Collections¶

Note

Usage on each methods are valid on the premise that the following classes is defined in advance.

# coding: utf-8

from owlmixin import OwlMixin, TOption
from owlmixin.owlcollections import TDict, TList
from owlmixin.owlenum import OwlEnum, OwlObjectEnum


class Animal(OwlObjectEnum):  # pragma: no cover
    DOG = ("dog", {"cry": "bow-wow"})
    CAT = ("cat", {"cry": "mewing"})

    def cry(self):
        return self.object["cry"]


class Color(OwlEnum):  # pragma: no cover
    RED = "red"
    GREEN = "green"
    BLUE = "blue"


class Food(OwlMixin):  # pragma: no cover
    name: str
    names_by_lang: TOption[TDict[str]]


class Human(OwlMixin):  # pragma: no cover
    id: int
    name: str
    favorites: TList[Food]


class Machine(OwlMixin):  # pragma: no cover
    id: int
    name: str


class Japanese(OwlMixin):  # pragma: no cover
    name: str
    language: str = "japanese"

TList¶

class owlmixin.owlcollections.TList(iterable=(), /)[source]¶

to_iterator() → TIterator[T][source]¶

Usage:

>>> it = TList([1, 2, 3]).to_iterator()
>>> it.to_list()
[1, 2, 3]
>>> it.to_list()
[]

get(index: int) → TOption[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).get(3)
Option --> 4
>>> TList([1, 2, 3, 4, 5]).get(5)
Option --> None

for_each(func: Callable[[T], None]) → None[source]¶

Usage:

>>> TList([1, 2, 3]).for_each(lambda x: print(str(x)))
1
2
3

map(func: Callable[[T], U]) → TList[U][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).map(lambda x: x+1)
[2, 3, 4, 5, 6]

emap(func: Callable[[T, int], U]) → TList[U][source]¶

Usage:

>>> TList([10, 20, 30, 40, 50]).emap(lambda x, i: (x+1, i))
[(11, 0), (21, 1), (31, 2), (41, 3), (51, 4)]

flatten() → TList[U][source]¶

Usage:

>>> TList([[1, 2], [3, 4]]).flatten()
[1, 2, 3, 4]

flat_map(func: Callable[[T], List[U]]) → TList[U][source]¶

Usage:

>>> TList([1, 2, 3]).flat_map(lambda x: [x, x+1])
[1, 2, 2, 3, 3, 4]

filter(func: Callable[[T], bool]) → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).filter(lambda x: x > 3)
[4, 5]

reject(func: Callable[[T], bool]) → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).reject(lambda x: x > 3)
[1, 2, 3]

head() → TOption[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).head()
Option --> 1
>>> TList([]).head()
Option --> None

take(size_: int) → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).take(3)
[1, 2, 3]

take_while(func: Callable[[T], bool]) → TList[T][source]¶

Usage:

>>> TList([1, 2, 30, 4, 50]).take_while(lambda x: x < 10)
[1, 2]

tail(size_: int) → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).tail(3)
[3, 4, 5]

uniq() → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, 2, 1]).uniq()
[1, 2, 3]

uniq_by(func: Callable[[T], Any]) → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, -2, -1]).uniq_by(lambda x: x**2)
[1, 2, 3]

partition(func: Callable[[T], bool]) → Tuple[TList[T], TList[T]][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).partition(lambda x: x > 3)
([1, 2, 3], [4, 5])

group_by(to_key: Callable[[T], str]) → TDict[TList[T]][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).group_by(lambda x: x % 2).to_json()
'{"0": [2,4],"1": [1,3,5]}'

key_by(to_key: Callable[[T], str]) → TDict[T][source]¶

Parameters:: to_key – value -> key

Usage:

