Class: Daru::DataFrame

Inherits:
Object
  • Object
show all
Includes:
Maths::Arithmetic::DataFrame, Maths::Statistics::DataFrame, Plotting::DataFrame
Defined in:
lib/daru/dataframe.rb,
lib/daru/extensions/rserve.rb

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Methods included from Plotting::DataFrame

#plot

Methods included from Maths::Statistics::DataFrame

#correlation, #count, #covariance, #describe, #max, #mean, #min, #product, #standardize, #std, #sum

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({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

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# File 'lib/daru/dataframe.rb', line 222

def initialize source, opts={}
  vectors = opts[:order]
  index   = opts[:index]
  clone   = opts[:clone] == false ? false : true
  @data   = []

  temp_name = opts[:name]
  @name   = temp_name.is_a?(Numeric) ? temp_name : (temp_name || SecureRandom.uuid).to_sym

  if source.empty?
    @vectors = create_index vectors
    @index   = create_index index
    create_empty_vectors
  else
    case source
    when Array
      if source.all? { |s| s.is_a?(Array) }
        raise ArgumentError, "Number of vectors (#{vectors.size}) should \
          equal order size (#{source.size})" if source.size != vectors.size

        @index   = create_index(index || source[0].size)
        @vectors = create_index(vectors)

        @vectors.each_with_index do |vec,idx|
          @data << Daru::Vector.new(source[idx], index: @index)
        end
      elsif source.all? { |s| s.is_a?(Daru::Vector) }
        hsh = {}
        vectors.each_with_index do |name, idx|
          hsh[name] = source[idx]
        end
        initialize(hsh, index: index, order: vectors, name: @name, clone: clone)
      else # array of hashes
        if vectors.nil?
          @vectors = Daru::Index.new source[0].keys.map(&:to_sym)
        else
          @vectors = Daru::Index.new (vectors + (source[0].keys - vectors)).uniq.map(&:to_sym)
        end
        @index = Daru::Index.new(index || source.size)

        @vectors.each do |name|
          v = []
          source.each do |hsh|
            v << (hsh[name] || hsh[name.to_s])
          end

          @data << Daru::Vector.new(v, name: set_name(name), index: @index)
        end
      end
    when Hash
      create_vectors_index_with vectors, source
      if all_daru_vectors_in_source? source
        if !index.nil?
          @index = create_index index
        elsif all_vectors_have_equal_indexes?(source)
          @index = source.values[0].index.dup
        else
          all_indexes = []
          source.each_value do |vector|
            all_indexes << vector.index.to_a
          end
          # sort only if missing indexes detected
          all_indexes.flatten!.uniq!.sort!

          @index = Daru::Index.new all_indexes
          clone = true
        end

        if clone
          @vectors.each do |vector|
            @data << Daru::Vector.new([], name: vector, index: @index)

            @index.each do |idx|
              @data[@vectors[vector]][idx] = source[vector][idx]
            end
          end
        else
          @data.concat source.values
        end
      else
        @index = create_index(index || source.values[0].size)

        @vectors.each do |name|
          @data << Daru::Vector.new(source[name].dup, name: set_name(name), index: @index)
        end
      end
    end
  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 1848

def method_missing(name, *args, &block)
  if md = name.match(/(.+)\=/)
    insert_or_modify_vector name[/(.+)\=/].delete("=").to_sym, args[0]
  elsif self.has_vector? name
    self[name, :vector]
  else
    super(name, *args, &block)
  end
end

Instance Attribute Details

#indexObject (readonly)

The index of the rows of the DataFrame


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# File 'lib/daru/dataframe.rb', line 178

def index
  @index
end

#nameObject (readonly)

The name of the DataFrame


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# File 'lib/daru/dataframe.rb', line 181

def name
  @name
end

#sizeObject (readonly)

The number of rows present in the DataFrame


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# File 'lib/daru/dataframe.rb', line 184

def size
  @size
end

#vectorsObject (readonly)

