require 'fat_table'
"Current version is: #{FatTable::VERSION}"
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 a 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 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.
- Version
- Introduction
- Installation
- Usage
- Quick Start
- A Word About the Examples
- Anatomy of a Table
- Constructing Tables
- Accessing Parts of Tables
- Operations on Tables
- Formatting Tables
- Development
- Contributing
Add this line to your application’s Gemfile:
gem 'fat_table'Or, something like this in your gemspec file:
gem.add_runtime_dependency 'fat_table'And then execute:
$ bundleOr install it yourself as:
$ gem install fat_tableSomewhere in your code, make sure that FatTable is required:
require 'fat_table'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
FatTable. See the detailed explanations further on down.
Here is a set of data that records some kind of stock activity. It’s an array of arrays with the first inner array being the headings.
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']]Use FatTable to read the data and convert in into a table object. Note that the headings within the table are all converted to symbols, lower-cased and any spaces replaced with underscores.
Below, we select only those rows having more than 2000 shares, sort by a compund key, select all columns but add a column, :ref, for the row number, and finally re-order the columns with a final select.
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)You can use the resulting table in other operations, such as performing joins or set operations with other tables, etc. The world’s your oyster. But eventually you will want to present the table in some format, and that is where the formatting methods come in. They let you add footers, including groups footers, as well as styling the various elements with very simple formatting directives that can apply to various “locations” in the table. Any formatting directives that are beyond the capabilities of the output medium are simply ignored.
We can format the table constructed above.
table.to_text do |fmt|
# Add a group footer at the bottom of each group that results from sorting
# with the order_by method.
fmt.gfooter('Avg', shares: :avg, price: :avg)
# Add some table footers. Averages for the price and shares columns. The
# avg_footer method applies the avg aggregate to all the named columns with
# an "Average" label.
fmt.avg_footer(:price, :shares)
# And a second footer that shows the sum for the shares column.
fmt.sum_footer(:shares)
# Formats for all locations, :ref column is centered and bold, all numerics
# are right-aligned, and all booleans are centered and printed with 'Y' or
# 'N'
fmt.format(ref: 'CB', numeric: 'R', boolean: 'CY')
# Formats for different "locations" in the table:
# The headers are all centered and bold.
fmt.format_for(:header, string: 'CB')
# In the body rows (i.e., not the headers or footers), the code column is
# centered, shares have grouping commas applied and are rounded to one
# decimal place, but the price column is rounded to 4 places with no
# grouping commas.
fmt.format_for(:body, code: 'C', shares: ',0.1', price: '0.4', )
# But the price column in the first row of the body (:bfirst location) will
# also be formatted with a currency symbol.
fmt.format_for(:bfirst, price: '$0.4', )
# In the footers, apply the same rounding rules, but make the results bold.
fmt.format_for(:gfooter, shares: 'B,0.1', price: 'B0.4', )
fmt.format_for(:footer, shares: 'B,0.1', price: '$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 | | | +=========+============+======+=============+==========+====+
For the text format above, we were wasting our breath specifying bold styling
since there is no way to make that happen in plain ASCII text. But with
LaTeX, bold is doable. The output of the following code block is being
written to a file examples/quicktable.tex which is then \included-ed in a
simple wrapper file, examples/quick.tex so it can be compiled by LaTeX.
table.to_latex do |fmt|
fmt.gfooter('Avg', shares: :avg, price: :avg)
fmt.avg_footer(:price, :shares)
fmt.sum_footer(:shares)
fmt.format(ref: 'CB', numeric: 'R', boolean: 'CY')
fmt.format_for(:header, string: 'CB')
fmt.format_for(:body, code: 'C', shares: ',0.1c[blue.lightgray]', price: '0.4', )
fmt.format_for(:bfirst, price: '$0.4', )
fmt.format_for(:gfooter, shares: 'B,0.1', price: 'B0.4', )
fmt.format_for(:footer, shares: 'B,0.1', price: '$B0.4', )
end[[file:examples/quicktable.tex]]
These commands run pdflatex on the result twice to get the table aligned properly.
cd examples
pdflatex quick.tex
pdflatex quick.texAnd we convert the PDF into a smaller image for display:
cd examples
pdftoppm -png quick.pdf >quick.png
convert quick.png -resize 600x800 quick_small.png
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 as shown
in this README.
The examples in this README file are executed in Emacs org-mode as code
blocks within the README.org file, so they typically end with a call to
.to_aoa. That causes Emacs to insert the “Array of Array” ruby data
structure into the file and format it as a table, which is the convention for
Emacs org-mode. 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)>
If you use puts table.to_term, you can see the effect of the color formatting
directives.
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:
- Boolean (for holding ruby
TrueClassandFalseClassobjects), - DateTime (for holding ruby
DateTimeorDateobjects), - Numeric (for holding ruby
Integer,Rational, orBigDecimalobjects), - String (for ruby
Stringobjects), or - NilClass (for the undetermined column type).
By default, 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 is added to a Column and it can be
converted into one of the permissible types, 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 IncompatibleTypeError exception.
All of the table constructors allow you to set the type for a column in advance by adding keyword arguments to the end of the contructor arguments where the keyword is a header symbol and the value is a string designating one of the types. For example, suppose we are constructing a table from a CSV file, and we know that one of the columns is labeled ‘Start’ and another ‘Price’. We want to require the items in the ‘Start’ column to be a valid date and the items in the ‘Price’ column to be valid numbers:
FatTable.from_csv_file('data.csv', start: 'date', price: 'num')
The type string can be anything that starts with ‘dat’, ‘num’, ‘boo’, or
‘str’, regardless of case, to designate DateTime, Numeric, Boolean, or
String types, respectively. Any other string keeps the type as NilClass,
that is, it remains open for automatic typing.
The strictness of requiring all items to be of the same type can be relaxed by declaring a column to be “tolerant.” You can do so by adding a ‘~’ to the end of a keyword type specifier in the table constructor. In the above example, if we wanted to allow strings to be mixed up with the numeric prices, we would use the following:
FatTable.from_csv_file('data.csv', start: 'date', price: 'num~')
If a Column is tolerant, FatTable tries to convert new items into the
column’s specified type, or if the type is still open, to one of DateTime,
Numeric, or Boolean and then fixing the column’s type, or, if it cannot do
so converts the item into a String but does not raise an
IncompatibleTypeError exception. These interloper strings are treated like
nils for purposes of sorting and evaluation, but are displayed according to
any string formatting on output. See Designating “Tolerant” Columns below.
