dictquery 0.5.0

Library to query python dicts

DictQuery

Library to query python dicts

Several syntax examples:

"age >= 12"
"`user.name` == 'cyberlis'"
"`user.email` MATCH /\w+@\w+\.com/ AND age != 11"
"`user.friends.age` > 12 AND `user.friends.name` LIKE 'Ra*ond'"
"email LIKE 'mariondelgado?bleendot?com'"
"eyeColor IN ['blue', 'green', 'black']"
"isActive AND (gender == 'female' OR age == 27)"
"latitude != longitude"

Supported data types

type	example
KEY	name, age, `friends.name.firstname`, `friends.age`
NUMBER	42, -12, 34.7
STRING	'hello', "hellow"
BOOLEAN	true, false
NONE	none, null
NOW	utc current datetime
REGEXP	/\d+\d+\w+/
ARRAY	list of any items and any types

Keys

Key literals must start with a letter or an underscore, such as:

• _underscore
• underscore_

The remainder of your variable name may consist of letters, numbers and underscores.

• password1
• n00b
• un_der_scores

If you need a key with separator character (. or /) because you use nested keys, or need spaces or other punctuation characters in key, use back-ticks (``)

DictQuery supports nested dicts splited by dot . or any separator specified in key_separator param. Default key_separator='.'

>>> import dictquery as dq
>>> dq.match(data, "`friends.age` <= 26")
True
>>> compiled = dq.compile("`friends/age` <= 26", key_separator='/')
>>> compiled.match(data)
True

if you don't need nested keys parsing and want get keys as is or if your keys contain separator char, you can disable nested keys behaviour by setting use_nested_keys=False

>>> import dictquery as dq
>>> dq.match(data, "`user.address`")
False
>>> dq.match(data, "age")
True
>>> compiled = dq.compile("`user.address`", use_nested_keys=False)
>>> compiled.match(data)
True

In query you can use dict keys 'as is' without any binary operation. DictQuery will get value by the key and evaluate it to bool

>>> import dictquery as dq
>>> dq.match(data, "isActive")
False
>>> dq.match(data, "isActive == false")
True

if key is not found by default this situation evaluates to boolean False (no exception raised). You can set raise_keyerror=True to raise keyerror if key would not be found.

>>> import dictquery as dq
>>> dq.match(data, "favoriteFruit")
False
>>> compiled = dq.compile("`favoriteFruit`", raise_keyerror=True)
>>> compiled.match(data)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File ".../dictquery/dictquery/visitors.py", line 41, in match
    return self.evaluate(data)
  File ".../dictquery/dictquery/visitors.py", line 35, in evaluate
    result = bool(self.ast.accept(self))
  File ".../dictquery/dictquery/parsers.py", line 80, in accept
    return visitor.visit_key(self)
  File ".../dictquery/dictquery/visitors.py", line 84, in visit_key
    values=self._get_dict_value(expr.value),
  File ".../dictquery/dictquery/visitors.py", line 30, in _get_dict_value
    self.key_separator, self.raise_keyerror)
  File ".../dictquery/dictquery/datavalue.py", line 112, in query_value
    raise DQKeyError("Key '{}' not found".format(data_key))
dictquery.exceptions.DQKeyError: "Key 'favoriteFruit' not found"

Comparisons

Operation	Meaning
<	strictly less than
<=	less than or equal
>	strictly greater than
>=	greater than or equal
==	equal
!=	not equal

>>> import dictquery as dq
>>> dq.match(data, "age == 26")
True
>>> dq.match(data, "latitude > 12")
True
>>> dq.match(data, "longitude < 30")
True
>>> dq.match(data, "`friends.age` <= 26")
True
>>> dq.match(data, "longitude >= -130")
True
>>> dq.match(data, "id != 0")
True
>>> dq.match(data, "gender == 'male'")
False

String comparisons and matching

String literals are written in a variety of ways:

• Single quotes: 'allows embedded "double" quotes'
• Double quotes: "allows embedded 'single' quotes".

