Library to query python dicts
Several syntax examples:
"`age` >= 12"
"`user.name` == 'cyberlis'"
"`user.email` MATCH /\w+@\w+\.com/ AND `age` != 11"
"`user.frinds.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`"
| type | example |
|---|---|
| KEY | `name`, `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 |
Dict keys 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
>>> 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 evalute 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 evalutes 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.py", line 355, in match
return self._eval_expr(query_dict, self.ast)
File "dictquery.py", line 327, in _eval_expr
dict_value = self._get_dict_value(query_dict, tree.value)
File "dictquery.py", line 302, in _get_dict_value
self.key_separator, self.raise_keyerror)
File "dictquery.py", line 258, in get_dict_value
raise DQKeyError("Key '%s' not found" % dict_key)
dictquery.DQKeyError: "Key 'favoriteFruit' not found"
| 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 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 |
| CONTAIN | 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` CONTAIN '.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
| Operation | Meaning |
|---|---|
| IN | dict item in array |
| CONTAIN | dict item contains matching item |
>>> import dictquery as dq
>>> dq.match(data, "`tags` CONTAIN 'dolor'")
True
>>> dq.match(data, "`eyeColor` IN ['blue', 'green', 'black']")
True
CONTAIN can be used with dict items to check if key in dict
>>> import dictquery as dq
>>> dq.match(data, "`name` CONTAIN 'firstname'")
True
>>> dq.match(data, "`name` CONTAIN 'thirdname'")
False
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
| 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 evalution 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
from datetime import datetime
data = {
"_id": 10,
"isActive": False,
"age": 27,
"eyeColor": "green",
"name": {
"firstname": "Marion",
"secondname": "Delgado",
},
"gender": "female",
"email": "mariondelgado@bleendot.com",
"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"
}
]
}