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, ory, regardless of case, are interpreted asTrueClassand the strings,f,false,no, orn, regardless of case, are interpreted asFalseClass, in either case resulting in a Boolean column. Empty strings in a column already having a Boolean type are converted tonil. - DateTime
- Strings that contain patterns of
yyyy-mm-ddoryyyy/mm/ddormm-dd-yyyormm/dd/yyyyor any of the foregoing with an addedThh:mm:ssorThh:mmwill be interpreted as aDateTimeor aDate(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 byDateTime.parse. Org mode timestamps (any of the foregoing surrounded by square[]or pointy<>brackets), active or inactive, are valid input strings forDateTimecolumns. Empty strings in a column already having theDateTimetype are converted tonil. - Numeric
- All commas (
,) underscores (_) and ($) dollar signs (or other currency symbol as set byFatTable.currency_symbolare removed from the string and if the remaining string can be interpreted as aNumeric, it will be. It is interpreted as anIntegerif there are no decimal places in the remaining string, as aRationalif the string has the form<number>:<number>or<number>/<number>, or as aBigDecimalif 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,
FatTableapplies#to_sto the input value and, treats it as an item of typeString. Empty strings in a column already having theStringtype 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
nilswill retain theNilClasstype.
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.
You should avoid the use of the column names :omni and :sort_key because
they have special meanings in the select and order_with commands,
respectively.
The rows of a FatTable table can be 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 rows in
each group.
You can create an empty table with FatTable::Table.new or, the shorter form,
FatTable.new, and then add rows with the << operator and a Hash. The keys
in the added rows determine the names of the headers:
require 'fat_table'
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 }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.
tab.to_text+======+======+============+===+===+ | A | B | C | D | E | +------+------+------------+---+---+ | 1 | 2 | 2017-01-21 | F | | | 3.14 | 2.17 | 2016-01-21 | T | | +======+======+============+===+===+
You can continue to add rows to the table:
tab << { 'F' => '335:113', a: Rational(3, 5) }This last << operation adds a new column headed :f to the table and makes
the value of :f in all prior rows nil. Also, the values for the new row
for which no key was give are assigned nil as well:
tab.to_text+======+======+============+===+===+=========+ | A | B | C | D | E | F | +------+------+------------+---+---+---------+ | 1 | 2 | 2017-01-21 | F | | | | 3.14 | 2.17 | 2016-01-21 | T | | | +------+------+------------+---+---+---------+ | 3/5 | | | | | 335/113 | +======+======+============+===+===+=========+
Alternatively, you can specify the headers at the outset, in which case, headers in added rows that do not match any of the initial headers cause new columns to be created:
require 'fat_table'
tab = FatTable.new(:a, 'b', 'C', :d)
tab.headers[:a, :b, :c, :d]
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_text+======+======+============+===+===+ | A | B | C | D | E | +------+------+------------+---+---+ | 1 | 2 | 2017-01-21 | F | | | 3.14 | 2.17 | 2016-01-21 | T | | +------+------+------------+---+---+ | 1 | 2 | 2017-01-21 | F | | | 3.14 | 2.17 | 2016-01-21 | T | | +======+======+============+===+===+
Occasionally, FatTable’s automatic type detection can get in the way and you
just want it to treat one or more columns as Strings regardless of their
appearance. Think, for example, of zip codes. As mentioned above, when a
table is contructed, you can designate a ‘String’ type for a column by
using a keyword parameter.
require 'fat_table'
tab = FatTable.new(:a, 'b!', 'C', :d, :zip, zip: 'str')
tab << { a: 1, b: 2, c: "<2017-01-21>", d: 'f', e: '', zip: 18552 }
tab << { a: 3.14, b: 2.17, c: '[2016-01-21 Thu]', d: 'Y', e: nil }
tab << { zip: '01879--7884' }
tab << { zip: '66210', b: 'Not a Number' }
tab << { zip: '90210' }
tab.to_text+===+===+============+===+=============+===+ | A | B | C | D | Zip | E | +---+---+------------+---+-------------+---+ | 1 | 2 | 2017-01-21 | F | 18552 | | | 3 | 2 | 2016-01-21 | T | | | | | | | | 01879--7884 | | | | | | | 90210 | | | | | | | | | +===+===+============+===+=============+===+
In addition, at any time after creating a table, you can force the String type
on any number of columns with the force_string! method. When you do so, all
exisiting items in the column are converted to strings with the #to_s method.
tab = FatTable.new(:a, 'b', 'C', :d, :zip)
tab << { a: 1, b: 2, c: "<2017-01-21>", d: 'f', e: '', zip: 18552 }
tab << { a: 3.14, b: 2.17, c: '[2016-01-21 Thu]', d: 'Y', e: nil }
tab.force_string!(:zip, :c)
tab << { zip: '01879' }
tab << { zip: '66210' }
tab << { zip: '90210' }
tab.to_text+======+======+============+===+=======+===+ | A | B | C | D | Zip | E | +------+------+------------+---+-------+---+ | 1 | 2 | 2017-01-21 | F | 18552 | | | 3.14 | 2.17 | 2016-01-21 | T | | | | | | | | 01879 | | | | | | | 66210 | | | | | | | 90210 | | +======+======+============+===+=======+===+
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 table 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)You can also initialize a table directly from ruby data structures. You can, for example, build a table from an array of arrays. Remember that you can make any column tolerant with a keyword argument for the column symbol and ending it with a ‘~’.
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).to_aoa| Ref | Date | Code | Raw | Shares | Price | Info | Bool |
|---|---|---|---|---|---|---|---|
| 1 | 2013-05-02 | P | 795546 | 795546 | 1 | ENTITY1 | T |
| 2 | 2013-05-02 | P | 118186 | 118186 | 12 | ENTITY1 | T |
| 7 | 2013-05-20 | S | 12000 | 5046 | 28 | ENTITY3 | F |
| 8 | 2013-05-20 | S | 85000 | 35743 | 28 | ENTITY3 | T |
| 9 | 2013-05-20 | S | 33302 | 14003 | 29 | ENTITY3 | T |
| 10 | 2013-05-23 | S | 8000 | 3364 | 27 | ENTITY3 | T |
| 11 | 2013-05-23 | S | 23054 | 9694 | 27 | ENTITY3 | F |
| 12 | 2013-05-23 | S | 39906 | 16780 | 25 | ENTITY3 | T |
| 13 | 2013-05-29 | S | 13459 | 5660 | 25 | ENTITY3 | T |
| 14 | 2013-05-29 | S | 15700 | 6602 | 25 | ENTITY3 | F |
| 15 | 2013-05-29 | S | 15900 | 6686 | 25 | ENTITY3 | T |
| 16 | 2013-05-30 | S | 6679 | 2809 | 25 | ENTITY3 | T |
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:
- The named org-mode table,
trades1, can be passed into a ruby code block using the:var tab=trades1header argument to the code block; that makes the variabletabavailable to the code block as an array of arrays, whichFatTablethen uses to initialize the table. - The code block requires that you set
:colnames noin the header arguments. This suppresses org-mode’s own processing of the header line so thatFatTablecan see the headers. Failure to do this will cause an error. - 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.
