Table of Contents

  1. Introduction
  2. Installation
    1. Prerequisites
    2. Installing the gem
  3. Usage
    1. Quick Start
    2. A Word About the Examples
    3. Anatomy of a Table
      1. Columns
      2. Headers
      3. Groups
    4. Constructing Tables
      1. Empty Tables
      2. From CSV or Org Mode files or strings
      3. From Arrays of Arrays
      4. From Arrays of Hashes
      5. From SQL queries
      6. Marking Groups in Input
    5. Accessing Parts of Tables
      1. Rows
      2. Columns
      3. Cells
      4. Other table attributes
    6. Operations on Tables
      1. Example Input Table
      2. Select
      3. Where
      4. Order_by
      5. Group_by
      6. Join
      7. Set Operations
      8. Uniq (aka Distinct)
      9. Remove groups with degroup!
    7. Formatting Tables
      1. Available Formatters
      2. Table Locations
      3. Formatting Directives
      4. Footers Methods
      5. Formatting Methods
      6. The format and format_for methods
  4. Development
  5. Contributing

Build Status

Introduction

FatTable is a gem that treats tables as a data type. It provides methods for constructing tables from a variety of sources, building them row-by-row, extracting rows, columns, and cells, and performing aggregate operations on columns. It also provides as set of SQL-esque methods for manipulating table objects: select for filtering by columns or for creating new columns, where for filtering by rows, order_by for sorting rows, distinct for eliminating duplicate rows, group_by for aggregating multiple rows into single rows and applying column aggregate methods to ungrouped columns, a collection of join methods for combining tables, and more.

Furthermore, FatTable provides methods for formatting tables and producing output that targets various output media: text, ANSI terminals, ruby data structures, LaTeX tables, Emacs org-mode tables, and more. The formatting methods can specify cell formatting in a way that is uniform across all the output methods and can also decorate the output with any number of footers, including group footers. FatTable applies formatting directives to the extent they makes sense for the output medium and treats other formatting directives as no-ops.

FatTable can be used to perform operations on data that are naturally best conceived of as tables, which in my experience is quite often. It can also serve as a foundation for providing reporting functions where flexibility about the output medium can be quite useful. Finally FatTable can be used within Emacs org-mode files in code blocks targeting the Ruby language. Org mode tables are presented to a ruby code block as an array of arrays, so FatTable can read them in with its .from_aoa constructor. A FatTable table output as an array of arrays with its .to_aoa output function will be rendered in an org-mode buffer as an org-table, ready for processing by other code blocks.

Installation

Prerequisites

The fat_table gem depends on several libraries being available for building, mostly those concerned with accessing databases. On an ubuntu system, the following packages should be installed before you install the fat_table gem:

  • ruby-dev
  • build-essential
  • libsqlite3-dev
  • libpq-dev
  • libmysqlclient-dev

Installing the gem

Add this line to your application’s Gemfile:

gem 'fat_table'

And then execute:

$ bundle

Or install it yourself as:

$ gem install fat_table

Usage

Quick Start

FatTable provides table objects as a data type that can be constructed and operated on in a number of ways. Here’s a quick example to illustrate the use of the main features of FatTable. See the detailed explanations further on down.

require 'fat_table'

data =
    [['Date', 'Code', 'Raw', 'Shares', 'Price', 'Info', 'Ok'],
     ['2013-05-29', 'S', 15_700.00, 6601.85, 24.7790, 'ENTITY3', 'F'],
     ['2013-05-02', 'P', 118_186.40, 118_186.4, 11.8500, 'ENTITY1', 'T'],
     ['2013-05-20', 'S', 12_000.00, 5046.00, 28.2804, 'ENTITY3', 'F'],
     ['2013-05-23', 'S', 8000.00, 3364.00, 27.1083, 'ENTITY3', 'T'],
     ['2013-05-23', 'S', 39_906.00, 16_780.47, 25.1749, 'ENTITY3', 'T'],
     ['2013-05-20', 'S', 85_000.00, 35_742.50, 28.3224, 'ENTITY3', 'T'],
     ['2013-05-02', 'P', 795_546.20, 795_546.2, 1.1850, 'ENTITY1', 'T'],
     ['2013-05-29', 'S', 13_459.00, 5659.51, 24.7464, 'ENTITY3', 'T'],
     ['2013-05-20', 'S', 33_302.00, 14_003.49, 28.6383, 'ENTITY3', 'T'],
     ['2013-05-29', 'S', 15_900.00, 6685.95, 24.5802, 'ENTITY3', 'T'],
     ['2013-05-30', 'S', 6_679.00, 2808.52, 25.0471, 'ENTITY3', 'T'],
     ['2013-05-23', 'S', 23_054.00, 9694.21, 26.8015, 'ENTITY3', 'F']]

# Build the Table and then perform chained operations on it

table = FatTable.from_aoa(data) \
  .where('shares > 2000') \
  .order_by(:date, :code) \
  .select(:date, :code, :shares,
          :price, :ok, ref: '@row') \
  .select(:ref, :date, :code,
          :shares, :price, :ok)

# Convert the table to an ASCII text string

table.to_text do |fmt|
  # Add some table footers
  fmt.avg_footer(:price, :shares)
  fmt.sum_footer(:shares)
  # Add a group footer
  fmt.gfooter('Avg', shares: :avg, price: :avg)
  # Formats for all locations
  fmt.format(ref: 'CB', numeric: 'R', boolean: 'CY')
  # Formats for different "locations" in the table
  fmt.format_for(:header, string: 'CB')
  fmt.format_for(:body, code: 'C', shares: ',0.1', price: '0.4', )
  fmt.format_for(:bfirst, price: '$0.4', )
  fmt.format_for(:footer, shares: 'B,0.1', price: '$B0.4', )
  fmt.format_for(:gfooter, shares: 'B,0.1', price: 'B0.4', )
end

+=========+============+======+=============+==========+====+
|   Ref   |    Date    | Code |    Shares   |   Price  | Ok |
+---------|------------|------|-------------|----------|----+
|    1    | 2013-05-02 |   P  |   118,186.4 | $11.8500 |  Y |
|    2    | 2013-05-02 |   P  |   795,546.2 |   1.1850 |  Y |
+---------|------------|------|-------------|----------|----+
|   Avg   |            |      |   456,866.3 |   6.5175 |    |
+---------|------------|------|-------------|----------|----+
|    3    | 2013-05-20 |   S  |     5,046.0 |  28.2804 |  N |
|    4    | 2013-05-20 |   S  |    35,742.5 |  28.3224 |  Y |
|    5    | 2013-05-20 |   S  |    14,003.5 |  28.6383 |  Y |
+---------|------------|------|-------------|----------|----+
|   Avg   |            |      |    18,264.0 |  28.4137 |    |
+---------|------------|------|-------------|----------|----+
|    6    | 2013-05-23 |   S  |     3,364.0 |  27.1083 |  Y |
|    7    | 2013-05-23 |   S  |    16,780.5 |  25.1749 |  Y |
|    8    | 2013-05-23 |   S  |     9,694.2 |  26.8015 |  N |
+---------|------------|------|-------------|----------|----+
|   Avg   |            |      |     9,946.2 |  26.3616 |    |
+---------|------------|------|-------------|----------|----+
|    9    | 2013-05-29 |   S  |     6,601.9 |  24.7790 |  N |
|    10   | 2013-05-29 |   S  |     5,659.5 |  24.7464 |  Y |
|    11   | 2013-05-29 |   S  |     6,686.0 |  24.5802 |  Y |
+---------|------------|------|-------------|----------|----+
|   Avg   |            |      |     6,315.8 |  24.7019 |    |
+---------|------------|------|-------------|----------|----+
|    12   | 2013-05-30 |   S  |     2,808.5 |  25.0471 |  Y |
+---------|------------|------|-------------|----------|----+
|   Avg   |            |      |     2,808.5 |  25.0471 |    |
+---------|------------|------|-------------|----------|----+
| Average |            |      |    85,009.9 | $23.0428 |    |
+---------|------------|------|-------------|----------|----+
|  Total  |            |      | 1,020,119.1 |          |    |
+=========+============+======+=============+==========+====+

A Word About the Examples

When you install the fat_table gem, you have access to a program ft_console which opens a pry session with fat_table loaded and the tables used in the examples in this README defined as instance variables so you can experiment with them. Because they are defined as instance variables, you have to write tab1 as @tab1 in ft_console, but otherwise the examples should work.

The examples in this README file are executed as code blocks within the README.org file, so they typically end with a call to .to_aoa. That causes the table to be inserted into the file and formatted as a table. With ft_console, you should instead display your tables with .to_text or .to_term. These will return a string that you can print to the terminal with puts.

To read in the table used in the Quick Start section above, you might do the following:

$ ft_console[1] pry(main)> ls
ActiveSupport::ToJsonWithActiveSupportEncoder#methods: to_json
self.methods: inspect  to_s
instance variables:
  @aoa   @tab1      @tab2      @tab_a      @tab_b      @tt
  @data  @tab1_str  @tab2_str  @tab_a_str  @tab_b_str
locals: _  __  _dir_  _ex_  _file_  _in_  _out_  _pry_  lib  str  version
[2] pry(main)> table = FatTable.from_aoa(@data)
=> #<FatTable::Table:0x0055b40e6cd870
 @boundaries=[],
 @columns=
  [#<FatTable::Column:0x0055b40e6cc948
    @header=:date,
    @items=
     [Wed, 29 May 2013,
      Thu, 02 May 2013,
      Mon, 20 May 2013,
      Thu, 23 May 2013,
      Thu, 23 May 2013,
      Mon, 20 May 2013,
      Thu, 02 May 2013,
      Wed, 29 May 2013,
      Mon, 20 May 2013,
...
    @items=["ENTITY3", "ENTITY1", "ENTITY3", "ENTITY3", "ENTITY3", "ENTITY3", "ENTITY1", "ENTITY3", "ENTITY3", "ENTITY3", "ENTITY3", "ENTITY3"],
    @raw_header=:info,
    @type="String">,
   #<FatTable::Column:0x0055b40e6d2668 @header=:ok, @items=[false, true, false, true, true, true, true, true, true, true, true, false], @raw_header=:ok, @type="Boolean">]>
[3] pry(main)> puts table.to_text
+============+======+==========+==========+=========+=========+====+
| Date       | Code | Raw      | Shares   | Price   | Info    | Ok |
+------------|------|----------|----------|---------|---------|----+
| 2013-05-29 | S    | 15700.0  | 6601.85  | 24.779  | ENTITY3 | F  |
| 2013-05-02 | P    | 118186.4 | 118186.4 | 11.85   | ENTITY1 | T  |
| 2013-05-20 | S    | 12000.0  | 5046.0   | 28.2804 | ENTITY3 | F  |
| 2013-05-23 | S    | 8000.0   | 3364.0   | 27.1083 | ENTITY3 | T  |
| 2013-05-23 | S    | 39906.0  | 16780.47 | 25.1749 | ENTITY3 | T  |
| 2013-05-20 | S    | 85000.0  | 35742.5  | 28.3224 | ENTITY3 | T  |
| 2013-05-02 | P    | 795546.2 | 795546.2 | 1.185   | ENTITY1 | T  |
| 2013-05-29 | S    | 13459.0  | 5659.51  | 24.7464 | ENTITY3 | T  |
| 2013-05-20 | S    | 33302.0  | 14003.49 | 28.6383 | ENTITY3 | T  |
| 2013-05-29 | S    | 15900.0  | 6685.95  | 24.5802 | ENTITY3 | T  |
| 2013-05-30 | S    | 6679.0   | 2808.52  | 25.0471 | ENTITY3 | T  |
| 2013-05-23 | S    | 23054.0  | 9694.21  | 26.8015 | ENTITY3 | F  |
+============+======+==========+==========+=========+=========+====+
=> nil
[4] pry(main)>

And if you use .to_term, you can see the effect of the color formatting directives.

