You [Gerald Bauer¹] have been permanently banned [for life] from participating in r/ruby (because of your writing off / outside of r/ruby). I do not see your participation adding anything to this [ruby] community.
-- Richard Schneeman (r/ruby mod and fanatic illiberal ultra leftie on a cancel culture mission)
¹: I know. Who cares? Who is this Gerald Bauer anyway. A random nobody for sure. It just happens that I am the admin among other things of Planet Ruby.
Case Studies of Code of Conduct "Cancel Culture" Out-Of-Control Power Abuse - Ruby - A Call for Tolerance On Ruby-Talk Results In Ban On Reddit RubyUpdate (August, 2022) - A Call for More Tolerance And Call For No-Ban Policy Results In Ban On Ruby-Talk (With No Reason Given)
> I just banned gerald.bauer@gmail.com. > > -- SHIBATA Hiroshi > >> THANK YOU >> >> -- Ryan Davis >> >> >> My full support to moderators. >> >> -- Xavier Noria >> >> My full support to moderators. >> >> -- Carlo E. Prelz >> >> That's fun. >> >> -- Alice
« Ruby Open Data Week 2021, March 6th to March 12th - 7 Days of Ruby (Open Data) Gems
Written by Gerald Bauer
A code monkey and enthusiastic collector (and publisher) of open football and beer data. Skills include Ruby, SQLite and CSV. Spec lead of CSV <3 JSON.
Let’s say you want to share your data with the world or use the data that others share with the world. How to get started? A pragmatic way is to use tabular data packages.
Tabular Data Package is a simple structure for publishing and sharing tabular data with the following key features:
- Data is stored in CSV (comma separated values) files
- Metadata about the dataset both general (e.g. title, author) and the specific data files (e.g. schema) is stored in a single JSON file named
datapackage.json
which follows the Data Package format
(Source: Tabular Data Packages, Data Hub)
Here’s a minimal example of a tabular data package
holding two files, that is, data.csv
and datapackage.json
:
data.csv
:
Brewery,City,Name,Abv
Andechser Klosterbrauerei,Andechs,Doppelbock Dunkel,7%
Augustiner Bräu München,München,Edelstoff,5.6%
Bayerische Staatsbrauerei Weihenstephan,Freising,Hefe Weissbier,5.4%
Brauerei Spezial,Bamberg,Rauchbier Märzen,5.1%
Hacker-Pschorr Bräu,München,Münchner Dunkel,5.0%
Staatliches Hofbräuhaus München,München,Hofbräu Oktoberfestbier,6.3%
...
datapackage.json
:
{
"name": "beer",
"resources": [
{
"path": "data.csv",
"schema": {
"fields": [ { "name": "Brewery", "type": "string" },
{ "name": "City", "type": "string" },
{ "name": "Name", "type": "string" },
{ "name": "Abv", "type": "number" } ]
}
}
]
}
For some “real world” examples see the Data Packages Listing at Data Hub’s Core Datasets site for a start. Tabular data packages include:
Name | Comments |
---|---|
country-codes |
Comprehensive country codes: ISO 3166, ITU, ISO 4217 currency codes and many more |
language-codes |
ISO Language Codes (639-1 and 693-2) |
currency-codes |
ISO 4217 Currency Codes |
gdb |
Country, Regional and World GDP (Gross Domestic Product) |
s-and-p-500-companies |
S&P 500 Companies with Financial Information |
un-locode |
UN-LOCODE Codelist |
and many more.
Now the questions is how to work with tabular data packages in Ruby. Let’s try the csvpack gem.
require 'csvpack'
CsvPack.import(
's-and-p-500-companies',
'gdb'
)
Using CsvPack.import
will:
1) download all data packages to the ./pack
folder
2) (auto-)add all tables to an in-memory SQLite database using SQL create_table
statements via ActiveRecord
migrations e.g.
create_table :constituents_financials do |t|
t.string :symbol # Symbol (string)
t.string :name # Name (string)
t.string :sector # Sector (string)
t.float :price # Price (number)
t.float :dividend_yield # Dividend Yield (number)
t.float :price_earnings # Price/Earnings (number)
t.float :earnings_share # Earnings/Share (number)
t.float :book_value # Book Value (number)
t.float :_52_week_low # 52 week low (number)
t.float :_52_week_high # 52 week high (number)
t.float :market_cap # Market Cap (number)
t.float :ebitda # EBITDA (number)
t.float :price_sales # Price/Sales (number)
t.float :price_book # Price/Book (number)
t.string :sec_filings # SEC Filings (string)
end
3) (auto-)import all records using SQL inserts e.g.