The vectors (columns) index of the DataFrame


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# File 'lib/daru/dataframe.rb', line 175

def vectors
  @vectors
end

Class Method Details

._load(data) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1780

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 140

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

  cols_values = columns.factors
  cols_n      = cols_values.size

  h_rows = rows.factors.inject({}) do |a,v| 
    a[v] = cols_values.inject({}) do |a1,v1| 
      a1[v1]=nil 
      a1
    end
    a
  end

  values.each_index do |i|
    h_rows[rows[i]][columns[i]] = values[i]
  end
  df = Daru::DataFrame.new({}, order: [:_id] + cols_values.to_a)

  rows.factors.each do |row|
    n_row = Array.new(cols_n+1)
    n_row[0] = row
    cols_values.each_index do |i|
      n_row[i+1] = h_rows[row][cols_values[i]]
    end

    df.add_row(n_row)
  end
  df.update
  df
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 - Path 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 48

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 61

def from_excel path, opts={}, &block      
  Daru::IO.from_excel path, opts, &block
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 87

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")

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# File 'lib/daru/dataframe.rb', line 71

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.


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# File 'lib/daru/dataframe.rb', line 93

def rows source, opts={}
  df = nil
  if source.all? { |v| v.size == source[0].size }
    first = source[0]
    index = []
    opts[:order] ||=
    if first.is_a?(Daru::Vector) # assume that all are Vectors
      source.each { |vec| index << vec.name }
      first.index.to_a
    elsif first.is_a?(Array)
      Array.new(first.size) { |i| i.to_s }
    end

    if source.all? { |s| s.is_a?(Array) }
      df = Daru::DataFrame.new(source.transpose, opts)
    else # array of Daru::Vectors
      df = Daru::DataFrame.new({}, opts)
      source.each_with_index do |row, idx|
        df[(index[idx] || idx), :row] = row
      end
    end
  else
    raise SizeError, "All vectors must have same length"
  end

  df
end

Instance Method Details

#==(other) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1843

def == other
  @index == other.index and @size == other.size and @vectors == other.vectors and 
  @vectors.all? { |vector| self[vector, :vector] == other[vector, :vector] }
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 or vectors. Use df.row for accessing row with index ':a' or df.vector for accessing vector with index :vec.


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# File 'lib/daru/dataframe.rb', line 320

def [](*names)
  if names[-1] == :vector or names[-1] == :row
    axis = names[-1]
    names = names[0..-2]
  else
    axis = :vector
  end
  names.map! { |e| e.respond_to?(:to_sym) ? e.to_sym : e }

  if axis == :vector
    access_vector *names
  elsif axis == :row
    access_row *names
  else
    raise IndexError, "Expected axis to be row or vector not #{axis}"
  end
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 345

def []=(*args)
  axis = args.include?(:row) ? :row : :vector
  args.delete :vector
  args.delete :row

  name = args[0..-2]
  vector = args[-1]
  name.map! { |e| e.respond_to?(:to_sym) ? e.to_sym : e }

  if axis == :vector
    insert_or_modify_vector name, vector
  elsif axis == :row        
    insert_or_modify_row name, vector
  else
    raise IndexError, "Expected axis to be row or vector, not #{axis}."
  end
end

#_dump(depth) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1771

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

#add_row(row, index = nil) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 377

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 381

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 1068

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

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


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# File 'lib/daru/dataframe.rb', line 1554

def add_vectors_by_split_recode(name_, join='-', sep=Daru::SPLIT_TOKEN)
  split = self[name_].split_by_separator(sep)
  i = 1
  split.each { |k,v|
    new_field = name_.to_s + join + i.to_s
    v.rename name_.to_s + ":" + k.to_s
    self[new_field.to_sym] = v
    i += 1
  }
end

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

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1106

def all? axis=:vector, &block
  if axis == :vector or axis == :column
    @data.all?(&block)
  elsif axis == :row
    each_row do |row|
      return false unless yield(row)
    end
    return true
  else
    raise ArgumentError, "Unidentified axis #{axis}"
  end
end

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

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1093

def any? axis=:vector, &block
  if axis == :vector or axis == :column
    @data.any?(&block)
  elsif axis == :row
    each_row do |row|
      return true if yield(row)
    end
    return false
  else
    raise ArgumentError, "Unidentified axis #{axis}"
  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 859

def bootstrap(n=nil)
  n ||= nrows
  ds_boot = Daru::DataFrame.new({}, order: @vectors)
  n.times do
    ds_boot.add_row(row[rand(n)])
  end
  ds_boot.update
  ds_boot
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 435

def clone *vectors_to_clone
  vectors_to_clone.flatten! unless vectors_to_clone.all? { |a| !a.is_a?(Array) }
  return super if vectors_to_clone.empty?

  h = vectors_to_clone.inject({}) do |hsh, vec|
    hsh[vec] = self[vec]
    hsh
  end
  Daru::DataFrame.new(h, clone: false)
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 448

def clone_only_valid
  if has_missing_data?
    dup_only_valid
  else
    clone
  end
end

#clone_structureObject

Only clone the structure of the DataFrame.