Anatomy of a Table

Columns

FatTable::Table objects consist of an array of FatTable::Column objects. Each Column has a header, a type, and an array of items, all of the given type or nil. There are only five permissible types for a Column:

  1. Boolean (for holding ruby TrueClass and FalseClass objects),
  2. DateTime (for holding ruby DateTime or Date objects),
  3. Numeric (for holding ruby Integer, Rational, or BigDecimal objects),
  4. String (for ruby String objects), or
  5. NilClass (for the undetermined column type).

When a Table is constructed from an external source, all Columns start out having a type of NilClass, that is, their type is as yet undetermined. When a string or object of one of the four determined types is added to a Column, it fixes the type of the column and all further items added to the Column must either be nil (indicating no value) or be capable of being coerced to the column’s type. Otherwise, FatTable raises an exception.

Items of input must be either one of the permissible ruby objects or strings. If they are strings, FatTable attempts to parse them as one of the permissible types as follows:

  • Boolean: the strings, 't', 'true', 'yes', or 'y', regardless of case, are interpreted as TrueClass and the strings, 'f', 'false', 'no', or 'n', regardless of case, are interpreted as FalseClass, in either case resulting in a Boolean column. Empty strings in a column already having a Boolean type are converted to nil.
  • DateTime: strings that contain patterns of 'yyyy-mm-dd' or 'yyyy/mm/dd' or 'mm-dd-yyy' or 'mm/dd/yyyy' or any of the foregoing with an added 'Thh:mm:ss' or 'Thh:mm' will be interpreted as a DateTime or a Date (if there are no sub-day time components present). The number of digits in the month and day can be one or two, but the year component must be four digits. Any time components are valid if they can be properly interpreted by DateTime.parse. Org mode timestamps (any of the foregoing surrounded by square ’[]’ or pointy ’<>’ brackets), active or inactive, are valid input strings for DateTime columns. Empty strings in a column already having the DateTime type are converted to nil.
  • Numeric: all commas ',', underscores, '_', and '$' dollar signs (or other currency symbol as set by FatTable.currency_symbol are removed from the string and if the remaining string can be interpreted as a Numeric, it will be. It is interpreted as an Integer if there are no decimal places in the remaining string, as a Rational if the string has the form ’<number>:<number>’ or ’<number>/<number>’, or as a BigDecimal if there is a decimal point in the remaining string. Empty strings in a column already having the Numeric type are converted to nil.
  • String: if all else fails, FatTable applies #to_s to the input value and, treats it as an item of type String. Empty strings in a column already having the String type are kept as empty strings.
  • NilClass: until the input contains a non-blank string that can be parsed as one of the other types, it has this type, meaning that the type is still open. A column comprised completely of blank strings or nils will retain the NilClass type.

Headers

Headers for the columns are formed from the input. No two columns in a table can have the same header. Headers in the input are converted to symbols by

  • converting the header to a string with #to_s,
  • converting any run of blanks to an underscore _,
  • removing any characters that are not letters, numbers, or underscores, and
  • lowercasing all remaining letters

Thus, a header of 'Date' becomes :date, a header of 'Id Number' becomes, :id_number, etc. When referring to a column in code, you must use the symbol form of the header.

If no sensible headers can be discerned from the input, headers of the form :col_1, :col_2, etc., are synthesized.

Groups

The rows of a FatTable table can be sub-divided into groups, either from markers in the input or as a result of certain operations. There is only one level of grouping, so FatTable has no concept of sub-groups. Groups can be shown on output with rules or “hlines” that underline the last row in each group, and you can decorate the output with group footers that summarize the columns in each group.

Constructing Tables

Empty Tables

You can create an empty table with FatTable.new, and then add rows with the << operator and a Hash:

tab = FatTable.new
tab << { a: 1, b: 2, c: "<2017-01-21>', d: 'f', e: '' }
tab << { a: 3.14, b: 2.17, c: '[2016-01-21 Thu]', d: 'Y', e: nil }
tab.to_aoa

After this, the table will have column headers :a, :b, :c, :d, and :e. Column, :a and :b will have type Numeric, column :c will have type DateTime, and column :d will have type Boolean. Column :e will still have an open type. Notice that dates in the input can be wrapped in brackets as in org-mode time stamps.

From CSV or Org Mode files or strings

Tables can also be read from .csv files or files containing org-mode tables. In the case of org-mode files, FatTable skips through the file until it finds a line that look like a table, that is, it begins with any number of spaces followed by |-. Only the first table in an .org file is read.

For both .csv and .org files, the first row in the tables is taken as the header row, and the headers are converted to symbols as described above.

    tab1 = FatTable.from_csv_file('~/data.csv')
    tab2 = FatTable.from_org_file('~/project.org')

    csv_body = <<-EOS
  Ref,Date,Code,RawShares,Shares,Price,Info
  1,2006-05-02,P,5000,5000,8.6000,2006-08-09-1-I
  2,2006-05-03,P,5000,5000,8.4200,2006-08-09-1-I
  3,2006-05-04,P,5000,5000,8.4000,2006-08-09-1-I
  4,2006-05-10,P,8600,8600,8.0200,2006-08-09-1-D
  5,2006-05-12,P,10000,10000,7.2500,2006-08-09-1-D
  6,2006-05-12,P,2000,2000,6.7400,2006-08-09-1-I
  EOS

    tab3 = FatTable.from_csv_string(csv_body)

    org_body = <<-EOS
.* Smith Transactions
:PROPERTIES:
:TABLE_EXPORT_FILE: smith.csv
:END:

#+TBLNAME: smith_tab
| Ref |       Date | Code |     Raw | Shares |    Price | Info    |
|-----|------------|------|---------|--------|----------|---------|
|  29 | 2013-05-02 | P    | 795,546 |  2,609 |  1.18500 | ENTITY1 |
|  30 | 2013-05-02 | P    | 118,186 |    388 | 11.85000 | ENTITY1 |
|  31 | 2013-05-02 | P    | 340,948 |  1,926 |  1.18500 | ENTITY2 |
|  32 | 2013-05-02 | P    |  50,651 |    286 | 11.85000 | ENTITY2 |
|  33 | 2013-05-20 | S    |  12,000 |     32 | 28.28040 | ENTITY3 |
|  34 | 2013-05-20 | S    |  85,000 |    226 | 28.32240 | ENTITY3 |
|  35 | 2013-05-20 | S    |  33,302 |     88 | 28.63830 | ENTITY3 |
|  36 | 2013-05-23 | S    |   8,000 |     21 | 27.10830 | ENTITY3 |
|  37 | 2013-05-23 | S    |  23,054 |     61 | 26.80150 | ENTITY3 |
|  38 | 2013-05-23 | S    |  39,906 |    106 | 25.17490 | ENTITY3 |
|  39 | 2013-05-29 | S    |  13,459 |     36 | 24.74640 | ENTITY3 |
|  40 | 2013-05-29 | S    |  15,700 |     42 | 24.77900 | ENTITY3 |
|  41 | 2013-05-29 | S    |  15,900 |     42 | 24.58020 | ENTITY3 |
|  42 | 2013-05-30 | S    |   6,679 |     18 | 25.04710 | ENTITY3 |

.* Another Heading
EOS

    tab4 = FatTable.from_org_string(org_body)

From Arrays of Arrays

You can also initialize a table directly from ruby data structures. You can, for example, build a table from an array of arrays:

aoa = [
  ['Ref', 'Date', 'Code', 'Raw', 'Shares', 'Price', 'Info', 'Bool'],
  [1, '2013-05-02', 'P', 795_546.20, 795_546.2, 1.1850, 'ENTITY1', 'T'],
  [2, '2013-05-02', 'P', 118_186.40, 118_186.4, 11.8500, 'ENTITY1', 'T'],
  [7, '2013-05-20', 'S', 12_000.00, 5046.00, 28.2804, 'ENTITY3', 'F'],
  [8, '2013-05-20', 'S', 85_000.00, 35_742.50, 28.3224, 'ENTITY3', 'T'],
  [9, '2013-05-20', 'S', 33_302.00, 14_003.49, 28.6383, 'ENTITY3', 'T'],
  [10, '2013-05-23', 'S', 8000.00, 3364.00, 27.1083, 'ENTITY3', 'T'],
  [11, '2013-05-23', 'S', 23_054.00, 9694.21, 26.8015, 'ENTITY3', 'F'],
  [12, '2013-05-23', 'S', 39_906.00, 16_780.47, 25.1749, 'ENTITY3', 'T'],
  [13, '2013-05-29', 'S', 13_459.00, 5659.51, 24.7464, 'ENTITY3', 'T'],
  [14, '2013-05-29', 'S', 15_700.00, 6601.85, 24.7790, 'ENTITY3', 'F'],
  [15, '2013-05-29', 'S', 15_900.00, 6685.95, 24.5802, 'ENTITY3', 'T'],
  [16, '2013-05-30', 'S', 6_679.00, 2808.52, 25.0471, 'ENTITY3', 'T']
]
tab = FatTable.from_aoa(aoa)

Notice that the values can either be ruby objects, such as the Integer 85_000, or strings that can be parsed into one of the permissible column types.