FatTablepasses back to org-mode an array of arrays using the.to_aoamethod. In anorg-modebuffer, these are rendered as tables. We’ll often apply.to_aoaat the end of example blocks in thisREADMEto render the results as a table inside this file. As we’ll see below,.to_aoacan also take a block to which formatting and footer directives can be attached.
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 taken from the keys of the hashes. Accordingly, all
the hashes must 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 be parsed into one of the permissible column types.
Another way to initialize a FatTable table is with the results of a SQL
query. Before you can connect to a database, you need to make sure that the required
adapter for your database is installed. FatTable uses the sequel gem
under the hood, so any database that it supports can be used. For example, if
you are accessing a Postgres database, you must install the pg gem with
$ gem install pgYou must first set the database parameters to be used for the queries.
# This automatically requires sequel.
FatTable.connect(adapter: 'sqlite',
database: 'examples/trades.db')
tab = FatTable.from_sql('select * from trans;').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 | +============+======+==========+==========+=========+=========+====+
The arguments to connect are simply passed on to sequel’s connect method, so
any set of arguments that work for it should work for connect. Alternatively,
you can build the Sequel connection directly with Sequel.connect or with
adapter-specific Sequel connection methods and let FatTable know to use that
connection:
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
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.
At any point, you can add a boundary to a table by invokong the
mark_boundary method. Without an argument, it adds the boundary to the end
of the table; with a numeric argument, n, it adds the boundary after row
n.
FatTable tables has a concept of “groups” of rows that play a role in many of
the methods for operating on them as explained below.
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.
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.
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.
The two forms of indexing can be combined, in either order, 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.Here is a quick rundown of other table attributes that you can access:
tab.headers # => an Array of the headers in symbol form
tab.types # => a Hash mapping headers to column types
tab.type(head) # => return the type of the column for the given head
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) # => return the FatTable::Column object for the given head
tab.column?(head) # => does the table have a column with the given head?
tab.groups # => return an Array of the table's groups as Arrays of row Hashes.You should note that what the .types and .type(head) methods return is a
string naming the “type” assigned by FatTable. All of them are also the
names of Ruby classes except to ‘Boolean’ a class that doesn’t exist in Ruby.
The value true is a member of the TrueClass and false a member of the
FalseClass. So for FatTable to provide a column of type ‘Boolean’
requires it to synthesize the type from these Ruby classes.
tab.types{:a=>"Numeric", :b=>"Numeric", :c=>"DateTime", :d=>"Boolean", :e=>"NilClass", :f=>"Numeric"}
puts "Column :d says its type is '#{tab.type(:d)}' and that is a #{tab.type(:d).class}"Column :d says its type is 'Boolean' and that is a String
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 exceptions are the
degroup! operation, which mutates the receiver table by removing its group
boundaries, and force_string! (explained above at Forcing String Type),
which forces columns to have the String type despite what the automatic typing
rules determine.
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
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.
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.
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 |
EOSRendering tab1 into Emacs org-mode:
tab1 = FatTable.from_org_string(tab1_str)Rendering tab2 into Emacs org-mode:
tab2 = FatTable.from_org_string(tab2_str)With the select method, you can select columns to appear in the output
table, rearrange their order, and create new columns that are a function of
other 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 |
It can be tedious to type the names of all the columns in a select
statement, so FatTable recognizes the special column name :omni. If the
select’s first and only column argument is :omni, it will expand to the
names of all the existing columns in the table. Use of :omni otherwise is
not interpreted specially, so you will get an error complaining about a
non-existent column unless you happen to have a column named :omni in your
table, which is not advisable. You can add hash arguments after :omni but
you cannot add additional column names:
tab1.select(:omni, cost: 'shares * price').to_aoa| Ref | Date | Code | Price | G10 | QP10 | Shares | Lp | Qp | Iplp | Ipqp | Cost | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T001 | 2016-11-01 | P | 7.7 | T | F | 100 | 14 | 86 | 0.2453 | 0.1924 | 770.0 | | T002 | 2016-11-01 | P | 7.75 | T | F | 200 | 28 | 172 | 0.2453 | 0.1924 | 1550.0 | | T003 | 2016-11-01 | P | 7.5 | F | T | 800 | 112 | 688 | 0.2453 | 0.1924 | 6000.0 | | T003 | 2016-11-01 | P | 7.5 | F | T | 800 | 112 | 688 | 0.2453 | 0.1924 | 6000.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T004 | 2016-11-01 | S | 7.55 | T | F | 6811 | 966 | 5845 | 0.2453 | 0.1924 | 51423.05 | | T005 | 2016-11-01 | S | 7.5 | F | F | 4000 | 572 | 3428 | 0.2453 | 0.1924 | 30000.0 | | T006 | 2016-11-01 | S | 7.6 | F | T | 1000 | 143 | 857 | 0.2453 | 0.1924 | 7600.0 | | T006 | 2016-11-01 | S | 7.6 | F | T | 1000 | 143 | 857 | 0.2453 | 0.1924 | 7600.0 | | T007 | 2016-11-01 | S | 7.65 | T | F | 200 | 28 | 172 | 0.2453 | 0.1924 | 1530.0 | | T008 | 2016-11-01 | P | 7.65 | F | F | 2771 | 393 | 2378 | 0.2453 | 0.1924 | 21198.15 | | T009 | 2016-11-01 | P | 7.6 | F | F | 9550 | 1363 | 8187 | 0.2453 | 0.1924 | 72580.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T010 | 2016-11-01 | P | 7.55 | F | T | 3175 | 451 | 2724 | 0.2453 | 0.1924 | 23971.25 | | T011 | 2016-11-02 | P | 7.425 | T | F | 100 | 14 | 86 | 0.2453 | 0.1924 | 742.5 | | T012 | 2016-11-02 | P | 7.55 | F | F | 4700 | 677 | 4023 | 0.2453 | 0.1924 | 35485.0 | | T012 | 2016-11-02 | P | 7.55 | F | F | 4700 | 677 | 4023 | 0.2453 | 0.1924 | 35485.0 | | T013 | 2016-11-02 | P | 7.35 | T | T | 53100 | 7656 | 45444 | 0.2453 | 0.1924 | 390285.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T014 | 2016-11-02 | P | 7.45 | F | T | 5847 | 835 | 5012 | 0.2453 | 0.1924 | 43560.15 | | T015 | 2016-11-02 | P | 7.75 | F | F | 500 | 72 | 428 | 0.2453 | 0.1924 | 3875.0 | | T016 | 2016-11-02 | P | 8.25 | T | T | 100 | 14 | 86 | 0.2453 | 0.1924 | 825.0 |
After the list of selected column names in the call to select, you can add
any number of hash-like arguments. You can use these to add a copy of an
existing column. By calling select again, you can include only the copied
column, in effect renaming it. For example, if you want tab1 but with :ref
changed to :id, just add an argument to define the new :id column:
tab1.select(:omni, id: :ref).