INSERT INTO constituents_financials
(symbol,
name,
sector,
price,
dividend_yield,
price_earnings,
earnings_share,
book_value,
_52_week_low,
_52_week_high,
market_cap,
ebitda,
price_sales,
price_book,
sec_filings)
VALUES
('MMM',
'3M Co',
'Industrials',
162.27,
2.11,
22.28,
7.284,
25.238,
123.61,
162.92,
104.0,
8.467,
3.28,
6.43,
'http://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=MMM')
4) (auto-)add ActiveRecord
models for all tables.
So what? Now you can use all the “magic” of ActiveRecord
to query
the datasets. Example:
puts "Constituent.count: #{Constituent.count}"
# SELECT COUNT(*) FROM "constituents"
# => 496
pp Constituent.first
# SELECT "constituents".* FROM "constituents" ORDER BY "constituents"."id" ASC LIMIT 1
# => #<Constituent:0x9f8cb78
# id: 1,
# symbol: "MMM",
# name: "3M Co",
# sector: "Industrials">
pp Constituent.find_by!( symbol: 'MMM' )
# SELECT "constituents".*
# FROM "constituents"
# WHERE "constituents"."symbol" = "MMM"
# LIMIT 1
# => #<Constituent:0x9f8cb78
# id: 1,
# symbol: "MMM",
# name: "3M Co",
# sector: "Industrials">
pp Constituent.find_by!( name: '3M Co' )
# SELECT "constituents".*
# FROM "constituents"
# WHERE "constituents"."name" = "3M Co"
# LIMIT 1
# => #<Constituent:0x9f8cb78
# id: 1,
# symbol: "MMM",
# name: "3M Co",
# sector: "Industrials">
pp Constituent.where( sector: 'Industrials' ).count
# SELECT COUNT(*) FROM "constituents"
# WHERE "constituents"."sector" = "Industrials"
# => 63
pp Constituent.where( sector: 'Industrials' ).all
# SELECT "constituents".*
# FROM "constituents"
# WHERE "constituents"."sector" = "Industrials"
# => [#<Constituent:0x9f8cb78
# id: 1,
# symbol: "MMM",
# name: "3M Co",
# sector: "Industrials">,
# #<Constituent:0xa2a4180
# id: 8,
# symbol: "ADT",
# name: "ADT Corp (The)",
# sector: "Industrials">,...]
and so on.
Use the CsvPack::Downloader
class to download a data package
to your disk (by default data packages get stored in ./pack
).
dl = CsvPack::Downloader.new
dl.fetch( 'language-codes' )
dl.fetch( 's-and-p-500-companies' )
dl.fetch( 'un-locode' )
Will result in:
-- pack
|-- language-codes
| |-- data
| | |-- language-codes-3b2.csv
| | |-- language-codes.csv
| | `-- language-codes-full.csv
| `-- datapackage.json
|-- s-and-p-500-companies
| |-- data
| | |-- constituents.csv
| | `-- constituents-financials.csv
| `-- datapackage.json
`-- un-locode
|-- data
| |-- code-list.csv
| |-- country-codes.csv
| |-- function-classifiers.csv
| |-- status-indicators.csv
| `-- subdivision-codes.csv
`-- datapackage.json
Use the CsvPack::Pack
class to read-in a data package
and add and import into an SQL database.
pack = CsvPack::Pack.new( './pack/un-locode/datapackage.json' )
pack.tables.each do |table|
table.up! # (auto-) add table using SQL create_table via ActiveRecord migration
table.import! # import all records using SQL inserts
end
That’s it.
You can connect to any database supported by ActiveRecord. If you do NOT establish a connection in your script - the standard (default fallback) is using an in-memory SQLite3 database.
For example, to create an SQLite3 database on disk - lets say data.db
-
use in your script (before the CsvPack.import
statement):
ActiveRecord::Base.establish_connection( adapter: 'sqlite3',
database: './data.db' )
For example, to connect to a PostgreSQL database use in your script
(before the CsvPack.import
statement):
require 'pg' ## pull-in PostgreSQL (pg) machinery
ActiveRecord::Base.establish_connection( adapter: 'postgresql',
username: 'ruby',
password: 'topsecret',
database: 'database' )
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