This method of building a table, .from_aoa, is particularly useful in dealing with Emacs org-mode code blocks. Tables in org-mode are passed to code blocks as arrays of arrays. Likewise, a result of a code block in the form of an array of arrays is displayed as an org-mode table:

#+NAME: trades1
| Ref  |       Date | Code |  Price | G10 | QP10 | Shares |    LP |     QP |   IPLP |   IPQP |
|------|------------|------|--------|-----|------|--------|-------|--------|--------|--------|
| T001 | 2016-11-01 | P    | 7.7000 | T   | F    |    100 |    14 |     86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    | 7.7500 | T   | F    |    200 |    28 |    172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    | 7.5000 | F   | T    |    800 |   112 |    688 | 0.2453 | 0.1924 |
| T004 | 2016-11-01 | S    | 7.5500 | T   | F    |   6811 |   966 |   5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    | 7.5000 | F   | F    |   4000 |   572 |   3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    | 7.6000 | F   | T    |   1000 |   143 |    857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    | 7.6500 | T   | F    |    200 |    28 |    172 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    | 7.6500 | F   | F    |   2771 |   393 |   2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    | 7.6000 | F   | F    |   9550 |  1363 |   8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    | 7.5500 | F   | T    |   3175 |   451 |   2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.4250 | T   | F    |    100 |    14 |     86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    | 7.5500 | F   | F    |   4700 |   677 |   4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    | 7.3500 | T   | T    |  53100 |  7656 |  45444 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    | 7.4500 | F   | T    |   5847 |   835 |   5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    | 7.7500 | F   | F    |    500 |    72 |    428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    | 8.2500 | T   | T    |    100 |    14 |     86 | 0.2453 | 0.1924 |

#+HEADER: :colnames no
:#+BEGIN_SRC ruby :var tab=trades1
  require 'fat_table'
  tab = FatTable.from_aoa(tab).where('shares > 500')
  tab.to_aoa
:#+END_SRC

#+RESULTS:
| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |  835 |  5012 | 0.2453 | 0.1924 |

This example illustrates several things:

  1. The named org-mode table, trades1, can be passed into a ruby code block using the :var tab=trades1 header argument to the code block; that makes the variable tab available to the code block as an array of arrays, which FatTable then uses to initialize the table.
  2. The code block requires that you set :colnames no in the header arguments. This suppresses org-mode’s own processing of the header line so that FatTable can see the headers. Failure to do this will cause an error.
  3. The table is subjected to some processing, in this case selecting those rows where the number of shares is greater than 500. More on that later.
  4. FatTable passes back to org-mode an array of arrays using the .to_aoa method. In an org-mode buffer, these are rendered as tables. We’ll often apply .to_aoa at the end of example blocks to render the results inside this README.org file. As we’ll see below, this method can also take a block to which formatting directives and footers can be attached.

From Arrays of Hashes

A second ruby data structure that can be used to initialize a FatTable table is an array of ruby Hashes. Each hash represents a row of the table, and the headers of the table are take from the keys of the hashes. Accordingly, all the hashes should have the same keys.

This same method can in fact take an array of any objects that can be converted to a Hash with the #to_h method, so you can use an array of your own objects to initialize a table, provided that you define a suitable #to_h method for the objects’ class.

aoh = [
  { ref: 'T001', date: '2016-11-01', code: 'P', price: '7.7000',  shares: 100 },
  { ref: 'T002', date: '2016-11-01', code: 'P', price: 7.7500,  shares: 200 },
  { ref: 'T003', date: '2016-11-01', code: 'P', price: 7.5000,  shares: 800 },
  { ref: 'T004', date: '2016-11-01', code: 'S', price: 7.5500,  shares: 6811 },
  { ref: 'T005', date: Date.today, code: 'S', price: 7.5000,  shares: 4000 },
  { ref: 'T006', date: '2016-11-01', code: 'S', price: 7.6000,  shares: 1000 },
  { ref: 'T007', date: '2016-11-01', code: 'S', price: 7.6500,  shares: 200 },
  { ref: 'T008', date: '2016-11-01', code: 'P', price: 7.6500,  shares: 2771 },
  { ref: 'T009', date: '2016-11-01', code: 'P', price: 7.6000,  shares: 9550 },
  { ref: 'T010', date: '2016-11-01', code: 'P', price: 7.5500,  shares: 3175 },
  { ref: 'T011', date: '2016-11-02', code: 'P', price: 7.4250,  shares: 100 },
  { ref: 'T012', date: '2016-11-02', code: 'P', price: 7.5500,  shares: 4700 },
  { ref: 'T013', date: '2016-11-02', code: 'P', price: 7.3500,  shares: 53100 },
  { ref: 'T014', date: '2016-11-02', code: 'P', price: 7.4500,  shares: 5847 },
  { ref: 'T015', date: '2016-11-02', code: 'P', price: 7.7500,  shares: 500 },
  { ref: 'T016', date: '2016-11-02', code: 'P', price: 8.2500,  shares: 100 }
]
tab = FatTable.from_aoh(aoh)

Notice, again, that the values can either be ruby objects, such as Date.today, or strings that can parsed into one of the permissible column types.

From SQL queries

Another way to initialize a FatTable table is with the results of a SQL query. FatTable uses the sequel gem to query databases. You must first set the database parameters to be used for the queries.

# This automatically requires sequel.
require 'fat_table'
FatTable.connect(driver: 'Pg',
                database: 'XXX_development',
                user: 'dtd',
                password: 'slflpowert',
                host: 'localhost',
                socket: '/tmp/.s.PGSQL.5432')
tab = FatTable.from_sql('select * from trades;')

Some of the parameters to the .connect function have defaults. The driver defaults to 'Pg' for postgresql and the socket defaults to /tmp/.s.PGSQL.5432 if the host is ’localhost’, which it is by default. If the host is not 'localhost', the dsn uses a port rather than a socket and defaults to port '5432'. While user and password default to nil, the database parameter is required.

The .connect function need only be called once, and the database handle it creates will be used for all subsequent .from_sql calls until .connect is called again.

Alternatively, you can build the Sequel connection with Sequel.connect or with adapter-specific Sequel connection methods and let FatTable know to use that connection:

require 'fat_table'
FatTable.db = Sequel.connect('postgres://user:password@localhost/dbname')
FatTable.db = Sequel.ado(conn_string: 'Provider=Microsoft.ACE.OLEDB.12.0;Data Source=drive:\path\filename.accdb')

Consult Sequel's documentation for details on its connection methods. http://sequel.jeremyevans.net/rdoc/files/doc/opening_databases_rdoc.html

Marking Groups in Input

The .from_aoa and .from_aoh functions take an optional keyword parameter hlines: that, if set to true, causes them to mark group boundaries in the table wherever a row Array (for .from_aoa) or Hash (for .from_aoh) is followed by a nil. Each boundary means that the rows above it and after the header or prior group boundary all belong to a group. By default hlines is false for both functions so neither expects hlines in its input.

In the case of .from_aoa, if hlines: is set true, the input must also include a nil in the second element of the outer array to indicate that the first row is to be used as headers. Otherwise, it will synthesize headers of the form :col_1, :col_2, … :col_n.

In org mode table text passed to .from_org_file and .from_org_string, you must mark the header row by following it with an hrule and you may mark group boundaries with an hrule. In org mode tables, hlines are table rows beginning with something like ’|---’. The .from_org_... functions always recognizes hlines in the input, so it takes no hlines: keyword parameter.

Accessing Parts of Tables

Rows

A FatTable table is an Enumerable, yielding each row of the table as a Hash keyed on the header symbols. The method Table#rows returns an Array of the rows as Hashes as well.

You can also use indexing to access a row of the table by number. Using an integer index returns a Hash of the given row. Thus, tab[20] returns the 21st data row of the table, while tab[0] returns the first row and tab[-1] returns the last row.

Columns

If the index provided to [] is a string or a symbol, it returns an Array of the items of the column with that header. Thus, tab[:ref] returns an Array of all the items of the table’s :ref column.

Cells

The two forms of indexing can be combined to access individual cells of the table:

tab[13]         # => Hash of the 14th row
tab[:date]      # => Array of all Dates in the :date column
tab[13][:date]  # => The Date in the 14th row
tab[:date][13]  # => The Date in the 14th row; indexes can be in either order.

Other table attributes

tab.headers       # => an Array of the headers in symbol form
tab.types         # => a Hash mapping headers to column types
tab.size          # => the number of rows in the table
tab.width         # => the number of columns in the table
tab.empty?        # => is the table empty?
tab.column?(head) # => does the table have a column with the given header?
tab.groups        # => return an Array of the table's groups as Arrays of row Hashes.

Operations on Tables

Once you have one or more tables, you will likely want to perform operations on them. The operations provided by FatTable are the subject of this section. Before getting into the operations, though, there are a couple of issues that cut across all or many of the operations.

First, tables are by and large immutable objects. Each operation creates a new table without affecting the input tables. The only exception is the degroup! operation, which mutates the receiver table by removing its group boundaries.

Second, because each operation returns a FatTable::Table object, the operations are chainable.

Third, FatTable::Table objects can have “groups” of rows within the table. These can be decorated with hlines and group footers on output. Some of these operations result in marking group boundaries in the result table, others remove group boundaries that may have existed in the input table. Operations that either create or remove groups will be noted below.

Finally, the operations are for the most part patterned on SQL table operations, but when expressions play a role, you write them using ruby syntax rather than SQL.

Example Input Table

For illustration purposes assume that the following tables are read into ruby variables called ’tab1’ and ’tab2. We have given the table groups, marked by the hlines below, and included some duplicate rows to illustrate the effect of certain operations on groups and duplicates.