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# File 'lib/daru/dataframe.rb', line 424

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 551

def collect axis=:vector, &block
  if axis == :vector or axis == :column
    collect_vectors(&block)
  elsif axis == :row
    collect_rows(&block)
  else
    raise ArgumentError, "Unknown axis #{axis}"
  end
end

#collect_matrix::Matrix

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

Returns:


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

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 775

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

  data = []
  each_row_with_index do |row, i|
    data.push yield(row, i)
  end

  Daru::Vector.new(data, 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 764

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

  data = []
  each_row do |row|
    data.push yield(row)
  end

  Daru::Vector.new(data, index: @index)
end

#collect_vector_with_index(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 799

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

  data = []
  each_vector_with_index do |vec, i|
    data.push yield(vec, i)
  end

  Daru::Vector.new(data, 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 788

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

  data = []
  each_vector do |vec|
    data.push yield(vec)
  end

  Daru::Vector.new(data, index: @vectors)
end

#column(name) ⇒ Object

Access a vector by name.


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# File 'lib/daru/dataframe.rb', line 373

def column name
  vector[name]
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 1000

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

Usage

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 1581

def create_sql(table,charset="UTF8")
  sql    = "CREATE TABLE #{table} ("
  fields = self.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


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# File 'lib/daru/dataframe.rb', line 840

def delete_row index
  idx = named_index_for index

  if @index.include? idx
    @index = reassign_index_as(@index.to_a - [idx])
    self.each_vector do |vector|
      vector.delete_at idx
    end
  else
    raise IndexError, "Index #{index} does not exist."
  end

  set_size
end

#delete_vector(vector) ⇒ Object

Delete a vector


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# File 'lib/daru/dataframe.rb', line 828

def delete_vector vector
  if @vectors.include? vector
    @data.delete_at @vectors[vector]
    @vectors = Daru::Index.new @vectors.to_a - [vector]
  else
    raise IndexError, "Vector #{vector} does not exist."
  end

  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 400

def dup vectors_to_dup=nil
  vectors_to_dup = @vectors unless vectors_to_dup

  new_order =
  if vectors.is_a?(MultiIndex)
    src = []
    vectors_to_dup.each do |vec|
      src << @data[@vectors[vec]].dup
    end

    Daru::MultiIndex.new(vectors_to_dup)
  else
    src = {}
    vectors_to_dup.each do |vector|
      src[vector] = @data[@vectors[vector]].dup
    end

    Daru::Index.new(vectors_to_dup)
  end

  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 458

def dup_only_valid vecs=nil
  rows_with_nil = @data.inject([]) do |memo, vector|
    memo.concat vector.missing_positions
    memo
  end.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 526

def each axis=:vector, &block
  if axis == :vector or axis == :column
    each_vector(&block)
  elsif axis == :row
    each_row(&block)
  else
    raise ArgumentError, "Unknown axis #{axis}"
  end
end

#each_row(&block) ⇒ Object

Iterate over each row


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# File 'lib/daru/dataframe.rb', line 493

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

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

  self
end

#each_row_with_index(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 503

def each_row_with_index(&block)
  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 469

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

  @data.each(&block)

  self
end

#each_vector_with_index(&block) ⇒ Object 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 480

def each_vector_with_index(&block)
  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 656

def filter axis=:vector, &block
  if axis == :vector or axis == :column
    filter_vectors(&block)
  elsif axis == :row
    filter_rows(&block)
  end
end

#filter_rows(&block) ⇒ Object

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 902

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

  df = Daru::DataFrame.new({}, order: @vectors.to_a)
  marked = []

  @index.each do |index|
    keep_row = yield access_row(index)
    marked << index if keep_row
  end

  marked.each do |idx|
    df.row[idx] = self[idx, :row]
  end

  df
end

#filter_vector(vec) ⇒ 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 891

def filter_vector vec
  d = []
  each_row do |row|
    d.push(row[vec]) if yield row
  end

  Daru::Vector.new(d)
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 922

def filter_vectors &block
  return to_enum(:filter_vectors) unless block_given?
  
  df = self.dup
  df.keep_vector_if &block

  df
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 1186

def group_by vectors
  vectors = [vectors] if vectors.is_a?(Symbol)
  vectors.each { |v| raise(ArgumentError, "Vector #{v} does not exist") unless
    has_vector?(v) }
    
  Daru::Core::GroupBy.new(self, vectors)
end

#has_missing_data?Boolean Also known as: flawed?

