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Create data structures from dictionaries.

Project description


Create data structures from dictionaries.


  • Transform dicts to attr.s, dataclass and NamedTuple
  • Supports nested structures when using typing.List and typing.Dict type hints.
  • Insert additional fields existing in dict into structure with fd_copy_unknown=True
  • Optional run-time type-checking with fd_check_types=True
  • Supports forward references since 0.2.1


from dataclasses import dataclass  # or import attr.dataclass for Python < 3.7
from typing import List, Optional
from from_dict import from_dict

class Preference:
    name: str
    score: int

class Customer:
    name: str
    nick_name: Optional[str]
    preferences: List[Preference]

input_customer_data = {
    "name": "Christopher Lee",
    "nick_name": None,
    "preferences": [
        { "name": "The Hobbit", "score": 37 },
        { "name": "Count Dooku", "score": 2 },
        { "name": "Saruman", "score": 99 }
    "friend": "Mellon"

customer = from_dict(Customer, input_customer_data)
# Structured data is available as attributes since attr.s exposes them like that
assert == "Christopher Lee"
# Nested structures are also constructed. List[sub_strucutre] and Dict[key, sub_structure] are supported
assert customer.preferences[0].name == "The Hobbit"
# Data not defined in the strucutre is inserted into the __dict__ if possible
assert customer.__dict__["friend"] == "Mellon"

Use cases

from-dict is especially useful when used on big and partially known data structures like JSON. Since undefined structure is ignored, we can use from-dict to avoid try-catch and KeyError hell:

Assume we want to interact with the Google GeoCoding API (cf.

The JSON that is returned on requests contains some keys that we are not interested in. So we create data-structures that contain the keys that we actually want to use:

from dataclasses import dataclass
from typing import List

class AddressComponent:
    long_name: str
    short_name: str
    types: List[str]

class Result:
    address_components: List[AddressComponent]
    formatted_address: str

class Response:
    results: List[Result]

With that, given the response of the API, we can extract the fields and ignore everything else.

from from_dict import from_dict

# This will throw a TypeError if something goes wrong.
structured_response: Response = from_dict(Response, 
                                          fd_check_types=True,   # Do check types at run-time
                                          fd_copy_unknown=False  # Do not copy undefined data to __dict__

# Now, we can access the data in a statically known manner
for res in structured_response.results:
    print(f"The formatted address is {res.formatted_address}")
    for addr_comp in res.address_components:
        print(f"Component {addr_comp.long_name}")

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