require 'fat_table'

tab1_str = <<-EOS
| Ref  | Date             | Code |  Price | G10 | QP10 | Shares |   LP |    QP |   IPLP |   IPQP |
|------|------------------|------|--------|-----|------|--------|------|-------|--------|--------|
| T001 | [2016-11-01 Tue] | P    | 7.7000 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T002 | [2016-11-01 Tue] | P    | 7.7500 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T003 | [2016-11-01 Tue] | P    | 7.5000 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T003 | [2016-11-01 Tue] | P    | 7.5000 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
|------|------------------|------|--------|-----|------|--------|------|-------|--------|--------|
| T004 | [2016-11-01 Tue] | S    | 7.5500 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | [2016-11-01 Tue] | S    | 7.5000 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T006 | [2016-11-01 Tue] | S    | 7.6000 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T006 | [2016-11-01 Tue] | S    | 7.6000 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T007 | [2016-11-01 Tue] | S    | 7.6500 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T008 | [2016-11-01 Tue] | P    | 7.6500 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | [2016-11-01 Tue] | P    | 7.6000 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
|------|------------------|------|--------|-----|------|--------|------|-------|--------|--------|
| T010 | [2016-11-01 Tue] | P    | 7.5500 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T011 | [2016-11-02 Wed] | P    | 7.4250 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T012 | [2016-11-02 Wed] | P    | 7.5500 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T012 | [2016-11-02 Wed] | P    | 7.5500 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | [2016-11-02 Wed] | P    | 7.3500 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |
|------|------------------|------|--------|-----|------|--------|------|-------|--------|--------|
| T014 | [2016-11-02 Wed] | P    | 7.4500 | F   | T    |   5847 |  835 |  5012 | 0.2453 | 0.1924 |
| T015 | [2016-11-02 Wed] | P    | 7.7500 | F   | F    |    500 |   72 |   428 | 0.2453 | 0.1924 |
| T016 | [2016-11-02 Wed] | P    | 8.2500 | T   | T    |    100 |   14 |    86 | 0.2453 | 0.1924 |
EOS

tab2_str = <<-EOS
| Ref  | Date             | Code |  Price | G10 | QP10 | Shares |    LP |   QP |   IPLP |   IPQP |
|------|------------------|------|--------|-----|------|--------|-------|------|--------|--------|
| T003 | [2016-11-01 Tue] | P    | 7.5000 | F   | T    |    800 |   112 |  688 | 0.2453 | 0.1924 |
| T003 | [2016-11-01 Tue] | P    | 7.5000 | F   | T    |    800 |   112 |  688 | 0.2453 | 0.1924 |
| T017 | [2016-11-01 Tue] | P    |    8.3 | F   | T    |   1801 |  1201 |  600 | 0.2453 | 0.1924 |
|------|------------------|------|--------|-----|------|--------|-------|------|--------|--------|
| T018 | [2016-11-01 Tue] | S    |  7.152 | T   | F    |   2516 |  2400 |  116 | 0.2453 | 0.1924 |
| T018 | [2016-11-01 Tue] | S    |  7.152 | T   | F    |   2516 |  2400 |  116 | 0.2453 | 0.1924 |
| T006 | [2016-11-01 Tue] | S    | 7.6000 | F   | T    |   1000 |   143 |  857 | 0.2453 | 0.1924 |
| T007 | [2016-11-01 Tue] | S    | 7.6500 | T   | F    |    200 |    28 |  172 | 0.2453 | 0.1924 |
|------|------------------|------|--------|-----|------|--------|-------|------|--------|--------|
| T014 | [2016-11-02 Wed] | P    | 7.4500 | F   | T    |   5847 |   835 | 5012 | 0.2453 | 0.1924 |
| T015 | [2016-11-02 Wed] | P    | 7.7500 | F   | F    |    500 |    72 |  428 | 0.2453 | 0.1924 |
| T015 | [2016-11-02 Wed] | P    | 7.7500 | F   | F    |    500 |    72 |  428 | 0.2453 | 0.1924 |
| T016 | [2016-11-02 Wed] | P    | 8.2500 | T   | T    |    100 |    14 |   86 | 0.2453 | 0.1924 |
|------|------------------|------|--------|-----|------|--------|-------|------|--------|--------|
| T019 | [2017-01-15 Sun] | S    |   8.75 | T   | F    |    300 |   175 |  125 | 0.2453 | 0.1924 |
| T020 | [2017-01-19 Thu] | S    |   8.25 | F   | T    |    700 |   615 |   85 | 0.2453 | 0.1924 |
| T021 | [2017-01-23 Mon] | P    |   7.16 | T   | T    |  12100 | 11050 | 1050 | 0.2453 | 0.1924 |
| T021 | [2017-01-23 Mon] | P    |   7.16 | T   | T    |  12100 | 11050 | 1050 | 0.2453 | 0.1924 |
EOS

tab1 = FatTable.from_org_string(tab1_str)
tab2 = FatTable.from_org_string(tab2_str)

Select

With the select method, you can select which existing columns should appear in the output table and create new columns in the output table that are a function of existing and new columns.

  1. Selecting Existing Columns

    Here we select three existing columns by simply passing header symbols in the order we want them to appear in the output. Thus, one use of select is to filter and permute the order of existing columns. The select method preserves any group boundaries present in the input table.

    tab1.select(:price, :ref, :shares).to_aoa

| Price | Ref  | Shares |
|-------|------|--------|
|   7.7 | T001 |    100 |
|  7.75 | T002 |    200 |
|   7.5 | T003 |    800 |
|   7.5 | T003 |    800 |
|-------|------|--------|
|  7.55 | T004 |   6811 |
|   7.5 | T005 |   4000 |
|   7.6 | T006 |   1000 |
|   7.6 | T006 |   1000 |
|  7.65 | T007 |    200 |
|  7.65 | T008 |   2771 |
|   7.6 | T009 |   9550 |
|-------|------|--------|
|  7.55 | T010 |   3175 |
| 7.425 | T011 |    100 |
|  7.55 | T012 |   4700 |
|  7.55 | T012 |   4700 |
|  7.35 | T013 |  53100 |
|-------|------|--------|
|  7.45 | T014 |   5847 |
|  7.75 | T015 |    500 |
|  8.25 | T016 |    100 |

  2. Adding New Columns

    More interesting is that select can take hash-like keyword arguments after the symbol arguments to create new columns in the output as functions of other columns. For each hash-like parameter, the keyword given must be a symbol, which becomes the header for the new column, and the value must be either: (1) a symbol representing an existing column, which has the effect of renaming an existing column, or (2) a string representing a ruby expression for the value of a new column.

    Within the string expression, the names of existing or already-specified columns are available as local variables, as well as the instance variables ’@row’ and ’@group’. So for our example table, the string expressions for new columns have access to local variables ref, date, code, price, g10, qp10, shares, lp, qp, iplp, and ipqp as well as the instance variables @row and @group. The local variables are set to the values of the cell in their respective columns for each row in the input table and the instance variables are set the number of the current row and group respectively.

    For example, if we want to rename the :date column and add a new column to compute the cost of shares, we could do the following:

    tab1.select(:ref, :price, :shares, traded_on: :date, cost: 'price * shares').to_aoa

| Ref  | Price | Shares |  Traded On |     Cost |
|------|-------|--------|------------|----------|
| T001 |   7.7 |    100 | 2016-11-01 |    770.0 |
| T002 |  7.75 |    200 | 2016-11-01 |   1550.0 |
| T003 |   7.5 |    800 | 2016-11-01 |   6000.0 |
| T003 |   7.5 |    800 | 2016-11-01 |   6000.0 |
|------|-------|--------|------------|----------|
| T004 |  7.55 |   6811 | 2016-11-01 | 51423.05 |
| T005 |   7.5 |   4000 | 2016-11-01 |  30000.0 |
| T006 |   7.6 |   1000 | 2016-11-01 |   7600.0 |
| T006 |   7.6 |   1000 | 2016-11-01 |   7600.0 |
| T007 |  7.65 |    200 | 2016-11-01 |   1530.0 |
| T008 |  7.65 |   2771 | 2016-11-01 | 21198.15 |
| T009 |   7.6 |   9550 | 2016-11-01 |  72580.0 |
|------|-------|--------|------------|----------|
| T010 |  7.55 |   3175 | 2016-11-01 | 23971.25 |
| T011 | 7.425 |    100 | 2016-11-02 |    742.5 |
| T012 |  7.55 |   4700 | 2016-11-02 |  35485.0 |
| T012 |  7.55 |   4700 | 2016-11-02 |  35485.0 |
| T013 |  7.35 |  53100 | 2016-11-02 | 390285.0 |
|------|-------|--------|------------|----------|
| T014 |  7.45 |   5847 | 2016-11-02 | 43560.15 |
| T015 |  7.75 |    500 | 2016-11-02 |   3875.0 |
| T016 |  8.25 |    100 | 2016-11-02 |    825.0 |

    The parameter ’traded_on: :date’ caused the :date column of the input table to be renamed ’:traded_on, and the parameter cost: 'price * shares' created a new column, :cost, as the product of values in the :price and :shares columns.

    The order of the columns in the result tables is the same as the order of the parameters to the select method. So, you can re-order the columns with a second, chained call to select:

    tab1.select(:ref, :price, :shares, traded_on: :date, cost: 'price * shares') \
  .select(:ref, :traded_on, :price, :shares, :cost) \
  .to_aoa

| Ref  |  Traded On | Price | Shares |     Cost |
|------|------------|-------|--------|----------|
| T001 | 2016-11-01 |   7.7 |    100 |    770.0 |
| T002 | 2016-11-01 |  7.75 |    200 |   1550.0 |
| T003 | 2016-11-01 |   7.5 |    800 |   6000.0 |
| T003 | 2016-11-01 |   7.5 |    800 |   6000.0 |
|------|------------|-------|--------|----------|
| T004 | 2016-11-01 |  7.55 |   6811 | 51423.05 |
| T005 | 2016-11-01 |   7.5 |   4000 |  30000.0 |
| T006 | 2016-11-01 |   7.6 |   1000 |   7600.0 |
| T006 | 2016-11-01 |   7.6 |   1000 |   7600.0 |
| T007 | 2016-11-01 |  7.65 |    200 |   1530.0 |
| T008 | 2016-11-01 |  7.65 |   2771 | 21198.15 |
| T009 | 2016-11-01 |   7.6 |   9550 |  72580.0 |
|------|------------|-------|--------|----------|
| T010 | 2016-11-01 |  7.55 |   3175 | 23971.25 |
| T011 | 2016-11-02 | 7.425 |    100 |    742.5 |
| T012 | 2016-11-02 |  7.55 |   4700 |  35485.0 |
| T012 | 2016-11-02 |  7.55 |   4700 |  35485.0 |
| T013 | 2016-11-02 |  7.35 |  53100 | 390285.0 |
|------|------------|-------|--------|----------|
| T014 | 2016-11-02 |  7.45 |   5847 | 43560.15 |
| T015 | 2016-11-02 |  7.75 |    500 |   3875.0 |
| T016 | 2016-11-02 |  8.25 |    100 |    825.0 |

  3. Custom Instance Variables and Hooks

    As the above examples demonstrate, the instance variables @row and @group are available when evaluating expressions that add new columns. You can also set up your own instance variables as well for keeping track of things that cross row boundaries, such as running sums.

    To declare instance variables, you can use the ivars: hash parameter to select. Each key of the hash becomes an instance variable and each value becomes its initial value before any rows are evaluated.

    In addition, you can provide before_hook: and after_hook: parameters to select as strings that are evaluated as ruby expressions before and after each row is processed. You can use these to update instance variables. The values set in the before_hook: can be used in expressions for adding new columns by referencing them with the ’@’ prefix.