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1024

def has_missing_data?
  !!@data.any? { |v| v.has_missing_data? }
end

#has_vector?(vector) ⇒ Boolean

Check if a vector is present

Returns:

  • (Boolean)

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# File 'lib/daru/dataframe.rb', line 1089

def has_vector? vector
  !!@vectors[*vector]
end

#head(quantity = 10) ⇒ Object

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 1122

def head quantity=10
  self[0..(quantity-1), :row]
end

#inspect(spacing = 10, threshold = 15) ⇒ 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 1810

def inspect spacing=10, threshold=15
  longest = [@name.to_s.size,
             (@vectors.map(&:to_s).map(&:size).max || 0), 
             (@index  .map(&:to_s).map(&:size).max || 0),
             (@data   .map{ |v| v.map(&:to_s).map(&:size).max}.max || 0)].max

  name      = @name || 'nil'
  content   = ""
  longest   = spacing if longest > spacing
  formatter = "\n"

  (@vectors.size + 1).times { formatter += "%#{longest}.#{longest}s " }
  content += "\n#<" + self.class.to_s + ":" + self.object_id.to_s + " @name = " + 
                name.to_s + " @size = " + @size.to_s + ">"
  content += sprintf formatter, "" , *@vectors.map(&:to_s)
  row_num  = 1

  self.each_row_with_index do |row, index|
    content += sprintf formatter, index.to_s, *row.to_hash.values.map { |e| (e || 'nil').to_s }
    row_num += 1
    if row_num > threshold
      dots = []

      (@vectors.size + 1).times { dots << "..." }
      content += sprintf formatter, *dots
      break
    end
  end
  content += "\n"

  content
end

#join(other_ds, fields_1 = [], fields_2 = [], type = :left) ⇒ Daru::DataFrame

Join 2 DataFrames by given fields type is one of :left and :inner, default is :left

Untested! Use at your own risk.

Returns:


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# File 'lib/daru/dataframe.rb', line 1439

def join(other_ds,fields_1=[],fields_2=[],type=:left)
  fields_new = other_ds.vectors.to_a - fields_2
  fields     =     self.vectors.to_a + fields_new

  other_ds_hash = {}
  other_ds.each_row do |row|
    key = row.to_hash.select { |k,v| fields_2.include?(k) }.values
    value = row.to_hash.select { |k,v| fields_new.include?(k) }

    if other_ds_hash[key].nil?
      other_ds_hash[key] = [value]
    else
      other_ds_hash[key] << value
    end
  end

  new_ds = DataFrame.new({}, order: fields)

  self.each_row do |row|
    key = row.to_hash.select{|k,v| fields_1.include?(k)}.values
    new_case = row.to_hash

    if other_ds_hash[key].nil?
      if type == :left
        fields_new.each{|field| new_case[field] = nil}
        new_ds.add_row(Daru::Vector.new(new_case))
      end
    else
      other_ds_hash[key].each do |new_values|
        new_ds.add_row(Daru::Vector.new(new_case.merge(new_values)))
      end
    end
  end

  new_ds
end

#keep_row_if(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 869

def keep_row_if &block
  deletion = []

  @index.each do |index|
    keep_row = yield access_row(index)

    deletion << index unless keep_row
  end
  deletion.each { |idx| 
    delete_row idx 
  }
end

#keep_vector_if(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 882

def keep_vector_if &block
  @vectors.each do |vector|
    keep_vector = yield @data[@vectors[vector]], vector
    
    delete_vector vector unless keep_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 577

def map axis=:vector, &block
  if axis == :vector or axis == :column
    map_vectors(&block)
  elsif axis == :row
    map_rows(&block)
  else
    raise ArgumentError, "Unknown axis #{axis}"
  end
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 595

def map! axis=:vector, &block
  if axis == :vector or axis == :column
    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 728