>>> TList(['a1', 'b2', 'c3']).key_by(lambda x: x[0]).to_json()
'{"a": "a1","b": "b2","c": "c3"}'
>>> TList([1, 2, 3, 4, 5]).key_by(lambda x: x % 2).to_json()
'{"0": 4,"1": 5}'

order_by(func: Callable[[T], Any], reverse: bool = False) → TList[T][source]¶

Usage:

>>> TList([12, 25, 31, 40, 57]).order_by(lambda x: x % 10)
[40, 31, 12, 25, 57]
>>> TList([12, 25, 31, 40, 57]).order_by(lambda x: x % 10, reverse=True)
[57, 25, 12, 31, 40]

concat(values: List[T], first: bool = False) → TList[T][source]¶

Usage:

>>> TList([1, 2]).concat(TList([3, 4]))
[1, 2, 3, 4]
>>> TList([1, 2]).concat(TList([3, 4]), first=True)
[3, 4, 1, 2]

reduce(func: Callable[[U, T], U], init_value: U) → U[source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).reduce(lambda t, x: t + 2*x, 100)
130

sum() → Union[int, float][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).sum()
15

sum_by(func: Callable[[T], Union[int, float]]) → Union[int, float][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).sum_by(lambda x: x*2)
30

count_by(func: Callable[[T], Any]) → TDict[int][source]¶

Usage:

>>> TList([1, 11, 25, 35, 21, 4]).count_by(lambda x: x % 10)
{1: 3, 5: 2, 4: 1}

size() → int[source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).size()
5

join(joint: str) → str[source]¶

Usage:

>>> TList(['A', 'B', 'C']).join("-")
'A-B-C'

unlines(*, crlf: bool = False) → str[source]¶

Usage:

>>> TList(['aaa', 'bbb', 'ccc']).unlines()
'aaa\nbbb\nccc'
>>> TList(['A', 'B', 'C']).unlines(crlf=True)
'A\r\nB\r\nC'

find(func: Callable[[T], bool]) → TOption[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).find(lambda x: x > 3)
Option --> 4
>>> TList([1, 2, 3, 4, 5]).find(lambda x: x > 6)
Option --> None

all(func: Callable[[T], bool]) → bool[source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).all(lambda x: x > 0)
True
>>> TList([1, 2, 3, 4, 5]).all(lambda x: x > 1)
False

any(func: Callable[[T], bool]) → bool[source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).any(lambda x: x > 4)
True
>>> TList([1, 2, 3, 4, 5]).any(lambda x: x > 5)
False

intersection(values: List[T]) → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).intersection([2, 4, 6])
[2, 4]

not_intersection(values: List[T]) → TList[T][source]¶

Usage:

>>> TList([1, 2, 3, 4, 5]).not_intersection([2, 4, 6])
[1, 3, 5]

to_csv(fieldnames: Sequence[str], *, with_header: bool = False, crlf: bool = False, tsv: bool = False) → str¶

From sequence of text to csv string

Parameters:

fieldnames – Order of columns by property name
with_header – Add headers at the first line if True
crlf – Add CRLF line break at the end of line if True, else add LF
tsv – Use tabs as separator if True, else use comma

Returns:

Csv string

Usage:

>>> from owlmixin.samples import Human
>>> humans = Human.from_dicts([
...     {"id": 1, "name": "Tom", "favorites": [{"name": "Apple"}]},
...     {"id": 2, "name": "John", "favorites": [{"name": "Orange"}]}
... ])

>>> print(humans.to_csv(fieldnames=['name', 'id', 'favorites']))
Tom,1,[{'name': 'Apple'}]
John,2,[{'name': 'Orange'}]

>>> print(humans.to_csv(fieldnames=['name', 'id', 'favorites'], with_header=True))
name,id,favorites
Tom,1,[{'name': 'Apple'}]
John,2,[{'name': 'Orange'}]

to_csvf(fpath: str, fieldnames: Sequence[str], *, encoding: str = 'utf8', with_header: bool = False, crlf: bool = False, tsv: bool = False) → str¶

