Design mini-lanugage for data input

aurora aurora00 at
Tue Mar 21 07:32:44 CET 2006

This is an entry I just added to ASPN. It is a somewhat novel technique I  
have employed quite successfully in my code. I repost it here for more  
explosure and discussions.


Title: Design mini-lanugage for data input


Many programs need a set of initial data. For ease of use and flexibility,  
design a mini-language for your input data. Use Python's superb text  
handling capability to parse and build the data structure from the input  

Source: Text Source
# this is an example to demonstrate the programming technique

DATA = """
# data souce:
# Country / Captial / Area [sq. km] / 2002 Population Estimate
China / Beijing / 9,596,960 / 1,284,303,705
India / New Delhi / 3,287,590 / 1,045,845,226
United States / Washington DC / 9,629,091 / 280,562,489
Indonesia / Jakarta / 1,919,440 / 231,328,092
Russia / Moscow / 17,075,200 / 144,978,573

def initData():
     """ parse and return a country list of (name, captial, area,  
population) """

     countries = []
     for line in DATA.splitlines():

         # filter out blank lines/comment lines
         line = line.strip()
         if not line or line.startswith('#'):

         # 4 fields separated by '/'
         parts = map(string.strip, line.split('/'))
         country, captial, area, population = parts

         # remove commas in numbers
         area = int(area.replace(',',''))
         population = int(population.replace(',',''))

         countries.append((country, captial, area, population))

     return countries

def findLargestCountry(countries):
     # your algorithm here

def main():
     countries = initData()
     print findLargestCountry(countries)



Many programs need a set of initial data. The simplest way is to construct  
Python data structure directly as shown below. This is often not ideal.  
Algorithm and data structure tend to change. Python program statements is  
likely differ literally from its data source, which might be text pulled  
 from web pages or other place. This means a great deal of effort is often  
needed to format and maintain the input as Python statements.

This is a sample program that initialize some geographical data.

# map of country -> (captial, area, population)
COUNTRIES['China'] = ('Beijing', 9596960, 1284303705)
COUNTRIES['India'] = ('New Delhi', 3287590, 1045845226)
COUNTRIES['United States'] = ('Washington DC', 9629091, 280562489)
COUNTRIES['Indonesia'] = ('Jakarta', 1919440, 231328092)
COUNTRIES['Russia'] = ('Moscow', 17075200, 144978573)


A more flexible approach is to define a mini-lanugage to describe the  
data. This can be as simple as formatting data into a multiple-line string.

1. Define the data format in text. It should mirror the data source and  
designed for ease for human editing.

2. Define the data structure.

3. Write glue code to parse the input data and initialize the data  

In the example above we use one line for each record. Each record has four  
fields, Country, captial, area and population, separated by slashes. One  
of the immediate benefit is that we no longer need to type so many quotes  
for every string literal. This concise data format is much easiler to read  
and edit than Python statements.

The parser simply break down the input text using splitlines() and then  
loop through them line by line. It is useful to account for some extra  
white space so that it is more flexible for human editor. In this case the  
numbers (area, population) from the data source contains commas. Rather  
than manually edit them out, they are copied as is into the text as is.  
Then they are parsed into integer using

area = int(area.replace(',',''))

Slash is chosen as the separator (rather than the more common comma)  
because it does not otherwise appear in the data. A record is parsed into  
field using


Don't forget to remove extra white space using string.strip()

Finally it built a data structure of list of country record as tuple of  
(country, captial, area, population). It is just as easy to turn them into  
objects or any other data structure as desired.

The mini-language technique can be refined to represent more complex, more  
structured input. It makes transformation and maintenance of input data  
much easier.

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