select(:id, :date, :code, :price, :shares).to_aoa| Id | Date | Code | Price | Shares | |------+------------+------+-------+--------| | T001 | 2016-11-01 | P | 7.7 | 100 | | T002 | 2016-11-01 | P | 7.75 | 200 | | T003 | 2016-11-01 | P | 7.5 | 800 | | T003 | 2016-11-01 | P | 7.5 | 800 | |------+------------+------+-------+--------| | T004 | 2016-11-01 | S | 7.55 | 6811 | | T005 | 2016-11-01 | S | 7.5 | 4000 | | T006 | 2016-11-01 | S | 7.6 | 1000 | | T006 | 2016-11-01 | S | 7.6 | 1000 | | T007 | 2016-11-01 | S | 7.65 | 200 | | T008 | 2016-11-01 | P | 7.65 | 2771 | | T009 | 2016-11-01 | P | 7.6 | 9550 | |------+------------+------+-------+--------| | T010 | 2016-11-01 | P | 7.55 | 3175 | | T011 | 2016-11-02 | P | 7.425 | 100 | | T012 | 2016-11-02 | P | 7.55 | 4700 | | T012 | 2016-11-02 | P | 7.55 | 4700 | | T013 | 2016-11-02 | P | 7.35 | 53100 | |------+------------+------+-------+--------| | T014 | 2016-11-02 | P | 7.45 | 5847 | | T015 | 2016-11-02 | P | 7.75 | 500 | | T016 | 2016-11-02 | P | 8.25 | 100 |
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 can be 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. In addition the instance variables
‘@row’ and ‘@group’ are available as the row number and group number of the
new value. 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 number respectively.
For example, if we want to rename the traded_on column to :date 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 |
Because select’s hash-like parameters evaluate a string as a ruby
expression, as just described, it must provide a way to set a new column to a
string literal. To indicate that a string should be inserted literally, add a
: as the first non-blank character in the string. This will supress
evaluation and insert the remainder of the string in the named column.
tab1.select(:ref, :price, :shares, traded_on: :date, cost: ':the price of freedom').
select(:ref, :traded_on, :price, :shares, :cost).to_aoaThis sets the :cost column to the string constant ‘the price of freedom’ for
the whole table.
You can set a column to a constant of any of the acceptable types, Numeric,
Date, DateTime, true, false, or nil.
tab1.select(:ref, :price, :shares, traded_on: :date, cost: Math::PI, today: Date.today).
select(:ref, :traded_on, :price, :shares, :cost, :today).to_aoaAs 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 |
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.
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 removes 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 |
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. Ordering is done is ascending order for each of the
columns, but can be reversed by adding a ‘!’ to the end a symbol argument.
All columns of the input table are included in the output.
Let’s sort our table first by :code, then in reverse order of :date.
tab1.order_by(:code, :date!) \
.to_aoa| Ref | Date | Code | Price | G10 | QP10 | Shares | Lp | Qp | Iplp | Ipqp | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------| | 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 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------| | 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 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------| | 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.
The order_with method is a convenient combination of select and
order_by. It takes a single string expression as an argument to serve as a
sort key—one that would be valid as a select expression—but with an
optional trailing ! to indicate reverse sort. The resulting table has an
additional column called :sort_key with the expression evaluated for each
row, and the table is sorted as with order_by on that column.
tab1.order_with('price * shares').to_aoa| Ref | Date | Code | Price | G10 | QP10 | Shares | Lp | Qp | Iplp | Ipqp | Sort Key | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T011 | 2016-11-02 | P | 7.425 | T | F | 100 | 14 | 86 | 0.2453 | 0.1924 | 742.5 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T001 | 2016-11-01 | P | 7.7 | T | F | 100 | 14 | 86 | 0.2453 | 0.1924 | 770.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T016 | 2016-11-02 | P | 8.25 | T | T | 100 | 14 | 86 | 0.2453 | 0.1924 | 825.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T007 | 2016-11-01 | S | 7.65 | T | F | 200 | 28 | 172 | 0.2453 | 0.1924 | 1530.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T002 | 2016-11-01 | P | 7.75 | T | F | 200 | 28 | 172 | 0.2453 | 0.1924 | 1550.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T015 | 2016-11-02 | P | 7.75 | F | F | 500 | 72 | 428 | 0.2453 | 0.1924 | 3875.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T003 | 2016-11-01 | P | 7.5 | F | T | 800 | 112 | 688 | 0.2453 | 0.1924 | 6000.0 | | T003 | 2016-11-01 | P | 7.5 | F | T | 800 | 112 | 688 | 0.2453 | 0.1924 | 6000.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T006 | 2016-11-01 | S | 7.6 | F | T | 1000 | 143 | 857 | 0.2453 | 0.1924 | 7600.0 | | T006 | 2016-11-01 | S | 7.6 | F | T | 1000 | 143 | 857 | 0.2453 | 0.1924 | 7600.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T008 | 2016-11-01 | P | 7.65 | F | F | 2771 | 393 | 2378 | 0.2453 | 0.1924 | 21198.15 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T010 | 2016-11-01 | P | 7.55 | F | T | 3175 | 451 | 2724 | 0.2453 | 0.1924 | 23971.25 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T005 | 2016-11-01 | S | 7.5 | F | F | 4000 | 572 | 3428 | 0.2453 | 0.1924 | 30000.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T012 | 2016-11-02 | P | 7.55 | F | F | 4700 | 677 | 4023 | 0.2453 | 0.1924 | 35485.0 | | T012 | 2016-11-02 | P | 7.55 | F | F | 4700 | 677 | 4023 | 0.2453 | 0.1924 | 35485.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T014 | 2016-11-02 | P | 7.45 | F | T | 5847 | 835 | 5012 | 0.2453 | 0.1924 | 43560.15 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T004 | 2016-11-01 | S | 7.55 | T | F | 6811 | 966 | 5845 | 0.2453 | 0.1924 | 51423.05 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T009 | 2016-11-01 | P | 7.6 | F | F | 9550 | 1363 | 8187 | 0.2453 | 0.1924 | 72580.0 | |------+------------+------+-------+-----+------+--------+------+-------+--------+--------+----------| | T013 | 2016-11-02 | P | 7.35 | T | T | 53100 | 7656 | 45444 | 0.2453 | 0.1924 | 390285.0 |
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 :all? aggregate is
applied to one of the boolean fields, that is, it is true if any of the values
in that column are true.