From instance to yaml file

Parameters:

fpath – Csv file path
fieldnames – Order of columns by property name
encoding – Csv file encoding
with_header – Add headers at the first line if True
crlf – Add CRLF line break at the end of line if True, else add LF
tsv – Use tabs as separator if True, else use comma

Returns:

Csv file path

to_dicts(*, ignore_none: bool = True, force_value: bool = True, ignore_empty: bool = False) → List[dict]¶

From instance to dict

Parameters:

ignore_none – Properties which is None are excluded if True
force_value – Transform to value using to_value (default: str()) of ValueTransformer which inherited if True
ignore_empty – Properties which is empty are excluded if True

Returns:

List[Dict]

Usage:

>>> from owlmixin.samples import Human, Food
>>> human_dicts = [
...     {
...         "id": 1,
...         "name": "Tom",
...         "favorites": [
...             {"name": "Apple", "names_by_lang": {"en": "Apple"}}
...         ]
...     },
...     {
...         "id": 2,
...         "name": "John",
...         "favorites": [
...             {"name": "Orange", "names_by_lang": {"en": "Orange"}}
...         ]
...     }
... ]
>>> Human.from_dicts(human_dicts).to_dicts() == human_dicts
True

You can include None properties by specifying False for ignore_none

>>> f = Food.from_dicts([{"name": "Apple"}]).to_dicts(ignore_none=False)
>>> f[0]["name"]
'Apple'
>>> "names_by_lang" in f[0]
True
>>> f[0]["names_by_lang"]

As default

>>> f = Food.from_dicts([{"name": "Apple"}]).to_dicts()
>>> f[0]["name"]
'Apple'
>>> "names_by_lang" in f[0]
False

You can exclude Empty properties by specifying True for ignore_empty

>>> f = Human.from_dicts([{"id": 1, "name": "Ichiro", "favorites": []}]).to_dicts()
>>> f[0]["favorites"]
[]
>>> f = Human.from_dicts([{"id": 1, "name": "Ichiro", "favorites": []}]).to_dicts(ignore_empty=True)
>>> "favorites" in f[0]
False

to_json(*, indent: int = None, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to json string

Parameters:

indent – Number of indentation
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> human.to_json()
'{"favorites": [{"name": "Apple","names_by_lang": {"de": "Apfel","en": "Apple"}},{"name": "Orange"}],"id": 1,"name": "Tom"}'

to_jsonf(fpath: str, encoding: str = 'utf8', *, indent: int = None, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to json file

Parameters:

fpath – Json file path
encoding – Json file encoding
indent – Number of indentation
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json file path

to_pretty_json(*, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to pretty json string

Parameters:

ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> print(human.to_pretty_json())
{
    "favorites": [
        {
            "name": "Apple",
            "names_by_lang": {
                "de": "Apfel",
                "en": "Apple"
            }
        },
        {
            "name": "Orange"
        }
    ],
    "id": 1,
    "name": "Tom"
}

to_table(fieldnames: Sequence[str]) → str¶

From sequence of text to csv string

Parameters:: fieldnames – Order of columns by property name
Returns:: Table string

Usage:

>>> from owlmixin.samples import Human
>>> humans = Human.from_dicts([
...     {"id": 1, "name": "Tom", "favorites": [{"name": "Apple"}]},
...     {"id": 2, "name": "John", "favorites": [{"name": "Orange"}]}
... ])
>>> print(humans.to_table(fieldnames=['name', 'id', 'favorites']))
| name | id  |      favorites       |
| ---- | --- | -------------------- |
| Tom  | 1   | [{'name': 'Apple'}]  |
| John | 2   | [{'name': 'Orange'}] |

to_yaml(*, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to yaml string

Parameters:

ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Yaml string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> print(human.to_yaml())
favorites:
  - name: Apple
    names_by_lang:
      de: Apfel
      en: Apple
  - name: Orange
id: 1
name: Tom

to_yamlf(fpath: str, encoding: str = 'utf8', *, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to yaml file