Operation	Meaning
MATCH	regexp matching
LIKE	glob like matching
IN	dict item substring in string
CONTAINS	dict item substring contains string

< , <= , > , >= , == , != works same way with strings as python

>>> import dictquery as dq
>>> dq.match(data, "eyeColor == 'green'")
True
>>> dq.match(data, "`name.firstname` != 'Ratliff'")
True
>>> dq.match(data, "eyeColor IN 'string with green color'")
True
>>> dq.match(data, "email CONTAINS '.com'")
True
>>> dq.match(data, r"email MATCH /\w+@\w+\.\w+/")
True
>>> dq.match(data, r"email LIKE 'mariondelgado@*'")
True
>>> dq.match(data, r"email LIKE 'mariondelgado?bleendot?com'")
True

By default all string related operations are case sensitive. To change this behaviour you have to create instance of DictQuery with case_sensitive=False

>>> import dictquery as dq
>>> dq.match(data, "`name.firstname` == 'marion'")
False
>>> compiled = dq.compile("`name.firstname` == 'marion'", case_sensitive=False)
>>> compiled.match(data)
True

Array comparisons

Operation	Meaning
IN	dict item in array
CONTAINS	dict item contains matching item

>>> import dictquery as dq
>>> dq.match(data, "tags CONTAINS 'dolor'")
True
>>> dq.match(data, "eyeColor IN ['blue', 'green', 'black']")
True

Key presence in dict

CONTAINS can be used with dict items to check if key in dict

>>> import dictquery as dq
>>> dq.match(data, "name CONTAINS 'firstname'")
True
>>> dq.match(data, "name CONTAINS 'thirdname'")
False

Datetime comparisons with NOW

NOW returns current utc datetime

dict item can be compared with NOW using standard operations (< , <= , > , >= , == , !=)

>>> import dictquery as dq
>>> dq.match(data, "registered < NOW")
True
>>> dq.match(data, "registered != NOW")
True

Logical operators

Operator	Meaning	Example
and	True if both the operands are true	x and y
or	True if either of the operands is true	x or y
not	True if operand is false (complements the operand)	not x

>>> import dictquery as dq
>>> dq.match(data, "isActive AND gender == 'female'")
False
>>> dq.match(data, "isActive OR gender == 'female'")
True
>>> dq.match(data, "NOT isActive AND gender == 'female'")
True

You can use parentheses to group statements or change evaluation order

>>> import dictquery as dq
>>> dq.match(data, "isActive AND gender == 'female' OR age == 27")
True
>>> dq.match(data, "isActive AND (gender == 'female' OR age == 27)")
False

Data for examples above:

from datetime import datetime
data = {
  "_id": 10,
  "isActive": False,
  "age": 27,
  "eyeColor": "green",
  "name": {
    "firstname": "Marion",
    "secondname": "Delgado",
  },
  "gender": "female",
  "email": "[e-mail]",
  "registered": datetime.strptime("2015-03-29T06:07:58", "%Y-%m-%dT%H:%M:%S"),
  "latitude": 74.785608,
  "longitude": -112.366088,
  "tags": [
    "voluptate",
    "ex",
    "dolor",
    "aute"
  ],
  "user.address": "155 Village Road, Enetai, Puerto Rico, 2634",
  "friends": [
    {
      "id": 0,
      "name": {
        "firstname": "Ratliff",
        "secondname": "Becker",
      },
      "age": 27,
      "eyeColor": "green"
    },
    {
      "id": 1,
      "name": {
        "firstname": "Raymond",
        "secondname": "Albert",
      },
      "age": 19,
      "eyeColor": "brown"
    },
    {
      "id": 2,
      "name": {
        "firstname": "Mavis",
        "secondname": "Sheppard",
      },
      "age": 34,
      "eyeColor": "blue"
    }
  ]
}