a
#   2   3]

Parameters:

  • cat_name (object)

    name of category vector to split the dataframe

Returns:

  • (Array)

    array of dataframes split by category with category vector used to split not included

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 2355

def split_by_category cat_name
  cat_dv = self[cat_name]
  raise ArgumentError, "#{cat_name} is not a category vector" unless
    cat_dv.category?

  cat_dv.categories.map do |cat|
    where(cat_dv.eq cat)
      .rename(cat)
      .delete_vector cat_name
  end
end

#summaryString

Generate a summary of this DataFrame based on individual vectors in the DataFrame

Returns:

  • (String)

    String containing the summary of the DataFrame


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# File 'lib/daru/dataframe.rb', line 1756

def summary
  summary = "= #{name}"
  summary << "\n  Number of rows: #{nrows}"
  @vectors.each do |v|
    summary << "\n  Element:[#{v}]\n"
    summary << self[v].summary(1)
  end
  summary
end

#tail(quantity = 10) ⇒ Object Also known as: last

The last ten elements of the DataFrame

Parameters:

  • quantity (Fixnum) (defaults to: 10)

    (10) The number of elements to display from the bottom.


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# File 'lib/daru/dataframe.rb', line 1393

def tail quantity=10
  start = [-quantity, -size].max
  row.at start..-1
end

#to_aObject

Converts the DataFrame into an array of hashes where key is vector name and value is the corresponding element. The 0th index of the array contains the array of hashes while the 1th index contains the indexes of each row of the dataframe. Each element in the index array corresponds to its row in the array of hashes, which has the same index.


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# File 'lib/daru/dataframe.rb', line 2102

def to_a
  [each_row.map(&:to_h), @index.to_a]
end

#to_category(*names) ⇒ Daru::DataFrame

Converts the specified non category type vectors to category type vectors

Examples:

df = Daru::DataFrame.new({
  a: [1, 2, 3],
  b: ['a', 'a', 'b']
})
df.to_category :b
df[:b].type
# => :category

Parameters:

  • names (Array)

    of non category type vectors to be converted

Returns:

  • (Daru::DataFrame)

    data frame in which specified vectors have been converted to category type


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# File 'lib/daru/dataframe.rb', line 2303

def to_category *names
  names.each { |n| self[n] = self[n].to_category }
  self
end

#to_dfself

Returns the dataframe. This can be convenient when the user does not know whether the object is a vector or a dataframe.

Returns:

  • (self)

    the dataframe


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# File 'lib/daru/dataframe.rb', line 2067

def to_df
  self
end

#to_gslObject

Convert all numeric vectors to GSL::Matrix


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# File 'lib/daru/dataframe.rb', line 2072

def to_gsl
  numerics_as_arrays = numeric_vectors.map { |n| self[n].to_a }

  GSL::Matrix.alloc(*numerics_as_arrays.transpose)
end

#to_hObject

Converts DataFrame to a hash (explicit) with keys as vector names and values as the corresponding vectors.


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# File 'lib/daru/dataframe.rb', line 2118

def to_h
  @vectors
    .each_with_index
    .map { |vec_name, idx| [vec_name, @data[idx]] }.to_h
end

#to_html(threshold = Daru.max_rows) ⇒ Object

Convert to html for IRuby.


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# File 'lib/daru/dataframe.rb', line 2125

def to_html(threshold=Daru.max_rows)
  table_thead = to_html_thead
  table_tbody = to_html_tbody(threshold)
  path = if index.is_a?(MultiIndex)
           File.expand_path('../iruby/templates/dataframe_mi.html.erb', __FILE__)
         else
           File.expand_path('../iruby/templates/dataframe.html.erb', __FILE__)
         end
  ERB.new(File.read(path).strip).result(binding)
end

#to_html_tbody(threshold = Daru.max_rows) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 2146

def to_html_tbody(threshold=Daru.max_rows)
  threshold ||= @size
  table_tbody_path =
    if index.is_a?(MultiIndex)
      File.expand_path('../iruby/templates/dataframe_mi_tbody.html.erb', __FILE__)
    else
      File.expand_path('../iruby/templates/dataframe_tbody.html.erb', __FILE__)
    end
  ERB.new(File.read(table_tbody_path).strip).result(binding)
end

#to_html_theadObject


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# File 'lib/daru/dataframe.rb', line 2136

def to_html_thead
  table_thead_path =
    if index.is_a?(MultiIndex)
      File.expand_path('../iruby/templates/dataframe_mi_thead.html.erb', __FILE__)
    else
      File.expand_path('../iruby/templates/dataframe_thead.html.erb', __FILE__)
    end
  ERB.new(File.read(table_thead_path).strip).result(binding)
end

#to_json(no_index = true) ⇒ Object

Convert to json. If no_index is false then the index will NOT be included in the JSON thus created.


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# File 'lib/daru/dataframe.rb', line 2108

def to_json no_index=true
  if no_index
    to_a[0].to_json
  else
    to_a.to_json
  end
end

#to_matrixObject

Convert all vectors of type :numeric into a Matrix.