Parameters:

fpath – Yaml file path
encoding – Yaml file encoding
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Yaml file path

TIterator¶

class owlmixin.owlcollections.TIterator(iterable: Iterable)[source]¶

to_list() → TList[T][source]¶

Usage:

>>> TIterator([1, 2, 3]).to_list()
[1, 2, 3]

next_at(index: int) → TOption[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5])
>>> it.next_at(1)
Option --> 2
>>> it.next_at(1)
Option --> 4
>>> it.next_at(1)
Option --> None

for_each(func: Callable[[T], None]) → None[source]¶

Usage:

>>> TIterator([1, 2, 3]).for_each(lambda x: print(str(x)))
1
2
3

map(func: Callable[[T], U]) → TIterator[U][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5]).map(lambda x: x+1)
>>> it.to_list()
[2, 3, 4, 5, 6]
>>> it.to_list()
[]

emap(func: Callable[[T, int], U]) → TIterator[U][source]¶

Usage:

>>> it = TIterator([10, 20, 30, 40, 50]).emap(lambda x, i: (x+1, i))
>>> it.to_list()
[(11, 0), (21, 1), (31, 2), (41, 3), (51, 4)]
>>> it.to_list()
[]

filter(func: Callable[[T], bool]) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5]).filter(lambda x: x > 3)
>>> it.to_list()
[4, 5]
>>> it.to_list()
[]

reject(func: Callable[[T], bool]) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5]).reject(lambda x: x > 3)
>>> it.to_list()
[1, 2, 3]
>>> it.to_list()
[]

flatten() → TIterator[U][source]¶

Usage:

>>> it = TIterator([[1, 2], [3, 4]]).flatten()
>>> it.to_list()
[1, 2, 3, 4]
>>> it.to_list()
[]

flat_map(func: Callable[[T], List[U]]) → TIterator[U][source]¶

Usage:

>>> it = TIterator([1, 2, 3]).flat_map(lambda x: [x, x+1])
>>> it.to_list()
[1, 2, 2, 3, 3, 4]
>>> it.to_list()
[]

head() → TOption[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5])
>>> it.head().get()
1
>>> it.head().get()
2

take(size_: int) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5]).take(3)
>>> it.to_list()
[1, 2, 3]
>>> it.to_list()
[]

take_while(func: Callable[[T], bool]) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 30, 4, 50]).take_while(lambda x: x < 10)
>>> it.to_list()
[1, 2]
>>> it.to_list()
[]

tail(size_: int) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5]).tail(3)
>>> it.to_list()
[3, 4, 5]
>>> it.to_list()
[]

uniq() → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 2, 1]).uniq()
>>> it.to_list()
[1, 2, 3]
>>> it.to_list()
[]

uniq_by(func: Callable[[T], Any] = None) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, -2, -1]).uniq_by(lambda x: x**2)
>>> it.to_list()
[1, 2, 3]
>>> it.to_list()
[]

partition(func: Callable[[T], bool]) → Tuple[TIterator[T], TIterator[T]][source]¶

Usage:

>>> ng, ok = TIterator([1, 2, 3, 4, 5]).partition(lambda x: x > 3)
>>> ng.to_list()
[1, 2, 3]
>>> ng.to_list()
[]
>>> ok.to_list()
[4, 5]
>>> ok.to_list()
[]

group_by(to_key: Callable[[T], str]) → TDict[TList[T]][source]¶

Usage:

>>> TIterator([1, 2, 3, 4, 5]).group_by(lambda x: x % 2).to_json()
'{"0": [2,4],"1": [1,3,5]}'

key_by(to_key: Callable[[T], str]) → TDict[T][source]¶

Parameters:: to_key – value -> key

Usage:

>>> TIterator(['a1', 'b2', 'c3']).key_by(lambda x: x[0]).to_json()
'{"a": "a1","b": "b2","c": "c3"}'
>>> TIterator([1, 2, 3, 4, 5]).key_by(lambda x: x % 2).to_json()
'{"0": 4,"1": 5}'

order_by(func: Callable[[T], Any], reverse: bool = False) → TIterator[T][source]¶

Usage:

>>> it = TIterator([12, 25, 31, 40, 57]).order_by(lambda x: x % 10)
>>> it.to_list()
[40, 31, 12, 25, 57]
>>> it.to_list()
[]

>>> it = TIterator([12, 25, 31, 40, 57]).order_by(lambda x: x % 10, reverse=True)
>>> it.to_list()
[57, 25, 12, 31, 40]
>>> it.to_list()
[]

concat(values: Iterable[T], first: bool = False) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2]).concat(TIterator([3, 4]))
>>> it.to_list()
[1, 2, 3, 4]
>>> it.to_list()
[]

>>> it = TIterator([1, 2]).concat([3, 4], first=True)
>>> it.to_list()
[3, 4, 1, 2]
>>> it.to_list()
[]

reduce(func: Callable[[U, T], U], init_value: U) → U[source]¶

Usage:

>>> TIterator([1, 2, 3, 4, 5]).reduce(lambda t, x: t + 2*x, 100)
130

sum() → Union[int, float][source]¶

Usage:

>>> TIterator([1, 2, 3, 4, 5]).sum()
15

sum_by(func: Callable[[T], Union[int, float]]) → Union[int, float][source]¶

Usage:

>>> TIterator([1, 2, 3, 4, 5]).sum_by(lambda x: x*2)
30

count_by(func: Callable[[T], Any]) → TDict[int][source]¶

Usage:

>>> it = TIterator([1, 11, 25, 35, 21, 4])
>>> it.count_by(lambda x: x % 10)
{1: 3, 5: 2, 4: 1}
>>> it.to_list()
[]

join(joint: str) → str[source]¶

Usage:

>>> TIterator(['A', 'B', 'C']).join("-")
'A-B-C'

unlines(*, crlf: bool = False) → str[source]¶

Usage:

>>> TIterator(['aaa', 'bbb', 'ccc']).unlines()
'aaa\nbbb\nccc'
>>> TIterator(['A', 'B', 'C']).unlines(crlf=True)
'A\r\nB\r\nC'

find(func: Callable[[T], bool]) → TOption[T][source]¶

Usage:

>>> TIterator([1, 2, 3, 4, 5]).find(lambda x: x > 3)
Option --> 4
>>> TIterator([1, 2, 3, 4, 5]).find(lambda x: x > 6)
Option --> None

all(func: Callable[[T], bool]) → bool[source]¶

Usage:

>>> TIterator([1, 2, 3, 4, 5]).all(lambda x: x > 0)
True
>>> TIterator([1, 2, 3, 4, 5]).all(lambda x: x > 1)
False

any(func: Callable[[T], bool]) → bool[source]¶

Usage:

>>> TIterator([1, 2, 3, 4, 5]).any(lambda x: x > 4)
True
>>> TIterator([1, 2, 3, 4, 5]).any(lambda x: x > 5)
False

intersection(values: Iterable[T]) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5]).intersection([2, 4, 6])
>>> it.to_list()
[2, 4]
>>> it.to_list()
[]

not_intersection(values: Iterable[T]) → TIterator[T][source]¶

Usage:

>>> it = TIterator([1, 2, 3, 4, 5]).not_intersection([2, 4, 6])
>>> it.to_list()
[1, 3, 5]
>>> it.to_list()
[]

to_csv(fieldnames: Sequence[str], *, with_header: bool = False, crlf: bool = False, tsv: bool = False) → str¶

From sequence of text to csv string

Parameters:

fieldnames – Order of columns by property name
with_header – Add headers at the first line if True
crlf – Add CRLF line break at the end of line if True, else add LF
tsv – Use tabs as separator if True, else use comma