    For example, suppose we wanted to not only add a cost column, but a column that shows the cumulative cost after each transaction in our example table. The following example uses the ivars: and before_hook: parameters to keep track of the running cost of shares, then formats the table.

    tab = tab1.select(:ref, :price, :shares, traded_on: :date, \
            cost: 'price * shares', cumulative: '@total_cost', \
            ivars: { total_cost: 0 }, \
            before_hook: '@total_cost += price * shares')
FatTable.to_aoa(tab) do |f|
  f.format(price: '0.4', shares: '0.0,', cost: '0.2,', cumulative: '0.2,')
end

| Ref  |  Price | Shares |  Traded On |       Cost | Cumulative |
|------|--------|--------|------------|------------|------------|
| T001 | 7.7000 |    100 | 2016-11-01 |     770.00 |     770.00 |
| T002 | 7.7500 |    200 | 2016-11-01 |   1,550.00 |   2,320.00 |
| T003 | 7.5000 |    800 | 2016-11-01 |   6,000.00 |   8,320.00 |
| T003 | 7.5000 |    800 | 2016-11-01 |   6,000.00 |  14,320.00 |
|------|--------|--------|------------|------------|------------|
| T004 | 7.5500 |  6,811 | 2016-11-01 |  51,423.05 |  65,743.05 |
| T005 | 7.5000 |  4,000 | 2016-11-01 |  30,000.00 |  95,743.05 |
| T006 | 7.6000 |  1,000 | 2016-11-01 |   7,600.00 | 103,343.05 |
| T006 | 7.6000 |  1,000 | 2016-11-01 |   7,600.00 | 110,943.05 |
| T007 | 7.6500 |    200 | 2016-11-01 |   1,530.00 | 112,473.05 |
| T008 | 7.6500 |  2,771 | 2016-11-01 |  21,198.15 | 133,671.20 |
| T009 | 7.6000 |  9,550 | 2016-11-01 |  72,580.00 | 206,251.20 |
|------|--------|--------|------------|------------|------------|
| T010 | 7.5500 |  3,175 | 2016-11-01 |  23,971.25 | 230,222.45 |
| T011 | 7.4250 |    100 | 2016-11-02 |     742.50 | 230,964.95 |
| T012 | 7.5500 |  4,700 | 2016-11-02 |  35,485.00 | 266,449.95 |
| T012 | 7.5500 |  4,700 | 2016-11-02 |  35,485.00 | 301,934.95 |
| T013 | 7.3500 | 53,100 | 2016-11-02 | 390,285.00 | 692,219.95 |
|------|--------|--------|------------|------------|------------|
| T014 | 7.4500 |  5,847 | 2016-11-02 |  43,560.15 | 735,780.10 |
| T015 | 7.7500 |    500 | 2016-11-02 |   3,875.00 | 739,655.10 |
| T016 | 8.2500 |    100 | 2016-11-02 |     825.00 | 740,480.10 |

  4. Argument Order and Boundaries

    Notice that select can take any number of arguments but all the symbol arguments must come first followed by all the hash-like keyword arguments, including the special arguments for instance variables and hooks.

    As the example illustrates, .select transmits any group boundaries in its input table to the result table.

Where

You can filter the rows of the result table with the .where method. It takes a single string expression as an argument which is evaluated in a manner similar to .select in which the value of the cells in each column are available as local variables and the instance variables @row and @group are available for testing. The expression is evaluated for each row, and if the expression evaluates to a truthy value, the row is included in the output, otherwise it is not. The .where method obliterates any group boundaries in the input, so the output table has only a single group.

Here we select only those even-numbered rows where either of the two boolean fields is true:

tab1.where('@row.even? && (g10 || qp10)') \
  .to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |

Order_by

You can sort a table on any number of columns with order_by. The order_by method takes any number of symbol arguments for the columns to sort on. If you specify more than one column, the sort is performed on the first column, then all columns that are equal with respect to the first column are sorted by the second column, and so on. All columns of the input table are included in the output.

Let’s sort our table first by :code, then by :date.

tab1.order_by(:code, :date) \
  .to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |  835 |  5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |   72 |   428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |   14 |    86 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |

The interesting thing about order_by is that, while it ignores groups in its input, it adds group boundaries in the output table at those rows where the sort keys change. Thus, in each group, :code and :date are the same, and when either changes, order_by inserts a group boundary.

Group_by

Like order_by, group_by takes a set of parameters of column header symbols, the “grouping parameters”, by which to sort the table into a set of groups that are equal with respect to values in those columns. In addition, those parameters can be followed by a series of hash-like parameters, the “aggregating parameters”, that indicate how any of the remaining, non-group columns are to be aggregated into a single value. The output table has one row for each group for which the grouping parameters are equal containing those columns and an aggregate column for each of the aggregating parameters.

For example, let’s summarize the trades table by :code and :price again, and determine total shares, average price, and a few other features of each group:

tab1.group_by(:code, :date, price: :avg,
              shares: :sum, lp: :sum, qp: :sum,
              qp10: :all?) \
  .to_aoa { |f| f.format(avg_price: '0.5R') }

| Code |       Date | Avg Price | Sum Shares | Sum Lp | Sum Qp | All QP10 |
|------|------------|-----------|------------|--------|--------|----------|
| P    | 2016-11-01 |   7.60714 |      17396 |   2473 |  14923 | F        |
| P    | 2016-11-02 |   7.61786 |      69047 |   9945 |  59102 | F        |
| S    | 2016-11-01 |   7.58000 |      13011 |   1852 |  11159 | F        |

After the grouping column parameters, :code and :date, there are several hash-like “aggregating” parameters where the key is the column to aggregate and the value is a symbol for one of several aggregating methods that FatTable::Column objects understand. For example, the :avg method is applied to the :price column so that the output shows the average price in each group. The :shares, :lp, and :qp columns are summed, and the :any? aggregate is applied to one of the boolean fields, that is, it is true if any of the values in that column are true. The column names in the output of the aggregated columns have the name of the aggregating method pre-pended to the column name.

Here is a list of all the aggregate methods available. If the description restricts the aggregate to particular column types, applying it to other types will raise an exception.

  • first: the first non-nil item in the column,
  • last: the last non-nil item in the column,
  • range: form a Range ~~min..max~ to show the range of values in the column,
  • sum: for Numeric and String columns, apply ’+’ to all the non-nil values,
  • count: the number of non-nil values in the column,
  • min: for Numeric, String, and DateTime columns, return the smallest non-nil value in the column,
  • max: for Numeric, String, and DateTime columns, return the largest non-nil value in the column,
  • avg: for Numeric and DateTime columns, return the arithmetic mean of the non-nil values in the column; with respect to Date or DateTime objects, each is converted to a numeric Julian date, the average is calculated, and the result converted back to a Date or DateTime object,
  • var: for Numeric and DateTime columns, compute the sample variance of the non-nil values in the column, dates are converted to Julian date numbers as for the :avg aggregate,
  • pvar: for Numeric and DateTime columns, compute the population variance of the non-nil values in the column, dates are converted to Julian date numbers as for the :avg aggregate,
  • dev: for Numeric and DateTime columns, compute the sample standard deviation of the non-nil values in the column, dates are converted to Julian date numbers as for the :avg aggregate,
  • pdev: for Numeric and DateTime columns, compute the population standard deviation of the non-nil values in the column, dates are converted to numbers as for the :avg aggregate,
  • all?: for Boolean columns only, return true if all of the non-nil values in the column are true,
  • any?: for Boolean columns only, return true if any non-nil value in the column is true,
  • none?: for Boolean columns only, return true if no non-nil value in the column is true,
  • one?: for Boolean columns only, return true if exactly one non-nil value in the column is true,

Perhaps surprisingly, the group_by method ignores any groups in its input and results in no group boundaries in the output since each group formed by the implicit order_by on the grouping columns is collapsed into a single row.

Join

  1. Join Types

    So far, all the operations have operated on a single table. FatTable provides several join methods for combining two tables, each of which takes as parameters (1) a second table and (2) except in the case of cross_join, zero or more “join expressions”. In the descriptions below, T1 is the table on which the method is called, T2 is the table supplied as the first parameter other, and R1 and R2 are rows in their respective tables being considered for inclusion in the joined output table.

-   **`join(other, *jexps)`:** Performs an &ldquo;inner join&rdquo; on the tables. For each row
    `R1` of `T1`, the joined table has a row for each row in `T2` that
    satisfies the join condition with `R1`.

-   **`left_join(other, *jexps)`:** First, an inner join is performed. Then, for
    each row in `T1` that does not satisfy the join condition with any row in
    `T2`, a joined row is added with null values in columns of `T2`. Thus, the
    joined table always has at least one row for each row in `T1`.

-   **`right_join(other, *jexps)`:** First, an inner join is performed. Then, for
    each row in `T2` that does not satisfy the join condition with any row in
    `T1`, a joined row is added with null values in columns of `T1`. This is
    the converse of a left join: the result table will always have a row for
    each row in `T2`.

-   **`full_join(other, *jexps)`:** First, an inner join is performed. Then, for
    each row in `T1` that does not satisfy the join condition with any row in
    `T2`, a joined row is added with null values in columns of `T2`. Also, for
    each row of `T2` that does not satisfy the join condition with any row in
    `T1`, a joined row with null values in the columns of `T1` is added.

-   **`cross_join(other)`:** For every possible combination of rows from `T1` and
    `T2` (i.e., a Cartesian product), the joined table will contain a row
    consisting of all columns in `T1` followed by all columns in `T2`. If the
    tables have `N` and `M` rows respectively, the joined table will have `N *
         M` rows.

  1. Join Expressions

    For each of the join types, if no join expressions are given, the tables will be joined on columns having the same column header in both tables, and the join condition is satisfied when all the values in those columns are equal. If the join type is an inner join, this is a so-called “natural” join.

    If the join expressions are one or more symbols, the join condition requires that the values of both tables are equal for all columns named by the symbols. A column that appears in both tables can be given without modification and will be assumed to require equality on that column. If an unmodified symbol is not a name that appears in both tables, an exception will be raised. Column names that are unique to the first table must have a _a appended to the column name and column names that are unique to the other table must have a _b appended to the column name. These disambiguated column names must come in pairs, one for the first table and one for the second, and they will imply a join condition that the columns must be equal on those columns. Several such symbol expressions will require that all such implied pairs are equal in order for the join condition to be met.

    Finally, a join expression can be a string that contains an arbitrary ruby expression that will be evaluated for truthiness. Within the string, all column names must be disambiguated with the _a or _b modifiers whether they are common to both tables or not. As with select and where methods, the names of the columns in both tables (albeit disambiguated) are available as local variables within the expression, but the instance variables @row and @group are not.