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

  dt = []
  each_row do |row|
    dt << yield(row)
  end

  dt
end

#map_rows!(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 750

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

  index.dup.each do |i|
    r = yield self.row[i]
    r.is_a?(Daru::Vector) or raise TypeError, "Returned object must be Daru::Vector not #{r.class}"
    self.row[i] = r
  end

  self
end

#map_rows_with_index(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 739

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

  dt = []
  each_row_with_index do |row, index|
    dt << yield(row, index)
  end

  dt
end

#map_vectors(&block) ⇒ Object

Map each vector and return an Array.


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

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

  arry = []
  @data.each do |vec|
    arry << yield(vec)
  end

  arry
end

#map_vectors!(&block) ⇒ Object

Destructive form of #map_vectors


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# File 'lib/daru/dataframe.rb', line 703

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

  vectors.dup.each do |n|
    v = yield self[n]
    v.is_a?(Daru::Vector) or raise TypeError, "Must return a Daru::Vector not #{v.class}"
    self[n] = v
  end

  self
end

#map_vectors_with_index(&block) ⇒ Object

Map vectors alongwith the index.


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# File 'lib/daru/dataframe.rb', line 716

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

  dt = []
  each_vector_with_index do |vector, name|
    dt << yield(vector, name)
  end

  dt
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 1416

def merge other_df
  raise "Number of rows must be equal in this: #{nrows} and other: #{other_df.nrows}" unless nrows == other_df.nrows

  new_fields = (@vectors.to_a + other_df.vectors.to_a)
                    .recode_repeated
                    .map(&:to_sym)
  df_new     = DataFrame.new({}, order: new_fields)

  (0...nrows).to_a.each do |i|
    row = self.row[i].to_a + other_df.row[i].to_a
    df_new.add_row(row)
  end

  df_new.update
  df_new
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 1011

def missing_values_rows missing_values=[nil]
  number_of_missing = []
  each_row do |row|
    row.missing_values = missing_values
    number_of_missing << row.missing_positions.size
  end

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

#ncolsObject

The number of vectors


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# File 'lib/daru/dataframe.rb', line 1084

def ncols
  shape[1]
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 1034

def nest *tree_keys, &block
  tree_keys = tree_keys[0] if tree_keys[0].is_a? Array
  out = {}

  each_row do |row|
    current = out
    # Create tree
    tree_keys[0, tree_keys.size-1].each do |f|
      root = row[f]
      current[root] ||= {}
      current = current[root]
    end
    name = row[tree_keys.last]
    if !block
      current[name] ||= []
      current[name].push(row.to_hash.delete_if { |key,value| tree_keys.include? key})
    else
      current[name] = block.call(row, current,name)
    end
  end

  out
end

#nrowsObject

The number of rows


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# File 'lib/daru/dataframe.rb', line 1079

def nrows
  shape[0]
end

#numeric_vector_namesObject


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# File 'lib/daru/dataframe.rb', line 1231

def numeric_vector_names
  numerics = []

  each_vector do |vec, i|
    numerics << vec.name if(vec.type == :numeric)
  end
  numerics
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 1222

def numeric_vectors
  numerics = []

  each_vector_with_index do |vec, i|
    numerics << i if(vec.type == :numeric)
  end
  numerics
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.

Usage

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 1505

def one_to_many(parent_fields, pattern)
  re      = Regexp.new pattern.gsub("%v","(.+?)").gsub("%n","(\\d+?)")
  ds_vars = parent_fields
  vars    = []
  max_n   = 0
  h       = parent_fields.inject({}) { |a,v| 
    a[v] = Daru::Vector.new([])
    a 
  }
  # Adding _row_id
  h[:_col_id] = Daru::Vector.new([])
  ds_vars.push(:_col_id)