Note that 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
Numericcolumns, apply ‘+’ to all the non-nil values; forStringcolumns, join the elements with a single space, count- the number of non-nil values in the column,
min- for
Numeric,String, andDateTimecolumns, return the smallest non-nil, non-blank value in the column, max- for
Numeric,String, andDateTimecolumns, return the largest non-nil, non-blank value in the column, avg- for
NumericandDateTimecolumns, return the arithmetic mean of the non-nil values in the column; with respect toDateorDateTimeobjects, each is converted to a numeric Julian date, the average is calculated, and the result converted back to aDateorDateTimeobject, var- for
NumericandDateTimecolumns, compute the sample variance of the non-nil values in the column, dates are converted to Julian date numbers as for the:avgaggregate, pvar- for
NumericandDateTimecolumns, compute the population variance of the non-nil values in the column, dates are converted to Julian date numbers as for the:avgaggregate, dev- for
NumericandDateTimecolumns, compute the sample standard deviation of the non-nil values in the column, dates are converted to Julian date numbers as for the:avgaggregate, pdev- for
NumericandDateTimecolumns, compute the population standard deviation of the non-nil values in the column, dates are converted to numbers as for the:avgaggregate, all?- for
Booleancolumns only, return true if all of the non-nil values in the column are true, any?- for
Booleancolumns only, return true if any non-nil value in the column is true, none?- for
Booleancolumns only, return true if no non-nil value in the column is true, one?- for
Booleancolumns 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.
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 “inner join” on the tables. For each row
R1ofT1, the joined table has a row for each row inT2that satisfies the join condition withR1. left_join(other, *jexps)- First, an inner join is performed. Then, for
each row in
T1that does not satisfy the join condition with any row inT2, a joined row is added with null values in columns ofT2. Thus, the joined table always has at least one row for each row inT1. right_join(other, *jexps)- First, an inner join is performed. Then, for
each row in
T2that does not satisfy the join condition with any row inT1, a joined row is added with null values in columns ofT1. This is the converse of a left join: the result table will always have a row for each row inT2. full_join(other, *jexps)- First, an inner join is performed. Then, for
each row in
T1that does not satisfy the join condition with any row inT2, a joined row is added with null values in columns ofT2. Also, for each row ofT2that does not satisfy the join condition with any row inT1, a joined row with null values in the columns ofT1is added. cross_join(other)- For every possible combination of rows from
T1andT2(i.e., a Cartesian product), the joined table will contain a row consisting of all columns inT1followed by all columns inT2. If the tables haveNandMrows respectively, the joined table will haveN * Mrows.
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.
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:
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 |
EOSHere is tab_a:
tab_a = FatTable.from_org_string(tab_a_str)
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 tab_b:
tab_b = FatTable.from_org_string(tab_b_str)
tab_b.to_aoa| Id | Dept | Emp Id | |----+-------------+--------| | 1 | IT Billing | 1 | | 2 | Engineering | 2 | | 3 | Finance | 7 |
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 |
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 |
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 |
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.
So be careful lest you consume all your computer’s memory.
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 |
FatTable can perform several set operations on pairs of 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 |
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 |
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 |
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 |
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 |
Finally, it is sometimes helpful to remove any group boundaries from a table.
You can do this with .degroup!, which, together with force_string!, are
the only operations that mutate their receiver tables.
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 |
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.
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 output
formats can be defined by adding additional classes.
This formatter uses nothing but ASCII characters to draw the table. Notice
that, unlike to to_org formatter shown below, the intersections of lines are
represented by a + character. Embelishments such as color, bold, and so
forth are ignored.
tab_a.to_text+====+=======+=====+============+========+============+ | 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 | | +====+=======+=====+============+========+============+
This formatter is designed to format tables in a manner consistent with the
way tables are drawn within Emacs Org Mode. It also uses nothing by ASCII
characters to draw the table, but, the intersections of lines are represented
by a | character. Embelishments such as color, bold, and so forth are
ignored. When working in Org Mode, note that Emacs will convert an Array of
Arrays into an Org Mode table, so when constructing tables programmatically,
it may be better to use the to_aoa formatter shown below.
tab_a.to_org|----+-------+-----+------------+--------+--------------| | 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 | | |----+-------+-----+------------+--------+--------------|
When outputting to a terminal or other device that can interpret ANSI characters and escape codes, you can use this formatter to get a prettier table. It also allows embelishments such as color and text styles to the extent the device supports it.
tab_a.to_term╒════╤═══════╤═════╤════════════╤════════╤════════════╕ │ 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 │ │ ╘════╧═══════╧═════╧════════════╧════════╧════════════╛
This formatter outputs a table in the form suitable for inclusion in a LaTeX
document using the logtable package. Natualy it allows embelishments such
as color and text styles to the full extent of LaTeX’s formatting prowess.
tab_b.to_latex\begin{longtable}{lll}
Id&
Dept&
Emp Id\\
\endhead
1&
IT Billing&
1\\
2&
Engineering&
2\\
3&
Finance&
7\\
\end{longtable}
tab_b.to_aoa[["Id", "Dept", "Emp Id"], nil, ["1", "IT Billing", "1"], ["2", "Engineering", "2"], ["3", "Finance", "7"]]
tab_b.to_aoh[{:id=>"1", :dept=>"IT Billing", :emp_id=>"1"}, {:id=>"2", :dept=>"Engineering", :emp_id=>"2"},
{:id=>"3", :dept=>"Finance", :emp_id=>"7"}]
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:
For a string element, or any an element of any type (since these are applied after the element has been converted to a String), the following instructions are valid.
- u
- convert the element to all lowercase [default false],
- U
- convert the element to all uppercase [default false],
- t
- title case the element, that is, upcase the initial letter in each word and lower case the other letters [default false],
- B ~B
- make the element bold, or turn off bold [default ~B]
- I ~I
- make the element italic, or turn off italic [default ~I]
- R
- align the element on the right of the column [default off]
- L
- align the element on the left of the column [default on]
- C
- align the element in the center of the column [default off]
- c[<color_spec>]
- render the element in the given color; the <color_spec> 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, ‘none’, which keeps the output’s default color [default none].
- _ ~_
- underline the element, or turn off underline [default off]
- * ~*
- cause the element to blink, or turn off blink [default off]
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.
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 [default ~,],
- $ ~$
- format the number as currency according to the locale, or not [default ~$],
- 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. If n is negative, the value will be rounded to the left of the decimal point: e.g., if n is -2, the number will be rounded to the nearest hundred, if -3, to the nearest thousand, etc. [default 0.0]
- H
- convert the number (assumed to be in units of seconds) to
HH:MM:SS.ssform. 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. If this directive is included, all other numeric directives will be ignored. [default off]
For example, the directive ‘R5.0c[blue]’ would right-align the numeric element, pad it on the left with zeros, and color it blue.