  2. Join Examples

    The following examples are taken from the Postgresql tutorial, with some slight modifications. The examples will use the following two tables, which are also available in ft_console as @tab_a and @tab_b:

    require 'fat_table'

    tab_a_str = <<-EOS
  | Id | Name  | Age | Address    | Salary |  Join Date |
  |----|-------|-----|------------|--------|------------|
  |  1 | Paul  |  32 | California |  20000 | 2001-07-13 |
  |  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |
  |  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |
  |  5 | David |  27 | Texas      |  85000 | 2007-12-13 |
  |  2 | Allen |  25 | Texas      |        | 2005-07-13 |
  |  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |
  |  9 | James |  44 | Norway     |   5000 | 2005-07-13 |
  | 10 | James |  45 | Texas      |   5000 |            |
  EOS

    tab_b_str = <<-EOS
  | Id | Dept        | Emp Id |
  |----|-------------|--------|
  |  1 | IT Billing  |      1 |
  |  2 | Engineering |      2 |
  |  3 | Finance     |      7 |
  EOS

    tab_a = FatTable.from_org_string(tab_a_str)
    tab_b = FatTable.from_org_string(tab_b_str)

    1. Inner Joins

      With no join expression arguments, the tables are joined when their sole common field, :id, is equal in both tables. The result is the natural join of the two tables.

      tab_a.join(tab_b).to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date | Dept        | Emp Id |
|----|-------|-----|------------|--------|------------|-------------|--------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 | IT Billing  |      1 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 | Finance     |      7 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 | Engineering |      2 |

      But the natural join joined employee IDs in the first table and department IDs in the second table. To correct this, we need to explicitly state the columns we want to join on in each table by disambiguating them with _a and _b suffixes:

      tab_a.join(tab_b, :id_a, :emp_id_b).to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date | Id B | Dept        |
|----|-------|-----|------------|--------|------------|------|-------------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    1 | IT Billing  |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    2 | Engineering |

      Instead of using the disambiguated column names as symbols, we could also use a string containing a ruby expression. Within the expression, the column names should be treated as local variables:

      tab_a.join(tab_b, 'id_a == emp_id_b').to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date | Id B | Dept        | Emp Id |
|----|-------|-----|------------|--------|------------|------|-------------|--------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    1 | IT Billing  |      1 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    2 | Engineering |      2 |

    2. Left and Right Joins

      In left join, all the rows of tab_a are included in the output, augmented by the matching columns of tab_b and augmented with nils where there is no match:

      tab_a.left_join(tab_b, 'id_a == emp_id_b').to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date | Id B | Dept        | Emp Id |
|----|-------|-----|------------|--------|------------|------|-------------|--------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    1 | IT Billing  |      1 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |      |             |        |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |      |             |        |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |      |             |        |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    2 | Engineering |      2 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |      |             |        |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |      |             |        |
| 10 | James |  45 | Texas      |   5000 |            |      |             |        |

      In a right join, all the rows of tab_b are included in the output, augmented by the matching columns of tab_a and augmented with nils where there is no match:

      tab_a.right_join(tab_b, 'id_a == emp_id_b').to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date | Id B | Dept        | Emp Id |
|----|-------|-----|------------|--------|------------|------|-------------|--------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    1 | IT Billing  |      1 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    2 | Engineering |      2 |
|    |       |     |            |        |            |    3 | Finance     |      7 |

    3. Full Join

      A full join combines the effects of a left join and a right join. All the rows from both tables are included in the output augmented by columns of the other table where the join expression is satisfied and augmented with nils otherwise.

      tab_a.full_join(tab_b, 'id_a == emp_id_b').to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date | Id B | Dept        | Emp Id |
|----|-------|-----|------------|--------|------------|------|-------------|--------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    1 | IT Billing  |      1 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |      |             |        |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |      |             |        |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |      |             |        |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    2 | Engineering |      2 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |      |             |        |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |      |             |        |
| 10 | James |  45 | Texas      |   5000 |            |      |             |        |
|    |       |     |            |        |            |    3 | Finance     |      7 |

    4. Cross Join

      Finally, a cross join outputs every row of tab_a augmented with every row of tab_b, in other words, the Cartesian product of the two tables. If tab_a has N rows and tab_b has M rows, the output table will have N * M rows.

      tab_a.cross_join(tab_b).to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date | Id B | Dept        | Emp Id |
|----|-------|-----|------------|--------|------------|------|-------------|--------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    1 | IT Billing  |      1 |
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    2 | Engineering |      2 |
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |    3 | Finance     |      7 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |    1 | IT Billing  |      1 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |    2 | Engineering |      2 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |    3 | Finance     |      7 |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |    1 | IT Billing  |      1 |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |    2 | Engineering |      2 |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |    3 | Finance     |      7 |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |    1 | IT Billing  |      1 |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |    2 | Engineering |      2 |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |    3 | Finance     |      7 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    1 | IT Billing  |      1 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    2 | Engineering |      2 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |    3 | Finance     |      7 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |    1 | IT Billing  |      1 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |    2 | Engineering |      2 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |    3 | Finance     |      7 |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |    1 | IT Billing  |      1 |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |    2 | Engineering |      2 |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |    3 | Finance     |      7 |
| 10 | James |  45 | Texas      |   5000 |            |    1 | IT Billing  |      1 |
| 10 | James |  45 | Texas      |   5000 |            |    2 | Engineering |      2 |
| 10 | James |  45 | Texas      |   5000 |            |    3 | Finance     |      7 |

Set Operations

FatTable can perform several set operations on tables. In order for two tables to be used this way, they must have the same number of columns with the same types or an exception will be raised. We’ll call two tables that qualify for combining with set operations “set-compatible.”

We’ll use the following two set-compatible tables in the examples. They each have some duplicates and some group boundaries so you can see the effect of the set operations on duplicates and groups.

tab1.to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |  835 |  5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |   72 |   428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |   14 |    86 | 0.2453 | 0.1924 |

tab2.to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |    Lp |   Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-------|------|--------|--------|
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |   112 |  688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |   112 |  688 | 0.2453 | 0.1924 |
| T017 | 2016-11-01 | P    |   8.3 | F   | T    |   1801 |  1201 |  600 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|------|--------|--------|
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |  116 | 0.2453 | 0.1924 |
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |  116 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |   143 |  857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |    28 |  172 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|------|--------|--------|
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |   835 | 5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |    72 |  428 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |    72 |  428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |    14 |   86 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|------|--------|--------|
| T019 | 2017-01-15 | S    |  8.75 | T   | F    |    300 |   175 |  125 | 0.2453 | 0.1924 |
| T020 | 2017-01-19 | S    |  8.25 | F   | T    |    700 |   615 |   85 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 | 1050 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 | 1050 | 0.2453 | 0.1924 |

  1. Unions

    Two tables that are set-compatible can be combined with the union or union_all methods so that the rows of both tables appear in the output. In the output table, the headers of the receiver table are used. You can use select to change or re-order the headers if you prefer. The union method eliminates duplicate rows in the result table, the union_all method does not.

    Any group boundaries in the input tables are destroyed by union but are preserved by union_all. In addition, union_all (but not union) adds a group boundary between the rows of the two input tables.

    tab1.union(tab2).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |    Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |    14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |    28 |   172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |   112 |   688 | 0.2453 | 0.1924 |
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |   966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |   572 |  3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |   143 |   857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |    28 |   172 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |   393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 |  1363 |  8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |   451 |  2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |    14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |   677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 |  7656 | 45444 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |   835 |  5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |    72 |   428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |    14 |    86 | 0.2453 | 0.1924 |
| T017 | 2016-11-01 | P    |   8.3 | F   | T    |   1801 |  1201 |   600 | 0.2453 | 0.1924 |
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |   116 | 0.2453 | 0.1924 |
| T019 | 2017-01-15 | S    |  8.75 | T   | F    |    300 |   175 |   125 | 0.2453 | 0.1924 |
| T020 | 2017-01-19 | S    |  8.25 | F   | T    |    700 |   615 |    85 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 |  1050 | 0.2453 | 0.1924 |

tab1.union_all(tab2).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |    Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |    14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |    28 |   172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |   112 |   688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |   112 |   688 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |   966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |   572 |  3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |   143 |   857 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |   143 |   857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |    28 |   172 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |   393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 |  1363 |  8187 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |   451 |  2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |    14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |   677 |  4023 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |   677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 |  7656 | 45444 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |   835 |  5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |    72 |   428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |    14 |    86 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |   112 |   688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |   112 |   688 | 0.2453 | 0.1924 |
| T017 | 2016-11-01 | P    |   8.3 | F   | T    |   1801 |  1201 |   600 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |   116 | 0.2453 | 0.1924 |
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |   116 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |   143 |   857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |    28 |   172 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |   835 |  5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |    72 |   428 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |    72 |   428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |    14 |    86 | 0.2453 | 0.1924 |
|------|------------|------|-------|-----|------|--------|-------|-------|--------|--------|
| T019 | 2017-01-15 | S    |  8.75 | T   | F    |    300 |   175 |   125 | 0.2453 | 0.1924 |
| T020 | 2017-01-19 | S    |  8.25 | F   | T    |    700 |   615 |    85 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 |  1050 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 |  1050 | 0.2453 | 0.1924 |

  2. Intersections

    The intersect method returns a table having only rows common to both tables, eliminating any duplicate rows in the result.

    tab1.intersect(tab2).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |  Lp |   Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-----|------|--------|--------|
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 | 112 |  688 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 | 143 |  857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |  28 |  172 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 | 835 | 5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |  72 |  428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |  14 |   86 | 0.2453 | 0.1924 |

    With intersect_all, all the rows of the first table, including duplicates, are included in the result if they also occur in the second table. However, duplicates in the second table do not appear.

    tab1.intersect_all(tab2).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |  Lp |   Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-----|------|--------|--------|
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 | 112 |  688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 | 112 |  688 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 | 143 |  857 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 | 143 |  857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |  28 |  172 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 | 835 | 5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |  72 |  428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |  14 |   86 | 0.2453 | 0.1924 |

    As a result, it makes a difference which table is the receiver of the intersect_all method call and which is the argument. In other words, order of operation matters.

    tab2.intersect_all(tab1).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |  Lp |   Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-----|------|--------|--------|
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 | 112 |  688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 | 112 |  688 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 | 143 |  857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |  28 |  172 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 | 835 | 5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |  72 |  428 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |  72 |  428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |  14 |   86 | 0.2453 | 0.1924 |

  3. Differences with Except

    You can use the except method to delete from a table any rows that occur in another table, that is, compute the set difference between the tables.

    tab1.except(tab2).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |

    Like subtraction, though, the order of operands matters with set difference computed by except.

    tab2.except(tab1).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |    Lp |   Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-------|------|--------|--------|
| T017 | 2016-11-01 | P    |   8.3 | F   | T    |   1801 |  1201 |  600 | 0.2453 | 0.1924 |
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |  116 | 0.2453 | 0.1924 |
| T019 | 2017-01-15 | S    |  8.75 | T   | F    |    300 |   175 |  125 | 0.2453 | 0.1924 |
| T020 | 2017-01-19 | S    |  8.25 | F   | T    |    700 |   615 |   85 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 | 1050 | 0.2453 | 0.1924 |

    As with intersect_all, except_all includes any duplicates in the first, receiver table, but not those in the second, argument table.

    tab1.except_all(tab2).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |

    And, of course, the order of operands matters here as well.

    tab2.except_all(tab1).to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |    Lp |   Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|-------|------|--------|--------|
| T017 | 2016-11-01 | P    |   8.3 | F   | T    |   1801 |  1201 |  600 | 0.2453 | 0.1924 |
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |  116 | 0.2453 | 0.1924 |
| T018 | 2016-11-01 | S    | 7.152 | T   | F    |   2516 |  2400 |  116 | 0.2453 | 0.1924 |
| T019 | 2017-01-15 | S    |  8.75 | T   | F    |    300 |   175 |  125 | 0.2453 | 0.1924 |
| T020 | 2017-01-19 | S    |  8.25 | F   | T    |    700 |   615 |   85 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 | 1050 | 0.2453 | 0.1924 |
| T021 | 2017-01-23 | P    |  7.16 | T   | T    |  12100 | 11050 | 1050 | 0.2453 | 0.1924 |

Uniq (aka Distinct)

The uniq method takes no arguments and simply removes any duplicate rows from the input table. The distinct method is an alias for uniq. Any groups in the input table are lost.

tab1.uniq.to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |  835 |  5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |   72 |   428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |   14 |    86 | 0.2453 | 0.1924 |

Remove groups with degroup!