Returns:

Csv string

Usage:

>>> from owlmixin.samples import Human
>>> humans = Human.from_dicts([
...     {"id": 1, "name": "Tom", "favorites": [{"name": "Apple"}]},
...     {"id": 2, "name": "John", "favorites": [{"name": "Orange"}]}
... ])

>>> print(humans.to_csv(fieldnames=['name', 'id', 'favorites']))
Tom,1,[{'name': 'Apple'}]
John,2,[{'name': 'Orange'}]

>>> print(humans.to_csv(fieldnames=['name', 'id', 'favorites'], with_header=True))
name,id,favorites
Tom,1,[{'name': 'Apple'}]
John,2,[{'name': 'Orange'}]

to_csvf(fpath: str, fieldnames: Sequence[str], *, encoding: str = 'utf8', with_header: bool = False, crlf: bool = False, tsv: bool = False) → str¶

From instance to yaml file

Parameters:

fpath – Csv file path
fieldnames – Order of columns by property name
encoding – Csv file encoding
with_header – Add headers at the first line if True
crlf – Add CRLF line break at the end of line if True, else add LF
tsv – Use tabs as separator if True, else use comma

Returns:

Csv file path

to_dicts(*, ignore_none: bool = True, force_value: bool = True, ignore_empty: bool = False) → List[dict]¶

From instance to dict

Parameters:

ignore_none – Properties which is None are excluded if True
force_value – Transform to value using to_value (default: str()) of ValueTransformer which inherited if True
ignore_empty – Properties which is empty are excluded if True

Returns:

List[Dict]

Usage:

>>> from owlmixin.samples import Human, Food
>>> human_dicts = [
...     {
...         "id": 1,
...         "name": "Tom",
...         "favorites": [
...             {"name": "Apple", "names_by_lang": {"en": "Apple"}}
...         ]
...     },
...     {
...         "id": 2,
...         "name": "John",
...         "favorites": [
...             {"name": "Orange", "names_by_lang": {"en": "Orange"}}
...         ]
...     }
... ]
>>> Human.from_dicts(human_dicts).to_dicts() == human_dicts
True

You can include None properties by specifying False for ignore_none

>>> f = Food.from_dicts([{"name": "Apple"}]).to_dicts(ignore_none=False)
>>> f[0]["name"]
'Apple'
>>> "names_by_lang" in f[0]
True
>>> f[0]["names_by_lang"]

As default

>>> f = Food.from_dicts([{"name": "Apple"}]).to_dicts()
>>> f[0]["name"]
'Apple'
>>> "names_by_lang" in f[0]
False

You can exclude Empty properties by specifying True for ignore_empty

>>> f = Human.from_dicts([{"id": 1, "name": "Ichiro", "favorites": []}]).to_dicts()
>>> f[0]["favorites"]
[]
>>> f = Human.from_dicts([{"id": 1, "name": "Ichiro", "favorites": []}]).to_dicts(ignore_empty=True)
>>> "favorites" in f[0]
False

to_json(*, indent: int = None, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to json string

Parameters:

indent – Number of indentation
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> human.to_json()
'{"favorites": [{"name": "Apple","names_by_lang": {"de": "Apfel","en": "Apple"}},{"name": "Orange"}],"id": 1,"name": "Tom"}'

to_jsonf(fpath: str, encoding: str = 'utf8', *, indent: int = None, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to json file

Parameters:

fpath – Json file path
encoding – Json file encoding
indent – Number of indentation
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json file path

to_pretty_json(*, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to pretty json string

Parameters:

ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> print(human.to_pretty_json())
{
    "favorites": [
        {
            "name": "Apple",
            "names_by_lang": {
                "de": "Apfel",
                "en": "Apple"
            }
        },
        {
            "name": "Orange"
        }
    ],
    "id": 1,
    "name": "Tom"
}

to_table(fieldnames: Sequence[str]) → str¶

From sequence of text to csv string

Parameters:: fieldnames – Order of columns by property name
Returns:: Table string