For a DateTime, all the instructions valid for string are available, in
addition to the following:
- d[fmt]
- apply the format to a
Dateor aDateTimethat is a whole day, that is that has no or zero hour, minute, and second components, where fmt is a valid format string forDate#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 ‘Sep 22, 1957’, center it, and color it pink.
For a boolean cell, all the instructions valid for string are available, in addition to the following:
- Y
- print true as
Yand false asN, - T
- print true as
Tand false asF[this is the default], - X
- print true as
Xand false as an empty string ”, - b[xxx,yyy]
- print true as the string given as
xxxand false as the string given asyyy, - c[tcolor,fcolor]
- color a true element with
tcolorand a false element withfcolor. Each of the colors may be specified in the same manner as colors for strings described above.
For example, the directive ‘b[Yeppers,Nope]c[green.pink,red.pink]’ would
render a true boolean as Yeppers colored green on pink and render a false
boolean as Nope colored red on pink. See Yeppers for additional information.
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
nilitem with the given niltext [default ”].
For example, you might want to use ‘n[-]Cc[purple]’ to make nils visible as a centered purple hyphen.
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. See below at Table Locations for an explanation of all the
locations available.
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. If a key represents neither a column name nor a valid
type, it is silently ignored. 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_text do |f|
# Note: blat: is silently ignored
f.format(numeric: '0.0,R', id: '3.0C', blat: 'B')
f.format_for(:body, string: 'R')
f.format_for(:header, string: 'C')
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 | | +=====+=======+=====+============+========+============+
In the example, the format method affects the whole table. Its numeric:
directive affected the :age and :salary columns because their types are
Numeric. The id: column is also Numeric, but it’s more specific directive
takes precedence and it is formatted accordingly.
But the format_for methods affected two “locations”: the “body” and the
“header”. Within the body, the :string directive calls for all strings to
be right-aligned, but the headers are unaffected by it. The format_for the
:header location caused all the headers to be centered.
All the other cells in the table, namely the cells in the :join_date column,
had the default formatting applied.
In the format_for formatting method, the first argument names a “location.”
The table is divided into several locations for which separate formatting
directives may be given. These locations are identified by 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 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- The directives apply only to the header row, that is the first row, of the output table; before the directives are applied, the header’s symbol form is converted back into a string and capitalized as is a book title. Thus, only directives applicable to the String type have any effect.
:body- The directives apply to all rows in the body of the table.
: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
:bfirstdirectives apply, :bfirst- The directives apply to the first row in the body of the table,
taking precedence over those directives that apply to the body generally or
the
:gfirstdirectives that apply to the first row in each group. :footer- The directives apply to all the footer rows of the output table, regardless of how many there are.
gfooter- The directives apply to all group footer rows of the output tables, regardless of how many there are.
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.
A directive based the column name overrides any directive based on type. If any cell has both a type-based formatting and column-based, the column instructions prevail. In earlier versions the instuctions were “merged” but that is no longer the case.
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 can
be applied to all column types. Likewise, since all columns may contain nils,
the NilClass: type applies to nils in all columns regardless of the column’s
type.
tab_a.to_text do |f|
f.format(string: 'R', id: '3.0C', nil: 'Cn[-]', 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.
You can call the foot, gfoot, footer, or gfooter, methods on
Formatter objects to add footers and group footers. Note that all of these
methods return a Footer object that can be accessed to extract the computed
values. All of these methods return the FatTable::Footer object so
constructed. It can be used to access the values and other attributes of the
footer computed. Their signatures are:
foot(label: label, label_col: nil, **agg_cols)- where
labelis a label to be placed in the column with headerlabel_col, or, if ommitted, in the first cell of the footer (unless that column is named as one of theagg_cols, in which case the label is ignored), and**agg_colsis zero or more hash-like parameters with a column symbol as a key and a valid aggregate as the value. This causes a table-wide header to be added at the bottom of the table applyingagg, to theagg_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. gfoot(label: 'Group Total', label_col: nil, **agg_cols)- where the
parameters have the same meaning as for the
footmethod, but results 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. footer(label, *sum_cols, **agg_cols)- where
labelis a label to be placed in the first cell of the footer (unless that column is named as one of thesum_colsoragg_cols, in which case the label is ignored),*sum_colsare zero or more symbols for columns to be summed, and**agg_colsis zero or more hash-like parameters with a column symbol as a key and a valid aggregate as the value. This causes a table-wide header to be added at the bottom of the table applying the:sumaggregate to thesum_colsand the named aggregate to theagg_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
footermethod, but results 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’.
Most of the time, you will want a fixed string as the label. However, especially in the case of a group footer, you might want a dynamically contructed label. You can use a proc or lambda for a label, and it will be computed for you. In the case of non-group footers, the proc takes a single parameter, the footer object itself. This allows you to make the label a function of other footer values, for example, you could make the label include the most recent year from the date column:
fmtr.foot(label: -> (f) { "Average (latest year #{f.column(:date).max.year})" },
temp: :avg)In the case of a group footer, the lambda or proc may take either one or qtwo parameters. If it takes one, the parameter is simply the 0-based number of the group:
fmtr.gfoot(label: -> (k) { "Group #{(k+1).to_roman} Average" }, temp: :avg)This would format the label with a roman numeral (assuming you defined a method to do so) for the group number.
If it takes two arguments, the second argument is the footer itself, as with non-group footers:
fmtr.gfoot(label: -> (k, f) { "Year #{f.column(:date, k).max.year} Group #{(k+1).to_roman} Average" },
temp: :avg)This would add the group’s year to label, assuming the :date column of the footer’s table had the same year for each item in the group.
When adding a footer with the above methods, you can specify an aggregator for
each column named in the agg_cols parameter. There are several candidates
for what you can use for an aggregator:
- Symbol
- one of the following built-in aggregators: :first, :last, :range,
:sum, :count, :min, :max, :avg, :var, :pvar, :dev, :pdev, :any?, :all?,
:none?, and :one?.
- The symbols ending in a question mark are valid only for boolean columns;
- :count, :first, and :last work with any column type,
- :min, :max, and :range work with all types except boolean;
- :sum, works only with numeric columns, and
- :avg, :var, :dev, :pvar, and :pdev work with numeric or datetime columns. In the case of datetime columns, these aggrgators convert the dates to julian date numbers, perform the calculation, then convert the result back to a datetime object. Apart from the built-in aggrgators, you could define your own by opening the FatTable::Column class and adding a suitable instance method. In that case, the symbol could also refer to the method you defined.
- String
- using a string as an aggrgegator can result in:
- the string being converted to an object matching the type of the column (for example, using ‘$1,888’ in a numeric column puts the constant number 1888 in the footer field, using ‘1957-09-22’ puts the fixed date in the field, etc.)