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# File 'lib/daru/dataframe.rb', line 2079

def to_matrix
  Matrix.columns each_vector.select(&:numeric?).map(&:to_a)
end

#to_nmatrixObject

Convert all vectors of type :numeric and not containing nils into an NMatrix.


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# File 'lib/daru/dataframe.rb', line 2091

def to_nmatrix
  each_vector.select do |vector|
    vector.numeric? && !vector.include_values?(*Daru::MISSING_VALUES)
  end.map(&:to_a).transpose.to_nm
end

#to_nyaplotdfObject

Return a Nyaplot::DataFrame from the data of this DataFrame. :nocov:


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# File 'lib/daru/dataframe.rb', line 2085

def to_nyaplotdf
  Nyaplot::DataFrame.new(to_a[0])
end

#to_REXPObject

rubocop:disable Style/MethodName


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# File 'lib/daru/extensions/rserve.rb', line 5

def to_REXP # rubocop:disable Style/MethodName
  names = @vectors.to_a
  data  = names.map do |f|
    Rserve::REXP::Wrapper.wrap(self[f].to_a)
  end
  l = Rserve::Rlist.new(data, names.map(&:to_s))

  Rserve::REXP.create_data_frame(l)
end

#to_sObject


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# File 'lib/daru/dataframe.rb', line 2157

def to_s
  "#<#{self.class}#{': ' + @name.to_s if @name}(#{nrows}x#{ncols})>"
end

#transposeObject

Transpose a DataFrame, tranposing elements and row, column indexing.


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# File 'lib/daru/dataframe.rb', line 2253

def transpose
  Daru::DataFrame.new(
    each_vector.map(&:to_a).transpose,
    index: @vectors,
    order: @index,
    dtype: @dtype,
    name: @name
  )
end

#union(other_df) ⇒ Object

Concatenates another DataFrame as #concat. Additionally it tries to preserve the index. If the indices contain common elements, #union will overwrite the according rows in the first dataframe.


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# File 'lib/daru/dataframe.rb', line 1540

def union other_df
  index = (@index.to_a + other_df.index.to_a).uniq
  df = row[*(@index.to_a - other_df.index.to_a)]

  df = df.concat(other_df)
  df.index = Daru::Index.new(index)
  df
end

#uniq(*vtrs) ⇒ Object

Return unique rows by vector specified or all vectors

Examples:


=> #<Daru::DataFrame(6x2)>
     a   b
 0   1   a
 1   2   b
 2   3   c
 3   4   d
 2   3   c
 3   4   f

2.3.3 :> df.unique
=> #<Daru::DataFrame(5x2)>
     a   b
 0   1   a
 1   2   b
 2   3   c
 3   4   d
 3   4   f

2.3.3 :> df.unique(:a)
=> #<Daru::DataFrame(5x2)>
     a   b
 0   1   a
 1   2   b
 2   3   c
 3   4   d

Parameters:

  • vtrs (String)
    Symbol

    vector names(s) that should be considered


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# File 'lib/daru/dataframe.rb', line 772

def uniq(*vtrs)
  vecs = vtrs.empty? ? vectors.to_a : Array(vtrs)
  grouped = group_by(vecs)
  indexes = grouped.groups.values.map { |v| v[0] }.sort
  row[*indexes]
end

#updateObject

Method for updating the metadata (i.e. missing value positions) of the after assingment/deletion etc. are complete. This is provided so that time is not wasted in creating the metadata for the vector each time assignment/deletion of elements is done. Updating data this way is called lazy loading. To set or unset lazy loading, see the .lazy_update= method.


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# File 'lib/daru/dataframe.rb', line 2166

def update
  @data.each(&:update) if Daru.lazy_update
end

#vector_by_calculation(&block) ⇒ Object

DSL for yielding each row and returning a Daru::Vector based on the value each run of the block returns.

Usage

a1 = Daru::Vector.new([1, 2, 3, 4, 5, 6, 7])
a2 = Daru::Vector.new([10, 20, 30, 40, 50, 60, 70])
a3 = Daru::Vector.new([100, 200, 300, 400, 500, 600, 700])
ds = Daru::DataFrame.new({ :a => a1, :b => a2, :c => a3 })
total = ds.vector_by_calculation { a + b + c }
# <Daru::Vector:82314050 @name = nil @size = 7 >
#   nil
# 0 111
# 1 222
# 2 333
# 3 444
# 4 555
# 5 666
# 6 777

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# File 'lib/daru/dataframe.rb', line 1201

def vector_by_calculation &block
  a = each_row.map { |r| r.instance_eval(&block) }

  Daru::Vector.new a, index: @index
end

#vector_count_characters(vecs = nil) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1306

def vector_count_characters vecs=nil
  vecs ||= @vectors.to_a

  collect_rows do |row|
    vecs.map { |v| row[v].to_s.size }.inject(:+)
  end
end

#vector_mean(max_missing = 0) ⇒ Object

Calculate mean of the rows of the dataframe.

Arguments

  • max_missing - The maximum number of elements in the row that can be

zero for the mean calculation to happen. Default to 0.


