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Sparser is string parsing and regular expressions for humans

Parsing strings can be a surprisingly difficult task. This difficulty multiplies when dealing with multiline strings. Traditionally, regular expressions were used for this. As anyone who has worked with regular expressions knows, however, they are difficult to read, difficult to maintain, full of gotchas, and don't scale well over multiple lines. Sparser was developed to handle this problem.

>>> import sparser as sp
>>> pattern = "Hello {{str}}!"
>>> string = "Hello World!"
>>> r = sp.match(pattern, string)
>>> print r

>>> pattern = "Hello {{str planet}}!"
>>> string = "Hello World!"
>>> r = sp.match(pattern, string)
>>> print r

>>> pattern = "Hello {{str planet}}!"
>>> string = "Hello World!"
>>> r = sp.parse(pattern, string)
>>> print r
{'planet': 'World'}

Syntax-wise, Sparser is a mashup of regular expressions and Handlebars-style templating. A more precise tag-line might be, Sparser is a reverse-templating language for matching and parsing strings

>>> pattern = "The {{spstr rocket}} {{str}} off at {{int hour}}:{{int minute}}" \
              "and costs {{currency price}}."
>>> compiled = sp.compile(pattern)
>>> print compiled.parse("The Falcon 9 blasts off at 5:30 and costs $62,000,000.")
{'rocket': 'Falcon 9',
 'hour': 5,
 'minute': 30,
 'price': 62000000

Table of Contents

  1. Examples
  2. Installation
  3. Documentation
    1. Method reference
    2. Pattern behaviour
    3. Tags
    4. Built-in types
    5. Custom types
  4. TODO
  5. Similar projects
  6. Acknowledgements
  7. Bugs


Sparser has a basic set of built-in types like str, float, and currency. If this isn't enough, you can also pass custom types into the compile method

>>> patt = "The {{str}} {{animal who}} was {{str action}} {{spstr food}}" \
           "on the {{int date}}rd of {{str month}}"
>>> custom_types = {"animal": ("cat|dog|pig", None)}
>>> compiled = sp.compile(patt, custom_types)
>>> print compiled.parse("The handsome cat was slurping pho on the 23rd of July")
{'who': 'cat',
 'action': 'slurping',
 'food': 'pho',
 'date': 23,
 'month': 'July'}

The first argument in the custom types tuple is the regex to match. The second is a callback method. Use it if you want to clean the output.

>>> custom_types = {"animal": ("cat|dog|pig", str.upper)}
>>> compiled = sp.compile(patt, custom_types)
>>> print compiled.parse("The handsome cat was slurping pho on the 23rd of July")
{'who': 'CAT',
 'action': 'slurping',
 'food': 'pho',
 'date': 23,
 'month': 'July'}

For simple one-offs, use inline lambda types

>>> patt = "The {{str}} {{"cat|dog|pig" who}} was {{str action}} {{spstr food}}" \
           "on the {{int date}}{{"st|nd|rd|th"}} of {{str month}}"
>>> compiled = sp.compile(patt)
>>> print compiled.parse("The handsome cat was slurping pho on the 23rd of July")
{'who': 'cat,
 'action': 'slurping',
 'food': 'pho',
 'date': 23,
 'month': 'July'}

Loops are one of the most power features of Sparser. They are great for lighly formatted tables

>>> patt = """\
    {*loop imdb_list*}
        {*case*}{{int rank}}. {{spstr title}} ({{int year}}) +{{float rating}}{*endcase*}
>>> compiled = sp.compile(patt)
>>> imdb_top_250 = """
1. The Shawshank Redemption (1994)  9.2
2. The Godfather (1972) 9.2
3. The Godfather: Part II (1974)    9.0
4. The Dark Knight (2008)   8.9"""
>>> print compiled.parse(imbd_top_250)
{"imdb_list": [
  {"rating": 9.2, "year": 1994, "rank": 1, "title": "The Shawshank Redemption"},
  {"rating": 9.2, "year": 1972, "rank": 2, "title": "The Godfather"},
  {"rating": 9.0, "year": 1974, "rank": 3, "title": "The Godfather: Part II"},
  {"rating": 8.9, "year": 2008, "rank": 4, "title": "The Dark Knight"}

Loops can have multiple cases which can either be named or unnamed

>>> patt = """\
    {*loop line_items*}
        {*case header*}{{spalpha name}}{*endcase*}
        {*case line_item*} {{spalpha name}} {{currency price}}{*endcase*}
        {*case total*} Total {{spalpha name}}: {{currency price}}{*endcase*}
        {*case break*}{*endcase*}
    Net income {{currency net_income}}"""
>>> compiled = sp.compile(patt)
>>> income_statement = """\
     Merchandise sales     $30
     Other revenues        $8
     Total Revenues:            $38