Finally, it is sometimes helpful to remove any group boundaries from a table. You can do this with .degroup!, which is the only operation that mutates its receiver table by removing its groups.

tab1.degroup!.to_aoa

| Ref  |       Date | Code | Price | G10 | QP10 | Shares |   Lp |    Qp |   Iplp |   Ipqp |
|------|------------|------|-------|-----|------|--------|------|-------|--------|--------|
| T001 | 2016-11-01 | P    |   7.7 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T002 | 2016-11-01 | P    |  7.75 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T003 | 2016-11-01 | P    |   7.5 | F   | T    |    800 |  112 |   688 | 0.2453 | 0.1924 |
| T004 | 2016-11-01 | S    |  7.55 | T   | F    |   6811 |  966 |  5845 | 0.2453 | 0.1924 |
| T005 | 2016-11-01 | S    |   7.5 | F   | F    |   4000 |  572 |  3428 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T006 | 2016-11-01 | S    |   7.6 | F   | T    |   1000 |  143 |   857 | 0.2453 | 0.1924 |
| T007 | 2016-11-01 | S    |  7.65 | T   | F    |    200 |   28 |   172 | 0.2453 | 0.1924 |
| T008 | 2016-11-01 | P    |  7.65 | F   | F    |   2771 |  393 |  2378 | 0.2453 | 0.1924 |
| T009 | 2016-11-01 | P    |   7.6 | F   | F    |   9550 | 1363 |  8187 | 0.2453 | 0.1924 |
| T010 | 2016-11-01 | P    |  7.55 | F   | T    |   3175 |  451 |  2724 | 0.2453 | 0.1924 |
| T011 | 2016-11-02 | P    | 7.425 | T   | F    |    100 |   14 |    86 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T012 | 2016-11-02 | P    |  7.55 | F   | F    |   4700 |  677 |  4023 | 0.2453 | 0.1924 |
| T013 | 2016-11-02 | P    |  7.35 | T   | T    |  53100 | 7656 | 45444 | 0.2453 | 0.1924 |
| T014 | 2016-11-02 | P    |  7.45 | F   | T    |   5847 |  835 |  5012 | 0.2453 | 0.1924 |
| T015 | 2016-11-02 | P    |  7.75 | F   | F    |    500 |   72 |   428 | 0.2453 | 0.1924 |
| T016 | 2016-11-02 | P    |  8.25 | T   | T    |    100 |   14 |    86 | 0.2453 | 0.1924 |

Formatting Tables

Besides creating and operating on tables, you may want to display the resulting table. FatTable seeks to provide a set of formatting directives that are the most common across many output media. It provides directives for alignment, for color, for adding currency symbols and grouping commas to numbers, for padding numbers, and for formatting dates and booleans.

In addition, you can add any number of footers to a table, which appear at the end of the table, and any number of group footers, which appear after each group in the table. These can be formatted independently of the table body.

If the target output medium does not support a formatting directive or the directive does not make sense, it is simply ignored. For example, you can output an org-mode table as a String, and since org-mode does not support colors, any color directives are ignored. Some of the output targets are not strings, but ruby data structures, and for them, things such as alignment are irrelevant.

Available Formatters

FatTable supports the following output targets for its tables:

  • Text: form the table with ACSII characters,
  • Org: form the table with ASCII characters but in the form used by Emacs org-mode for constructing tables,
  • Term: form the table with ANSI terminal codes and unicode characters, possibly including colored text and cell backgrounds,
  • LaTeX: form the table as input for LaTeX’s longtable environment,
  • Aoh: output the table as a ruby data structure, building the table as an array of hashes, and
  • Aoa: output the table as a ruby data structure, building the table as an array of array,

These are all implemented by classes that inherit from FatTable::Formatter class by defining about a dozen methods that get called at various places during the construction of the output table. The idea is that more classes can be defined by adding additional classes.

Table Locations

In the formatting methods, the table is divided into several “locations” for which separate formatting directives may be given. These locations are identified with the following symbols:

  • :header: the first row of the output table containing the headers,
  • :footer: all rows of the table’s footers,
  • :gfooter: all rows of the table’s group footers,
  • :body: all the data rows of the table, that is, those that are neither part of the header, footers, or gfooters,
  • :bfirst: the first row of the table’s body, and
  • :gfirst: the first row in each group in the table’s body.

Formatting Directives

The formatting methods explained in the next section all take formatting directives as strings in which letters and other characters signify what formatting applies. For example, we may apply the formatting directive 'R,$' to numbers in a certain part of the table. Each of those characters, and in some cases a whole substring, is a single directive. They can appear in any order, so '$R,' and ',$R' are equivalent.

Here is a list of all the formatting directives that apply to each cell type:

  1. String

    For a string element, the following instructions are valid. Note that these can also be applied to all the other cell types as well since they are all converted to a string in forming the output.

-   **u:** convert the element to all lowercase,
-   **U:** convert the element to all uppercase,
-   **t:** title case the element, that is, upcase the initial letter in
    each word and lower case the other letters
-   **B ~B:** make the element bold, or turn off bold
-   **I ~I:** make the element italic, or turn off italic
-   **R:** align the element on the right of the column
-   **L:** align the element on the left of the column
-   **C:** align the element in the center of the column
-   **c[color]:** render the element in the given color; the color can have
    the form fgcolor, fgcolor.bgcolor, or .bgcolor, to set the
    foreground or background colors respectively, and each of those can
    be an ANSI or X11 color name in addition to the special color,
    &rsquo;none&rsquo;, which keeps the terminal&rsquo;s default color.
-   **\_ ~\_:** underline the element, or turn off underline
-   **\* ~\*:** cause the element to blink, or turn off blink

For example, the directive `'tCc[red.yellow]'` would title-case the element,
center it, and color it red on a yellow background. The directives that are
boolean have negating forms so that, for example, if bold is turned on for all
columns of a given type, it can be countermanded in formatting directives for
particular columns.

  1. Numeric

    For a numeric element, all the instructions valid for string are available, in addition to the following:

-   **, ~,:** insert grouping commas, or do not insert grouping commas,
-   **$ ~$:** format the number as currency according to the locale, or not,
-   **m.n:** include at least m digits before the decimal point, padding on
    the left with zeroes as needed, and round the number to the n
    decimal places and include n digits after the decimal point,
    padding on the right with zeroes as needed,
-   **H:** convert the number (assumed to be in units of seconds) to `HH:MM:SS.ss`
    form. So a column that is the result of subtracting two :datetime forms
    will result in a :numeric expressed as seconds and can be displayed in
    hours, minutes, and seconds with this formatting instruction.

For example, the directive `'R5.0c[blue]'` would right-align the numeric
element, pad it on the left with zeros, and color it blue.

  1. DateTime

    For a DateTime, all the instructions valid for string are available, in addition to the following:

-   **d[fmt]:** apply the format to a `Date` or a  `DateTime` that is a whole day,
    that is that has no or zero hour, minute, and second components, where fmt
    is a valid format string for `Date#strftime`, otherwise, the datetime will
    be formatted as an ISO 8601 string, YYYY-MM-DD.
-   **D[fmt]:** apply the format to a datetime that has at least a non-zero hour
    component where fmt is a valid format string for Date#strftime, otherwise,
    the datetime will be formatted as an ISO 8601 string, YYYY-MM-DD.

For example, `'c[pink]d[%b %-d, %Y]C'`, would format a date element like &rsquo;Sep
22, 1957&rsquo;, center it, and color it pink.

  1. Boolean

    For a boolean cell, all the instructions valid for string are available, in addition to the following:

-   **Y:** print true as &rsquo;`Y`&rsquo; and false as &rsquo;`N`&rsquo;,
-   **T:** print true as &rsquo;`T`&rsquo; and false as &rsquo;`F`&rsquo;,
-   **X:** print true as &rsquo;`X`&rsquo; and false as an empty string &rsquo;&rsquo;,
-   **b[xxx,yyy]:** print true as the string given as `xxx` and false as the string
    given as `yyy`,
-   **c[tcolor,fcolor]:** color a true element with `tcolor` and a false element
    with `fcolor`. Each of the colors may be specified in the same manner as
    colors for strings described above.

For example, the directive &rsquo;`b[Yeppers,Nope]c[green.pink,red.pink]`&rsquo; would
render a true boolean as &rsquo;`Yeppers`&rsquo; colored green on pink and render a false
boolean as &rsquo;`Nope`&rsquo; colored red on pink. See [Yeppers](https://www.youtube.com/watch?v=oLdFFD8II8U) for additional information.

  1. NilClass

    By default, nil elements are rendered as blank cells, but you can make them visible with the following, and in that case, all the formatting instructions valid for strings are also available:

-   **n[niltext]:** render a `nil` item with the given niltext.

For example, you might want to use `'n[-]Cc[purple]'` to make nils visible as a
centered purple hyphen.

Footers Methods

You can call the footer and gfooter methods on Formatter objects to add footers and group footers. Their signatures are:

  • footer(label, *sum_cols, **agg_cols): where label is a label to be placed in the first cell of the footer (unless that column is named as one of the sum_cols or agg_cols, in which case the label is ignored), *sum_cols are zero or more symbols for columns to be summed, and **agg_cols is zero or more hash-like parameters with a column symbol as a key and a symbol for an aggregate method as the value. This causes a table-wide header to be added at the bottom of the table applying the :sum aggregate to the sum_cols and the named aggregate method to the agg_cols. A table can have any number of footers attached, and they will appear at the bottom of the output table in the order they are given.

  • gfooter(label, *sum_cols, **agg_cols): where the parameters have the same meaning as for the footer method, but result in a footer for each group in the table rather than the table as a whole. These will appear in the output table just below each group.