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# File 'lib/daru/dataframe.rb', line 1462

def vector_mean max_missing=0
  # FIXME: in vector_sum we preserve created vector dtype, but
  # here we are not. Is this by design or ...? - zverok, 2016-05-18
  mean_vec = Daru::Vector.new [0]*@size, index: @index, name: "mean_#{@name}"

  each_row_with_index.each_with_object(mean_vec) do |(row, i), memo|
    memo[i] = row.indexes(*Daru::MISSING_VALUES).size > max_missing ? nil : row.mean
  end
end

#vector_sum(*args) ⇒ Object

Sum all numeric/specified vectors in the DataFrame.

Returns a new vector that's a containing a sum of all numeric or specified vectors of the DataFrame. By default, if the vector contains a nil, the sum is nil. With :skipnil argument set to true, nil values are assumed to be 0 (zero) and the sum vector is returned.

Examples:

df = Daru::DataFrame.new({
   a: [1, 2, nil],
   b: [2, 1, 3],
   c: [1, 1, 1]
 })
=> #<Daru::DataFrame(3x3)>
       a   b   c
   0   1   2   1
   1   2   1   1
   2 nil   3   1
df.vector_sum [:a, :c]
=> #<Daru::Vector(3)>
   0   2
   1   3
   2 nil
df.vector_sum
=> #<Daru::Vector(3)>
   0   4
   1   4
   2 nil
df.vector_sum skipnil: true
=> #<Daru::Vector(3)>
       c
   0   4
   1   4
   2   4

Parameters:

  • args (Array)

    List of vectors to sum. Default is nil in which case all numeric vectors are summed.

  • opts (Hash)

    a customizable set of options

Returns:

  • Vector with sum of all vectors specified in the argument. If vecs parameter is empty, sum all numeric vector.


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# File 'lib/daru/dataframe.rb', line 1444

def vector_sum(*args)
  defaults = {vecs: nil, skipnil: false}
  options = args.last.is_a?(::Hash) ? args.pop : {}
  options = defaults.merge(options)
  vecs = args[0] || options[:vecs]
  skipnil = args[1] || options[:skipnil]

  vecs ||= numeric_vectors
  sum = Daru::Vector.new [0]*@size, index: @index, name: @name, dtype: @dtype
  vecs.inject(sum) { |memo, n| self[n].add(memo, skipnil: skipnil) }
end

#verify(*tests) ⇒ Object

Test each row with one or more tests. The function returns an array with all errors.

FIXME: description here is too sparse. As far as I can get, it should tell something about that each test is [descr, fields, block], and that first value may be column name to output. - zverok, 2016-05-18

Parameters:

  • tests (Proc)

    Each test is a Proc with the form *Proc.new {|row| row > 0}*


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# File 'lib/daru/dataframe.rb', line 1173

def verify(*tests)
  id = tests.first.is_a?(Symbol) ? tests.shift : @vectors.first

  each_row_with_index.map do |row, i|
    tests.reject { |*_, block| block.call(row) }
         .map { |test| verify_error_message row, test, id, i }
  end.flatten
end

#where(bool_array) ⇒ Object

Query a DataFrame by passing a Daru::Core::Query::BoolArray object.


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# File 'lib/daru/dataframe.rb', line 2279

def where bool_array
  Daru::Core::Query.df_where self, bool_array
end

#which(&block) ⇒ Object

a simple query DSL for accessing where(), inspired by gem “squeel” e.g.: df.which{ `FamilySize` == `FamilySize`.max } equals df.where( df.eq( df.max ) )

e.g.: df.which{ (`NameTitle` == 'Dr') & (`Sex` == 'female') } equals df.where( df.eq('Dr') & df.eq('female') )


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# File 'lib/daru/extensions/which_dsl.rb', line 15

def which(&block)
  WhichQuery.new(self, &block).exec
end

#write_csv(filename, opts = {}) ⇒ Object

Write this DataFrame to a CSV file.

Arguments

  • filename - Path of CSV file where the DataFrame is to be saved.

Options

  • convert_comma - If set to true, will convert any commas in any

of the data to full stops ('.'). All the options accepted by CSV.read() can also be passed into this function.


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# File 'lib/daru/dataframe.rb', line 2190

def write_csv filename, opts={}
  Daru::IO.dataframe_write_csv self, filename, opts
end

#write_excel(filename, opts = {}) ⇒ Object

Write this dataframe to an Excel Spreadsheet

Arguments

  • filename - The path of the file where the DataFrame should be written.


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# File 'lib/daru/dataframe.rb', line 2199

def write_excel filename, opts={}
  Daru::IO.dataframe_write_excel self, filename, opts
end

#write_sql(dbh, table) ⇒ Object

Insert each case of the Dataset on the selected table

Arguments

  • dbh - DBI database connection object.

  • query - Query string.

Usage

ds = Daru::DataFrame.new({:id=>Daru::Vector.new([1,2,3]), :name=>Daru::Vector.new(["a","b","c"])})
dbh = DBI.connect("DBI:Mysql:database:localhost", "user", "password")
ds.write_sql(dbh,"test")

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# File 'lib/daru/dataframe.rb', line 2215

def write_sql dbh, table
  Daru::IO.dataframe_write_sql self, dbh, table
end