     Cost of goods sold    $15
     Depreciation          $3
     Wages                 $30
     Total Expenses:            $28

    Net income                - $10"""
>>> print compiled.parse(income_statement)
{"line_items": [
  {"case": "header", "name": "Revenues"},
  {"case": "line_item", "price": 30.0, "name": "Merchandise sales"},
  {"case": "line_item", "price": 8.0, "name": "Other revenues"},
  {"case": "total", "price": 38.0, "name": "Revenues"},
  {"case": "break"},
  {"case": "header", "name": "Expenses"},
  {"case": "line_item", "price": 15.0, "name": "Cost of goods sold"},
  {"case": "line_item", "price": 3.0, "name": "Depreciation"},
  {"case": "line_item", "price": 30.0, "name": "Wages"},
  {"case": "total", "price": 28.0, "name": "Expenses"},
  {"case": "break"}],
 "net_income": -10.0}

Similar to loops are switch statements. These can be used for simple multi-option matches

>>> patt = """\
    The Patriots are {*switch statement*}
        {*case fact*}an NFL team{*endcase*}
        {*case opinion*}overrated{*endcase*}
        {*case telling_it_like_it_is*}ball deflaters and serial cheaters{*endcase*}
>>> compiled = sp.compile(patt)
>>> string = "The Patriots are ball deflaters and serial cheaters."
>>> print compiled.parse(string)
{"statement": {"case": "telling_it_like_it_is"}}

You can use the {*include *} statement to embed patterns in patterns. This works on a preprocessor level (like #define from C) so it is equivalent to copying and pasting. This is useful for reusing common patterns or just breaking up and organizing longer ones.

>>> patt = """
    {*loop logs*}
        {*case*}{*include iso8601*}: {{spstr error}}{*endcase*}
        {*case*}{{spstr error}}: {*include iso8601*}{*endcase*}
>>> iso8601 = """{{int year}}-{{int month}}-{{int day}}T{{int hour}}:{{int minute}}:{{float second}}"""
>>> logs = """
    AssertionError: 2017-03-04T21:40:43.408923
    ZeroDivisionError: 2017-03-04T21:49:20.932833
    2017-03-04T21:52:03.987341: TypeError
>>> compiled = sp.compile(patt, includes={"iso8601": iso8601})
>>> print compiled.parse(logs)
{'logs': [
  {'error': 'AssertionError',
   'day': 4,
   'hour': 21,
   'minute': 40,
   'month': 3,
   'second': 43.408923,
   'year': 2017},
  {'error': 'ZeroDivisionError',
   'day': 4,
   'hour': 21,
   'minute': 49,
   'month': 3,
   'second': 20.932833,
   'year': 2017},
  {'error': 'TypeError',
   'day': 4,
   'hour': 21,
   'minute': 52,
   'month': 3,
   'second': 3.987341,
   'year': 2017}

Unfortunately, Sparser does not support nesting switches and loops in v0.1. This might be updated in future versions


$ pip install sparser

Sparser has no dependencies and is supported in both Python 2 & 3. I have no idea how Windows users install Python packages but Google can help you.


Method reference

sparser.parse(pattern, string[, custom_types[, includes]])

Given a pattern and a string, parse the string and return a dictionary. If the string does not match the pattern, a SparserValueError exception is raised. Optionally, use custom_types ({type_name: (type_pattern, callback)} format) and/or includes ({include_name: pattern})

sparser.match(pattern, string[, custom_types[, includes]])

The same as parse except instead of returning a dictionary, return True if the pattern successfully matched the string. Internally, this works the same as sparser.parse but is useful when you just need to know whether something matched and don't want to deal with error handling or falsy, empty dictionaries.

sparser.compile((pattern[, custom_types[, includes]])

Pre-compile a pattern and return a SparserObject which you can later call parse/match on. This is useful if speed is essential or simply as a way to keep your code clean.

SparserObject.parse(string[, custom_types[, includes]])

Same as sparser.parse but pre-compiled using the sparser.compile method

SparserObject.match(string[, custom_types[, includes]])

Same as sparser.match but pre-compiled using the sparser.compile method

Pattern behavior

Matching to the end of input

Sparser expects a perfect match and doesn't do partial matching. What this means is that this works

>>> print sp.parse("Hello {{alpha planet}}, nice to meet you", "Hello world, nice to meet you")
{'planet': 'world'}

But these will raise SparserValueErrors

>>> print sp.parse("Hello {{alpha planet}}", "Hello world, nice to meet you")
>>> print sp.parse("Hello {{alpha planet}}, nice to meet you", "Hello world")
>>> print sp.parse("{{alpha planet}}", "Hello world")

To get around this, you can use a {{spstr}} tag before or after your pattern like this

>>> print sp.parse("Hello {{alpha planet}}{{spstr}}", "Hello world, nice to meet you")
{'planet': 'world'}
>>> print sp.parse("Hello {{alpha planet}}{{spstr}}", "Hello world")
{'planet': 'world'}
>>> print sp.parse("{{spstr}} Hello {{alpha planet}}", "blah blah Hello world")
{'planet': 'world'}

spstr is a built-in type that stands for "spaced string". This is equivalent to the regex .+ meaning "any character 1+ times".