- if the string cannot be parsed as a valid object matching the column’s type, it is placed literally in the footer field (for example, using ‘(estimated)’ can be used to add additional information to the footer)
- Ruby object
- you can put a number in a numeric footer field, a DateTime object in a datetime footer field, or a true or false in a boolean footer field;
- A Lambda
- finally, you can provide a lambda for performing arbitrary
calculations and placing the result in the footer field. The number of
arguments the lambda takes can vary:
- If the lambda is used in an ordinary footer column, it can take 0, 1, or 2 arguments: (1) the first argument, if given, will be set to the FatTable::Column object for that column and (2) the second argument, if given, will be set to the Footer object itself.
- If the lambda is used in a group footer column, it can 0, 1, 2, or 3 arguments: (1) the first argument, if given, will be set to the group’s 0-based index number, (2) the second argument, if given, will be set to a FatTable::Column object consisting of those items in the group’s column, and (3) the third argument, if given, will be set to the Footer object itself.
Each of the methods for adding a footer to a Formatter returns a Footer object
that you can query for attributes of the generated footer, including accessing
their computed values. Here are the accessors available on a
FatTable::Footer object:
[h]- Return the value of under the
hheader, or if this is a group footer, return an array of the values for all the groups under thehheader. - .<header>
- like,
[h]but makes the values available in method-call form. number_of_groups- Return the total number of groups in the table to which this footer belongs. Note that if the table has both group footers and normal footers, this will return the number of groups even for a normal footer.
column(h),column(h, k)- Return a FatTable::Column object for the header h and, if the footer is a group footer, the kth group.
items(h),items(h, k)- Return an Array of the values for the header
hand, if a group, for the ~k~th group. to_h,to_h(k)- Return a Hash with a key for each column header mapped
to the footer value for that column, nil for unused columns. Use the index
kto specify which group to access in the case of a group footer.
As a reminder, here is the table, tab_a defined earlier:
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 | |
You can add a footer compute the average of the given columns. You may be
surprised that you can average a set of dates, but :avg simply converts the
dates to Julian numbers, averages that, then converts the result back to a
date.
tab_a.to_text do |f|
f.format(numeric: '0.0R,', datetime: 'd[%v]D[%v]')
f.footer('Average', age: :avg, salary: :avg, join_date: :avg)
f.footer('Tally', age: :count)
end+=========+=======+=====+============+========+=============+ | Id | Name | Age | Address | Salary | Join Date | +---------+-------+-----+------------+--------+-------------+ | 1 | Paul | 32 | California | 20,000 | 13-JUL-2001 | | 3 | Teddy | 23 | Norway | 20,000 | 13-DEC-2007 | | 4 | Mark | 25 | Rich-Mond | 65,000 | 13-DEC-2007 | | 5 | David | 27 | Texas | 85,000 | 13-DEC-2007 | | 2 | Allen | 25 | Texas | | 13-JUL-2005 | | 8 | Paul | 24 | Houston | 20,000 | 13-JUL-2005 | | 9 | James | 44 | Norway | 5,000 | 13-JUL-2005 | | 10 | James | 45 | Texas | 5,000 | | +---------+-------+-----+------------+--------+-------------+ | Average | | 31 | | 31,429 | 29-DEC-2005 | +---------+-------+-----+------------+--------+-------------+ | Tally | | 8 | | | | +=========+=======+=====+============+========+=============+
If the string is convertible into its columns’s type, it will be converted to that type; otherwise, it will be placed in the footer literally. This example also shows how the values from one footer might be used in composing another footer.
tab_a.to_text do |f|
f.format(numeric: '0.0R,', datetime: 'd[%v]D[%v]')
avg_ft = f.footer('Average', age: :avg, salary: :avg, join_date: :avg)
f.footer('Tally', age: :count)
if avg_ft[:salary] < 30000
cmt = "We're saving"
else
cmt = "We're overspending"
end
f.footer('Pay', join_date: "We have #{avg_ft.number_of_groups} grp")
f.footer('Group count', join_date: "We have #{avg_ft.number_of_groups} grp")
f.footer('Comment', join_date: cmt)
end+=============+=======+=====+============+========+====================+ | Id | Name | Age | Address | Salary | Join Date | +-------------+-------+-----+------------+--------+--------------------+ | 1 | Paul | 32 | California | 20,000 | 13-JUL-2001 | | 3 | Teddy | 23 | Norway | 20,000 | 13-DEC-2007 | | 4 | Mark | 25 | Rich-Mond | 65,000 | 13-DEC-2007 | | 5 | David | 27 | Texas | 85,000 | 13-DEC-2007 | | 2 | Allen | 25 | Texas | | 13-JUL-2005 | | 8 | Paul | 24 | Houston | 20,000 | 13-JUL-2005 | | 9 | James | 44 | Norway | 5,000 | 13-JUL-2005 | | 10 | James | 45 | Texas | 5,000 | | +-------------+-------+-----+------------+--------+--------------------+ | Average | | 31 | | 31,429 | 29-DEC-2005 | +-------------+-------+-----+------------+--------+--------------------+ | Tally | | 8 | | | | +-------------+-------+-----+------------+--------+--------------------+ | Pay | | | | | We have 1 grp | +-------------+-------+-----+------------+--------+--------------------+ | Group count | | | | | We have 1 grp | +-------------+-------+-----+------------+--------+--------------------+ | Comment | | | | | We're overspending | +=============+=======+=====+============+========+====================+
You can make the aggregator an normal ruby object, in which case it is just inserted into the footer at the requested location. If its type is the same as the column type, it participates in the formatting for that type and column.
tab_a.to_text do |f|
f.footer('Average', age: :avg, salary: :avg, join_date: :avg)
f.footer('Report Date', age: :count, join_date: Date.today)
f.format(numeric: '0.0R,', datetime: 'd[%v]D[%v]')
end+=============+=======+=====+============+========+=============+ | Id | Name | Age | Address | Salary | Join Date | +-------------+-------+-----+------------+--------+-------------+ | 1 | Paul | 32 | California | 20,000 | 13-JUL-2001 | | 3 | Teddy | 23 | Norway | 20,000 | 13-DEC-2007 | | 4 | Mark | 25 | Rich-Mond | 65,000 | 13-DEC-2007 | | 5 | David | 27 | Texas | 85,000 | 13-DEC-2007 | | 2 | Allen | 25 | Texas | | 13-JUL-2005 | | 8 | Paul | 24 | Houston | 20,000 | 13-JUL-2005 | | 9 | James | 44 | Norway | 5,000 | 13-JUL-2005 | | 10 | James | 45 | Texas | 5,000 | | +-------------+-------+-----+------------+--------+-------------+ | Average | | 31 | | 31,429 | 29-DEC-2005 | +-------------+-------+-----+------------+--------+-------------+ | Report Date | | 8 | | | 20-JAN-2022 | +=============+=======+=====+============+========+=============+
But it can be any type. Here we pick a lottery winner from the employee ids.