  @vectors.each do |f|
    if f =~ re
      if !vars.include? $1
        vars.push($1)
        h[$1] = Daru::Vector.new([])
      end
      max_n = $2.to_i if max_n < $2.to_i
    end
  end
  ds = DataFrame.new(h, order: ds_vars+vars)

  each_row do |row|
    row_out = {}
    parent_fields.each do |f|
      row_out[f]=row[f]
    end

    max_n.times do |n1|
      n  = n1+1
      any_data = false
      vars.each do |v|
        data = row[pattern.gsub("%v",v.to_s).gsub("%n",n.to_s).to_sym]
        row_out[v] = data
        any_data = true if !data.nil?
      end

      if any_data
        row_out[:_col_id] = n
        ds.add_row(row_out)
      end
    end
  end
  ds.update
  ds
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 1243

def only_numerics opts={}
  cln = opts[:clone] == false ? false : true
  nv = numeric_vectors
  arry = nv.inject([]) do |arr, v|
    arr << self[v]
    arr
  end

  order = @vectors.is_a?(MultiIndex) ? MultiIndex.new(nv) : Index.new(nv)
  Daru::DataFrame.new(arry, clone: cln, order: order, index: @index)
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 1349

def pivot_table opts={}
  raise ArgumentError, "Specify grouping index" if !opts[:index] or opts[:index].empty?

  index   = opts[:index]
  vectors = opts[:vectors] || []
  aggregate_function = opts[:agg] || :mean
  values = 
  if opts[:values].is_a?(Symbol)
    [opts[:values]]
  elsif opts[:values].is_a?(Array)
    opts[:values]
  else # nil
    (@vectors.to_a - (index | vectors)) & numeric_vector_names
  end
  
  raise IndexError, "No numeric vectors to aggregate" if values.empty?

  grouped  = group_by(index)

  unless vectors.empty?
    super_hash = {}
    values.each do |value|
      grouped.groups.each do |group_name, row_numbers|
        super_hash[group_name] ||= {}

        row_numbers.each do |num|
          arry = []
          arry << value
          vectors.each { |v| arry << self[v][num] }
          sub_hash = super_hash[group_name]
          sub_hash[arry] ||= []

          sub_hash[arry] << self[value][num]
        end
      end
    end

    super_hash.each_value do |sub_hash|
      sub_hash.each do |group_name, aggregates|
        sub_hash[group_name] = Daru::Vector.new(aggregates).send(aggregate_function)
      end
    end

    df_index = Daru::MultiIndex.new(symbolize(super_hash.keys))

    vector_indexes = []
    super_hash.each_value do |sub_hash|
      vector_indexes.concat sub_hash.keys
    end
    df_vectors = Daru::MultiIndex.new symbolize(vector_indexes.uniq)
    pivoted_dataframe = Daru::DataFrame.new({}, index: df_index, order: df_vectors)

    super_hash.each do |row_index, sub_h|
      sub_h.each do |vector_index, val|
        pivoted_dataframe[symbolize(vector_index)][symbolize(row_index)] = val
      end
    end
    return pivoted_dataframe
  else
    grouped.send(aggregate_function)
  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 1793

def recast opts={}
  opts.each do |vector_name, dtype|
    vector[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, #recodeexpects 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 620

def recode axis=:vector, &block
  if axis == :vector or axis == :column
    recode_vectors(&block)
  elsif axis == :row
    recode_rows(&block)
  end
end

#recode_rows(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 677

def recode_rows &block
  block_given? or return to_enum(:recode_rows)

  df = self.dup
  df.each_row_with_index do |r, i|
    ret = yield r
    ret.is_a?(Daru::Vector) or raise TypeError, "Every iteration must return Daru::Vector not #{ret.class}"
    df.row[i] = ret
  end

  df
end

#recode_vectors(&block) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 664

def recode_vectors &block
  block_given? or return to_enum(:recode_vectors) 

  df = self.dup
  df.each_vector_with_index do |v, i|
    ret = yield v
    ret.is_a?(Daru::Vector) or raise TypeError, "Every iteration must return Daru::Vector not #{ret.class}"
    df[*i] = ret
  end

  df
end

#reindex(new_index) ⇒ Object

Non-destructive version of #reindex!


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# File 'lib/daru/dataframe.rb', line 1216

def reindex new_index
  self.dup.reindex! new_index
end

#reindex!(new_index) ⇒ Object

Change the index of the DataFrame and its underlying vectors. Destructive.