Un-matched cases

When Sparser is in a switch/loop, and there are multiple cases, it will return the first case to match the input. If no cases match within the switch/loop, it will raise a SparserValueError

Corraling loops and switches

Loops and switches greedily match everything until the pattern immediately after the block or the end of the string. So, if you have a table like this

1. Peach
2. Yoshi
3. Luigi
Play again?

The pattern below will raise a SparserValueError because it is going to try to match Play again? in the loop.

{*loop ranked_players*}
    {*case*}{{int rank}}. {{str name}}{*endcase*}

Instead, be sure to include the last line after the endloop

{*loop ranked_players*}
    {*case*}{{int rank}}. {{str name}}{*endcase*}
Play again?

Or, a {{spstr}} works in a pinch

{*loop ranked_players*}
    {*case*}{{int rank}}\. {{str name}}{*endcase*}

Loops and newlines

Loops are designed to handle table-like strings so newlines are implied in loop-matching. Multiline-loops are supported but inline loops are not. Sparser should support inline loops in version 0.2. In the meantime, you can use the regex bar (|) operator in custom or lambda types.

The number of spaces and newlines doesn't matter

Sparser is designed to make no distinction between single spaces and multiple spaces (just like HTML). This means

>>> sp.match("The moon", "The     moon")


>>> sp.match("The     sun", "The sun")


>>> sp.match("The     sun", "Thesun")

The same is true for \n newlines. This is not without precedent. HTML rendering works in the same way. This was done because of the unique challenges in parsing multi-line regular expressions.


For the moment, there are only eight tags in Sparser

Tag Notes
{{<var_type> <var_name>}} var_name is optional
{*switch <switch_name>*} can only contain {*case*} tags as direct decendents
{*loop <loop_name>*} can only contain {*case*} tags as direct decendents
{*case <case_name>*} case_name is optional
{*include <include_name>*} this just inserts one pattern into another. Think of it like C's #define macro

Built-in types

These types can be used in any variable tag

Type Description Python Regex Pattern
str a string with no spaces "\S+"
spstr a string with spaces allowed ".+"
int an integer. Won't accept decimals "-? ?[0-9,]+"
float a float "-? ?[0-9,.]+"
alpha a string without digits, special chars, or spaces "[a-zA-Z]+"
spalpha a string without digits or special chars "[a-zA-Z ]+"
alphanumeric a string without special chars or spaces "[a-zA-Z0-9_]+"
spalphanumeric a string without special chars "[a-zA-Z0-9_ ]+"
currency a float which might be prefixed with '$' and/or - "-? ?[$-]*[0-9,.]+"

Custom types

Custom types are optional parameters. They take the form of

{type_name: (regex_pattern, callback), ...}

Callback is a function to modify the extracted string before inclusion in the dictionary. For example, to uppercase the match, do

lambda float_str: float_str.upper()
# str.upper also works

If you wanted to add a custom date type, you could do something like

date_cb = lambda dt_str: date.strptime(st_str, "%m/%d/%Y")
custom_types = {"date": ("\d{2}/\d{2}/\d{4}", date_cb)}

If you just want to return the un-modified string, pass in None

custom_types = {"berry": ("(?:blue|black|rasp)berry" , None)}


  • Nested loops (will need to rewrite major parts as a finite state machine)
  • Inline loops (If a loop is not adjacent to \n on both sides, we should not automatically newline it)
  • Cleanup the building of the AST
  • Add unicode-compatible currency (euros, yen, etc.)
  • Tests should use assertRaisesRegexp and match to exactly which exception was raised
  • built-in date types
  • .search for un-strict matching

Similar projects


This project was born at UtilityAPI. We automate the collection of utility data for clean energy projects. A large part of our work involves the extraction of text from pdf bills. The difficulties inherent in that task served as the inspiration for this project and they have been very supportive with giving me time and encouragment.

Also at UtilityAPI, Akshay Doangkar was the other half of this project. Together, we collaboratively designed the specification and he ended up writing the first test implementation.


This is version 0.1 and there is liable to be a bug or two that we missed. Please let us know or submit a patch.

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