tab_a.to_text do |f|
f.footer('Average', age: :avg, salary: :avg, join_date: :avg)
winner_id = tab_a.column(:id).items.sample
f.footer('Lottery Winner', age: :count, join_date: winner_id)
f.format(numeric: '0.0R,', datetime: 'd[%v]D[%v]')
end+================+=======+=====+============+========+=============+ | Id | Name | Age | Address | Salary | Join Date | +----------------+-------+-----+------------+--------+-------------+ | 1 | Paul | 32 | California | 20,000 | 13-JUL-2001 | | 3 | Teddy | 23 | Norway | 20,000 | 13-DEC-2007 | | 4 | Mark | 25 | Rich-Mond | 65,000 | 13-DEC-2007 | | 5 | David | 27 | Texas | 85,000 | 13-DEC-2007 | | 2 | Allen | 25 | Texas | | 13-JUL-2005 | | 8 | Paul | 24 | Houston | 20,000 | 13-JUL-2005 | | 9 | James | 44 | Norway | 5,000 | 13-JUL-2005 | | 10 | James | 45 | Texas | 5,000 | | +----------------+-------+-----+------------+--------+-------------+ | Average | | 31 | | 31,429 | 29-DEC-2005 | +----------------+-------+-----+------------+--------+-------------+ | Lottery Winner | | 8 | | | 4 | +================+=======+=====+============+========+=============+
Perhaps the most flexible form of aggregator is a lambda form. They can take up to 2 or up to 3 parameters in non-group and group footers, respectively:
->(c, f) {...}- in a normal, non-group footer, you may provide for up to
two paramters: the first,
c, if given, will be bound to the column header to which the lambda is attached and and the second,f, if given will be bound to the footer in which the lambda appears. A lambda with no parameters can be provided as well if none are needed. ->(k, c, f)- in a group footer, you may provide for up to three
paramters: the the first,
k, if provided, will be bound to the group number of the group being evaluated, the second,c, if provided, will be bound to the column header to which the lambda is attached, and the third,f, will be bound to the footer in which the lambda appears. A lambda with no parameters can be provided as well if none are needed.
With the first argument, the footer itself becomes available and with it all
the things accessible with the footers, including the items in the current
column, through the f.items(c) accessor.
Compute the summ of the squares if the items in the :age column:
tab_a.to_text do |f|
f.format(numeric: '0.0R,', datetime: 'd[%v]D[%v]')
f.footer('Average', age: :avg, salary: :avg, join_date: :avg)
f.footer('SSQ', age: ->(c) { sa = c.items.map {|x| x * x}.sum; Math.sqrt(sa) })
end+=========+=======+=====+============+========+=============+ | Id | Name | Age | Address | Salary | Join Date | +---------+-------+-----+------------+--------+-------------+ | 1 | Paul | 32 | California | 20,000 | 13-JUL-2001 | | 3 | Teddy | 23 | Norway | 20,000 | 13-DEC-2007 | | 4 | Mark | 25 | Rich-Mond | 65,000 | 13-DEC-2007 | | 5 | David | 27 | Texas | 85,000 | 13-DEC-2007 | | 2 | Allen | 25 | Texas | | 13-JUL-2005 | | 8 | Paul | 24 | Houston | 20,000 | 13-JUL-2005 | | 9 | James | 44 | Norway | 5,000 | 13-JUL-2005 | | 10 | James | 45 | Texas | 5,000 | | +---------+-------+-----+------------+--------+-------------+ | Average | | 31 | | 31,429 | 29-DEC-2005 | +---------+-------+-----+------------+--------+-------------+ | SSQ | | 90 | | | | +=========+=======+=====+============+========+=============+
Group the table according to the employee’s year of joining, then compute the summ of the squares if the ages in each group:
tab_a.order_with('join_date.year').to_text do |f|
f.format(numeric: '0.0R,', datetime: 'd[%v]D[%v]', sort_key: '0.0~,')
f.footer('Average', age: :avg, salary: :avg, join_date: :avg)
f.gfooter('Group SSQ', age: ->(k, c, f) { sa = c.items.map {|x| x * x}.sum; Math.sqrt(sa) })
f.footer('Total SSQ', age: ->(c, f) { sa = c.items.map {|x| x * x}.sum; Math.sqrt(sa) })
end+===========+=======+=====+============+========+=============+==========+ | Id | Name | Age | Address | Salary | Join Date | Sort Key | +-----------+-------+-----+------------+--------+-------------+----------+ | 10 | James | 45 | Texas | 5,000 | | | +-----------+-------+-----+------------+--------+-------------+----------+ | Group SSQ | | 45 | | | | | +-----------+-------+-----+------------+--------+-------------+----------+ | 1 | Paul | 32 | California | 20,000 | 13-JUL-2001 | 2001 | +-----------+-------+-----+------------+--------+-------------+----------+ | Group SSQ | | 32 | | | | | +-----------+-------+-----+------------+--------+-------------+----------+ | 2 | Allen | 25 | Texas | | 13-JUL-2005 | 2005 | | 8 | Paul | 24 | Houston | 20,000 | 13-JUL-2005 | 2005 | | 9 | James | 44 | Norway | 5,000 | 13-JUL-2005 | 2005 | +-----------+-------+-----+------------+--------+-------------+----------+ | Group SSQ | | 56 | | | | | +-----------+-------+-----+------------+--------+-------------+----------+ | 3 | Teddy | 23 | Norway | 20,000 | 13-DEC-2007 | 2007 | | 4 | Mark | 25 | Rich-Mond | 65,000 | 13-DEC-2007 | 2007 | | 5 | David | 27 | Texas | 85,000 | 13-DEC-2007 | 2007 | +-----------+-------+-----+------------+--------+-------------+----------+ | Group SSQ | | 43 | | | | | +-----------+-------+-----+------------+--------+-------------+----------+ | Average | | 31 | | 31,429 | 29-DEC-2005 | | +-----------+-------+-----+------------+--------+-------------+----------+ | Total SSQ | | 90 | | | | | +===========+=======+=====+============+========+=============+==========+
As the examples show, one way to invoke the formatting methods is simply to
call one of the to_xxx methods directly on a table, which will yield a
FatTable::Formatter object to the block, and that is often the most
convenient way to do it. But there are a few other ways.
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 | |
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 | |
Finally, as in many of the examples, 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 | |
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`.
Bug reports and pull requests are welcome on GitHub at https://github.com/ddoherty03/fat_table.