Parameters:

  • new_index (Symbol, Array)

    Specify an Array if

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 1204

def reindex! new_index
  raise ArgumentError, "Index size must equal dataframe size" if new_index.is_a?(Array) and new_index.size != @size

  @index = possibly_multi_index?(new_index == :seq ? @size : new_index)
  @data.map! do |vector|
    vector.reindex possibly_multi_index?(@index.to_a)
  end

  self
end

#reindex_vectors!(new_vectors) ⇒ Object

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 1194

def reindex_vectors! new_vectors
  raise ArgumentError, "Number of vectors passed into function (#{new_vectors.size}) should equal that present in the DataFrame (#{@vectors.size})" if 
    @vectors.size != new_vectors.size

  @vectors = Daru::Index.new new_vectors.map(&:to_sym), new_vectors.map { |e| @vectors[e] }
end

#rename(new_name) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1716

def rename new_name
  if new_name.is_a?(Numeric)
    @name = new_name 
    return
  end
  @name = new_name.to_sym
end

#report_building(b) ⇒ Object

:nodoc: #


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# File 'lib/daru/dataframe.rb', line 1260

def report_building(b) # :nodoc: #
  b.section(:name=>@name) do |g|
    g.text "Number of rows: #{nrows}"
    @vectors.each do |v|
      g.text "Element:[#{v}]"
      g.parse_element(self[v])
    end
  end
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 390

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

#save(filename) ⇒ Object

Use marshalling to save dataframe to a file.


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# File 'lib/daru/dataframe.rb', line 1767

def save filename
  Daru::IO.save self, filename
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 1074

def shape
  [@index.size, @vectors.size]
end

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

Non-destructive version of #sort!


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# File 'lib/daru/dataframe.rb', line 1311

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

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

Sorts a dataframe (ascending/descending)according to the given sequence of vectors, using the attributes provided in the blocks.

Usage

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

#<Daru::DataFrame:140630680 @name = 04e00197-f8d5-4161-bca2-93266bfabc6f @size = 4>
#            a          b 
# 0         -3          4 
# 1          2          3 
# 2         -1          2 
# 3          4          1 
df.sort([:a], by: { a: lambda { |a,b| a.abs <=> b.abs } })

Parameters:

  • order (Array)

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

  • opts (Hash) (defaults to: {})

    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: {|a, b| a <=> b}

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

Raises:

  • (ArgumentError)

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# File 'lib/daru/dataframe.rb', line 1295

def sort! vector_order, opts={}
  raise ArgumentError, "Required atleast one vector name" if vector_order.size < 1
  opts = {
    ascending: true,
    type: :quick_sort,
    by: {}
  }.merge(opts)

  opts[:by]        = create_logic_blocks vector_order, opts[:by]
  opts[:ascending] = sort_order_array vector_order, opts[:ascending]
  index = @index.to_a
  send(opts[:type], vector_order, index, opts[:by], opts[:ascending])
  reindex! index
end

#summary(method = :to_text) ⇒ Object

Generate a summary of this DataFrame with ReportBuilder.


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# File 'lib/daru/dataframe.rb', line 1256

def summary(method = :to_text)
  ReportBuilder.new(no_title: true).add(self).send(method)
end

#tail(quantity = 10) ⇒ Object

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 1129

def tail quantity=10
  self[(@size - quantity)..(@size-1), :row]
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 1632

def to_a
  arry = [[],[]]
  self.each_row do |row|
    arry[0] << row.to_hash
  end
  arry[1] = @index.to_a

  arry
end

#to_gslObject

Convert all numeric vectors to GSL::Matrix


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# File 'lib/daru/dataframe.rb', line 1592

def to_gsl
  numerics_as_arrays = []
  numeric_vectors.each do |n|
    numerics_as_arrays << self[n].to_a
  end

  GSL::Matrix.alloc *numerics_as_arrays.transpose
end

#to_hashObject

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


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# File 'lib/daru/dataframe.rb', line 1652

def to_hash
  hsh = {}
  @vectors.each_with_index do |vec_name, idx|
    hsh[vec_name] = @data[idx]
  end

  hsh
end

#to_html(threshold = 30) ⇒ Object

Convert to html for IRuby.