Usage:

>>> from owlmixin.samples import Human
>>> humans = Human.from_dicts([
...     {"id": 1, "name": "Tom", "favorites": [{"name": "Apple"}]},
...     {"id": 2, "name": "John", "favorites": [{"name": "Orange"}]}
... ])
>>> print(humans.to_table(fieldnames=['name', 'id', 'favorites']))
| name | id  |      favorites       |
| ---- | --- | -------------------- |
| Tom  | 1   | [{'name': 'Apple'}]  |
| John | 2   | [{'name': 'Orange'}] |

to_yaml(*, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to yaml string

Parameters:

ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Yaml string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> print(human.to_yaml())
favorites:
  - name: Apple
    names_by_lang:
      de: Apfel
      en: Apple
  - name: Orange
id: 1
name: Tom

to_yamlf(fpath: str, encoding: str = 'utf8', *, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to yaml file

Parameters:

fpath – Yaml file path
encoding – Yaml file encoding
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Yaml file path

TDict¶

class owlmixin.owlcollections.TDict[source]¶

map(func: Callable[[K, T], U]) → TIterator[U][source]¶

Usage:

>>> it = TDict(k1=1, k2=2, k3=3).map(lambda k, v: v*2)
>>> sorted(it.to_list())
[2, 4, 6]
>>> it.to_list()
[]

map_values(func: Callable[[T], U]) → TDict[U][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).map_values(lambda x: x*2) == {
...     "k1": 2,
...     "k2": 4,
...     "k3": 6
... }
True

map_values2(func: Callable[[K, T], U]) → TDict[U][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).map_values2(lambda k, v: f'{k} -> {v*2}') == {
...     "k1": "k1 -> 2",
...     "k2": "k2 -> 4",
...     "k3": "k3 -> 6"
... }
True

filter(func: Callable[[K, T], bool]) → TIterator[T][source]¶

Usage:

>>> it = TDict(k1=1, k2=2, k3=3).filter(lambda k, v: v < 2)
>>> it.to_list()
[1]
>>> it.to_list()
[]

reject(func: Callable[[K, T], bool]) → TIterator[T][source]¶

Usage:

>>> it = TDict(k1=1, k2=2, k3=3).reject(lambda k, v: v < 3)
>>> it.to_list()
[3]
>>> it.to_list()
[]

sum() → Union[int, float][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).sum()
6

sum_by(func: Callable[[K, T], Union[int, float]]) → Union[int, float][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).sum_by(lambda k, v: v*2)
12

size() → int[source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).size()
3

find(func: Callable[[K, T], bool]) → TOption[T][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).find(lambda k, v: v == 2)
Option --> 2
>>> TDict(k1=1, k2=2, k3=3).find(lambda k, v: v == 4)
Option --> None

to_list() → TList[T][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).to_list().order_by(lambda x: x)
[1, 2, 3]

to_iterator() → TIterator[T][source]¶

Usage:

>>> it = TDict(k1=1, k2=2, k3=3).to_iterator().order_by(lambda x: x)
>>> it.to_list()
[1, 2, 3]
>>> it.to_list()
[]

all(func: Callable[[K, T], bool]) → bool[source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).all(lambda k, v: v > 0)
True
>>> TDict(k1=1, k2=2, k3=3).all(lambda k, v: v > 1)
False

any(func: Callable[[K, T], bool]) → bool[source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).any(lambda k, v: v > 2)
True
>>> TDict(k1=1, k2=2, k3=3).any(lambda k, v: v > 3)
False

assign(dict_: Dict[str, T]) → TDict[T][source]¶

Usage:

>>> TDict(k1=1, k2=2).assign({'k3': 3})
{'k1': 1, 'k2': 2, 'k3': 3}
>>> TDict(k1=1, k2=2).assign(TDict({'k2': 3}))
{'k1': 1, 'k2': 3}

pick_by(func: Callable[[K, T], bool]) → TDict[T][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).pick_by(lambda k, v: v > 2)
{'k3': 3}

omit_by(func: Callable[[K, T], bool]) → TDict[T][source]¶

Usage:

>>> TDict(k1=1, k2=2, k3=3).omit_by(lambda k, v: v > 2)
{'k1': 1, 'k2': 2}

str_format(format_: str) → str¶

From instance to str with formatting

Parameters:: format – format string
Returns:: str

Usage:

>>> from owlmixin.samples import Human, Food
>>> human_dict = {
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple"}}
...     ]
... }
>>> Human.from_dict(human_dict).str_format('{id}: {name}')
'1: Tom'

to_dict(*, ignore_none: bool = True, force_value: bool = True, ignore_empty: bool = False) → dict¶

From instance to dict

Parameters:

ignore_none – Properties which is None are excluded if True
force_value – Transform to value using to_value (default: str()) of ValueTransformer which inherited if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Dict

Usage:

>>> from owlmixin.samples import Human, Food
>>> human_dict = {
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple"}}
...     ]
... }
>>> Human.from_dict(human_dict).to_dict() == human_dict
True

You can include None properties by specifying False for ignore_none

>>> f = Food.from_dict({"name": "Apple"}).to_dict(ignore_none=False)
>>> f["name"]
'Apple'
>>> "names_by_lang" in f
True
>>> f["names_by_lang"]

As default

>>> f = Food.from_dict({"name": "Apple"}).to_dict()
>>> f["name"]
'Apple'
>>> "names_by_lang" in f
False

You can exclude Empty properties by specifying True for ignore_empty

>>> f = Human.from_dict({"id": 1, "name": "Ichiro", "favorites": []}).to_dict()
>>> f["favorites"]
[]
>>> f = Human.from_dict({"id": 1, "name": "Ichiro", "favorites": []}).to_dict(ignore_empty=True)
>>> "favorites" in f
False

to_json(*, indent: int = None, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to json string

Parameters:

indent – Number of indentation
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> human.to_json()
'{"favorites": [{"name": "Apple","names_by_lang": {"de": "Apfel","en": "Apple"}},{"name": "Orange"}],"id": 1,"name": "Tom"}'

to_jsonf(fpath: str, encoding: str = 'utf8', *, indent: int = None, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to json file

Parameters:

fpath – Json file path
encoding – Json file encoding
indent – Number of indentation
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json file path

to_pretty_json(*, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to pretty json string

Parameters:

ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Json string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> print(human.to_pretty_json())
{
    "favorites": [
        {
            "name": "Apple",
            "names_by_lang": {
                "de": "Apfel",
                "en": "Apple"
            }
        },
        {
            "name": "Orange"
        }
    ],
    "id": 1,
    "name": "Tom"
}

to_yaml(*, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to yaml string

Parameters:

ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Yaml string

Usage:

>>> from owlmixin.samples import Human
>>> human = Human.from_dict({
...     "id": 1,
...     "name": "Tom",
...     "favorites": [
...         {"name": "Apple", "names_by_lang": {"en": "Apple", "de": "Apfel"}},
...         {"name": "Orange"}
...     ]
... })
>>> print(human.to_yaml())
favorites:
  - name: Apple
    names_by_lang:
      de: Apfel
      en: Apple
  - name: Orange
id: 1
name: Tom

to_yamlf(fpath: str, encoding: str = 'utf8', *, ignore_none: bool = True, ignore_empty: bool = False) → str¶

From instance to yaml file

Parameters:

fpath – Yaml file path
encoding – Yaml file encoding
ignore_none – Properties which is None are excluded if True
ignore_empty – Properties which is empty are excluded if True

Returns:

Yaml file path

Collections¶

TList¶

TIterator¶

TDict¶

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