There are also a number of convenience methods for adding common footers:

  • sum_footer(*cols): Add a footer summing the given columns with the label ’Total’.
  • sum_gfooter(*cols): Add a group footer summing the given columns with the label ’Group Total’.
  • avg_footer(*cols): Add a footer averaging the given columns with the label ’Average’.
  • avg_gfooter(*cols): Add a group footer averaging the given columns with the label ’Group Average’.
  • min_footer(*cols): Add a footer showing the minimum for the given columns with the label ’Minimum’.
  • min_gfooter(*cols): Add a group footer showing the minumum for the given columns with the label ’Group Minimum’.
  • max_footer(*cols): Add a footer showing the maximum for the given columns with the label ’Maximum’.
  • max_gfooter(*cols): Add a group footer showing the maximum for the given columns with the label ’Group Maximum’.

Formatting Methods

You can call methods on Formatter objects to specify formatting directives for specific columns or types. There are two methods for doing so, format_for and format.

  1. Instantiating a Formatter

    There are several ways to invoke the formatting methods on a table. First, you can instantiate a XXXFormatter object and feed it a table as a parameter. There is a Formatter subclass for each target output medium, for example, AoaFormatter will produce a ruby array of arrays. You can then call the output method on the XXXFormatter.

    FatTable::AoaFormatter.new(tab_a).output

| Id | Name  | Age | Address    | Salary |  Join Date |
|----|-------|-----|------------|--------|------------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |
| 10 | James |  45 | Texas      |   5000 |            |

    The XXXFormatter.new method yields the new instance to any block given, and you can call methods on it to affect the formatting of the output:

    FatTable::AoaFormatter.new(tab_a) do |f|
  f.format(numeric: '0.0,R', id: '3.0C')
end.output

|  Id | Name  | Age | Address    | Salary |  Join Date |
|-----|-------|-----|------------|--------|------------|
| 001 | Paul  |  32 | California | 20,000 | 2001-07-13 |
| 003 | Teddy |  23 | Norway     | 20,000 | 2007-12-13 |
| 004 | Mark  |  25 | Rich-Mond  | 65,000 | 2007-12-13 |
| 005 | David |  27 | Texas      | 85,000 | 2007-12-13 |
| 002 | Allen |  25 | Texas      |        | 2005-07-13 |
| 008 | Paul  |  24 | Houston    | 20,000 | 2005-07-13 |
| 009 | James |  44 | Norway     |  5,000 | 2005-07-13 |
| 010 | James |  45 | Texas      |  5,000 |            |

  2. FatTable module-level method calls

    The FatTable module provides a set of methods of the form to_aoa, to_text, etc., to access a Formatter without having to create an instance yourself. Without a block, they apply the default formatting to the table and call the .output method automatically:

    FatTable.to_aoa(tab_a)

| Id | Name  | Age | Address    | Salary |  Join Date |
|----|-------|-----|------------|--------|------------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |
| 10 | James |  45 | Texas      |   5000 |            |

    With a block, these methods yield a Formatter instance on which you can call formatting and footer methods. The .output method is called on the Formatter automatically after the block:

    FatTable.to_aoa(tab_a) do |f|
  f.format(numeric: '0.0,R', id: '3.0C')
end

|  Id | Name  | Age | Address    | Salary |  Join Date |
|-----|-------|-----|------------|--------|------------|
| 001 | Paul  |  32 | California | 20,000 | 2001-07-13 |
| 003 | Teddy |  23 | Norway     | 20,000 | 2007-12-13 |
| 004 | Mark  |  25 | Rich-Mond  | 65,000 | 2007-12-13 |
| 005 | David |  27 | Texas      | 85,000 | 2007-12-13 |
| 002 | Allen |  25 | Texas      |        | 2005-07-13 |
| 008 | Paul  |  24 | Houston    | 20,000 | 2005-07-13 |
| 009 | James |  44 | Norway     |  5,000 | 2005-07-13 |
| 010 | James |  45 | Texas      |  5,000 |            |

  3. Calling methods on Table objects

    Finally, you can call methods such as to_aoa, to_text, etc., directly on a Table:

    tab_a.to_aoa

| Id | Name  | Age | Address    | Salary |  Join Date |
|----|-------|-----|------------|--------|------------|
|  1 | Paul  |  32 | California |  20000 | 2001-07-13 |
|  3 | Teddy |  23 | Norway     |  20000 | 2007-12-13 |
|  4 | Mark  |  25 | Rich-Mond  |  65000 | 2007-12-13 |
|  5 | David |  27 | Texas      |  85000 | 2007-12-13 |
|  2 | Allen |  25 | Texas      |        | 2005-07-13 |
|  8 | Paul  |  24 | Houston    |  20000 | 2005-07-13 |
|  9 | James |  44 | Norway     |   5000 | 2005-07-13 |
| 10 | James |  45 | Texas      |   5000 |            |

    And you can supply a block to them as well to specify formatting or footers:

    tab_a.to_aoa do |f|
  f.format(numeric: '0.0,R', id: '3.0C')
  f.sum_footer(:salary, :age)
end

|    Id | Name  | Age | Address    |  Salary |  Join Date |
|-------|-------|-----|------------|---------|------------|
|   001 | Paul  |  32 | California |  20,000 | 2001-07-13 |
|   003 | Teddy |  23 | Norway     |  20,000 | 2007-12-13 |
|   004 | Mark  |  25 | Rich-Mond  |  65,000 | 2007-12-13 |
|   005 | David |  27 | Texas      |  85,000 | 2007-12-13 |
|   002 | Allen |  25 | Texas      |         | 2005-07-13 |
|   008 | Paul  |  24 | Houston    |  20,000 | 2005-07-13 |
|   009 | James |  44 | Norway     |   5,000 | 2005-07-13 |
|   010 | James |  45 | Texas      |   5,000 |            |
|-------|-------|-----|------------|---------|------------|
| Total |       | 245 |            | 220,000 |            |

The format and format_for methods

Formatters take only two kinds of methods, those that attach footers to a table, which are discussed in the next section, and those that specify formatting for table cells, which are the subject of this section.

To set formatting directives for all locations in a table at once, use the format method; to set formatting directives for a particular location in the table, use the format_for method, giving the location as the first parameter.

Other than that first parameter, the two methods take the same types of parameters. The remaining parameters are hash-like parameters that use either a column name or a type as the key and a string with the formatting directives to apply as the value. The following example says to set the formatting for all locations in the table and to format all numeric fields as strings that are rounded to whole numbers (the ’0.0’ part), that are right-aligned (the ’R’ part), and have grouping commas inserted (the ’,’ part). But the :id column is numeric, and the second parameter overrides the formatting for numerics in general and calls for the :id column to be padded to three digits with zeros on the left (the ’3.0’ part) and to be centered (the ’C’ part).

tab_a.to_aoa do |f|
  f.format(numeric: '0.0,R', id: '3.0C')
end

|  Id | Name  | Age | Address    | Salary |  Join Date |
|-----|-------|-----|------------|--------|------------|
| 001 | Paul  |  32 | California | 20,000 | 2001-07-13 |
| 003 | Teddy |  23 | Norway     | 20,000 | 2007-12-13 |
| 004 | Mark  |  25 | Rich-Mond  | 65,000 | 2007-12-13 |
| 005 | David |  27 | Texas      | 85,000 | 2007-12-13 |
| 002 | Allen |  25 | Texas      |        | 2005-07-13 |
| 008 | Paul  |  24 | Houston    | 20,000 | 2005-07-13 |
| 009 | James |  44 | Norway     |  5,000 | 2005-07-13 |
| 010 | James |  45 | Texas      |  5,000 |            |

The numeric: directive affected the :age and :salary columns and the id: directive affected only the :id column. All the other cells in the table had the default formatting applied.

  1. Location priority

    Formatting for any given cell depends on its location in the table. The format_for method takes a location to which its formatting directive are restricted as the first argument. It can be one of the following:

-   **`:header`:** directive apply only to the header row, that is the first row, of
    the output table,

-   **`:footer`:** directives apply to all the footer rows of the output table,
    regardless of how many there are,

-   **`gfooter`:** directives apply to all group footer rows of the output tables,
    regardless of how many there are,

-   **`:body`:** directives apply to all rows in the body of the table unless the
    row is the first row in the table or in a group and separate directives for
    those have been given, in which case those directives apply,

-   **`:gfirst`:** directives apply to the first row in each group in the body of
    the table, unless the row is also the first row in the table as a whole, in
    which case the `:bfirst` directives apply,

-   **`:bfirst`:** directives apply to the first row in the body of the table.

If you give directives for `:body`, they are copied to `:bfirst` and `:gfirst`
as well and can be overridden by directives for those locations.

Directives given to the `format` method apply the directives to all locations in
the table, but they can be overridden by more specific directives given in a
`format_for` directive.

  1. Type and Column priority

    A directive based on type applies to all columns having that type unless overridden by a directive specific to a named column; a directive based on a column name applies only to cells in that column.

    However, there is a twist. Since the end result of formatting is to convert all columns to strings, the formatting directives for the :string type applies to all columns. Likewise, since all columns may contain nils, the nil: type applies to nils in all columns regardless of the column’s type.

    require 'fat_table'
  tab_a.to_text do |f|
    f.format(string: 'R', id: '3.0C', salary: 'n[N/A]')
  end

+=====+=======+=====+============+========+============+
|  Id |  Name | Age |    Address | Salary |  Join Date |
+-----|-------|-----|------------|--------|------------+
| 001 |  Paul |  32 | California |  20000 | 2001-07-13 |
| 003 | Teddy |  23 |     Norway |  20000 | 2007-12-13 |
| 004 |  Mark |  25 |  Rich-Mond |  65000 | 2007-12-13 |
| 005 | David |  27 |      Texas |  85000 | 2007-12-13 |
| 002 | Allen |  25 |      Texas |    N/A | 2005-07-13 |
| 008 |  Paul |  24 |    Houston |  20000 | 2005-07-13 |
| 009 | James |  44 |     Norway |   5000 | 2005-07-13 |
| 010 | James |  45 |      Texas |   5000 |            |
+=====+=======+=====+============+========+============+

    The string: 'R' directive causes all the cells to be right-aligned except :id which specifies centering for the :id column only. The n[N/A] directive for specifies how nil are displayed in the numeric column, :salary, but not for other nils, such as in the last row of the :join_date column.

Development

After checking out the repo, run `bin/setup` to install dependencies. Then, run `rake spec` to run the tests. You can also run `bin/console` for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run `bundle exec rake install`. To release a new version, update the version number in `version.rb`, and then run `bundle exec rake release`, which will create a git tag for the version, push git commits and tags, and push the `.gem` file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/ddoherty03/fat_table.