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# File 'lib/daru/dataframe.rb', line 1662

def to_html threshold=30
  html = "<table>" + 
    "<tr>" +
      "<th colspan=\"#{@vectors.size+1}\">" + 
        "Daru::DataFrame:#{self.object_id} " + " rows: #{nrows} " + " cols: #{ncols}" 
      "</th>" +
    "</tr>"
  html +='<tr><th></th>'
  @vectors.each { |vector| html += '<th>' + vector.to_s + '</th>' }
  html += '</tr>'

  @index.each_with_index do |index, num|
    html += '<tr>'
    html += '<td>' + index.to_s + '</td>'

    self.row[index].each do |element|
      html += '<td>' + element.to_s + '</td>'
    end

    html += '</tr>'
    if num > threshold
      html += '<tr>'
      (@vectors + 1).size.times { html += '<td>...</td>' }
      html += '</tr>'

      last_index = @index.to_a.last
      last_row = self.row[last_index]
      html += '<tr>'
      html += "<td>" + last_index.to_s + "</td>"
      (0..(ncols - 1)).to_a.each do |i|
        html += '<td>' + last_row[i].to_s + '</td>' 
      end
      html += '</tr>'
      break
    end
  end
  html += '</table>'

  html
end

#to_json(no_index = true) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1642

def to_json no_index=true
  if no_index
    self.to_a[0].to_json
  else
    self.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 1602

def to_matrix
  numerics_as_arrays = []
  each_vector do |vector|
    numerics_as_arrays << vector.to_a if(vector.type == :numeric)
  end

  Matrix.columns numerics_as_arrays
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 1617

def to_nmatrix
  numerics_as_arrays = []
  each_vector do |vector|
    numerics_as_arrays << vector.to_a if(vector.type == :numeric and 
      vector.missing_positions.size == 0)
  end

  numerics_as_arrays.transpose.to_nm
end

#to_nyaplotdfObject

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


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# File 'lib/daru/dataframe.rb', line 1612

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

#to_REXPObject


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

def to_REXP
  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 1703

def to_s
  to_html
end

#transposeObject

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


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# File 'lib/daru/dataframe.rb', line 1800

def transpose
  arrys = []
  each_vector do |vec|
    arrys << vec.to_a
  end

  Daru::DataFrame.new(arrys.transpose, index: @vectors, order: @index, dtype: @dtype, name: @name)
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 1712

def update
  @data.each { |v| v.update } if Daru.lazy_update
end

#vectorObject

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

Usage

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

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# File 'lib/daru/dataframe.rb', line 368

def vector
  Daru::Accessors::DataFrameByVector.new(self)
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 979

def vector_by_calculation &block
  a = []
  each_row do |r|
    a.push r.instance_eval(&block)
  end

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

#vector_count_characters(vecs = nil) ⇒ Object


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# File 'lib/daru/dataframe.rb', line 1058

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

  collect_row_with_index do |row, i|
    vecs.inject(0) do |memo, vec|
      memo + (row[vec].nil? ? 0 : row[vec].to_s.size)
    end
  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 1152

def vector_mean max_missing=0
  mean_vec = Daru::Vector.new [0]*@size, index: @index, name: "mean_#{@name}"

  each_row_with_index do |row, i|
    mean_vec[i] = row.missing_positions.size > max_missing ? nil : row.mean
  end

  mean_vec
end

#vector_sum(vecs = nil) ⇒ Object

Returns a vector with sum of all vectors specified in the argument. Tf vecs parameter is empty, sum all numeric vector.


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# File 'lib/daru/dataframe.rb', line 1135

def vector_sum vecs=nil
  vecs ||= numeric_vectors
  sum = Daru::Vector.new [0]*@size, index: @index, name: @name, dtype: @dtype

  vecs.each do |n|
    sum += self[n]
  end

  sum
end

#verify(*tests) ⇒ Object

Test each row with one or more tests. Each test is a Proc with the form *Proc.new {|row| row > 0}*

The function returns an array with all errors.


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# File 'lib/daru/dataframe.rb', line 935

def verify(*tests)
  if(tests[0].is_a? Symbol)
    id = tests[0]
    tests.shift
  else
    id = @vectors.first
  end

  vr = []
  i  = 0
  each(:row) do |row|
    i += 1
    tests.each do |test|
      if !test[2].call(row)
        values = ""
        if test[1].size>0
          values = " (" + test[1].collect{ |k| "#{k}=#{row[k]}" }.join(", ") + ")"
        end
        vr.push("#{i} [#{row[id]}]: #{test[0]}#{values}")
      end
    end
  end
  vr
end

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

Write this DataFrame to a CSV file.

Arguements

  • 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 1736

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 1745

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 1761

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