dacite | Simple creation of data classes from dictionaries | JSON Processing library
kandi X-RAY | dacite Summary
kandi X-RAY | dacite Summary
This module simplifies creation of data classes (PEP 557) from dictionaries.
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Top functions reviewed by kandi - BETA
- Create a new instance from a dict .
- Checks if value is an instance of type_ .
- Transform value based on type hooks .
- Build the value for a given union .
- Build a value for a given collection .
- Build a value from data .
- Checks if the given type is a generic collection type .
- Returns the default value for a field .
- Extract generic arguments .
- Checks if the given type is a literal .
dacite Key Features
dacite Examples and Code Snippets
python==3.8.2
torch==1.4.0
torchvision==0.5.0
numpy==1.18.1
pandas==1.0.3
Pillow==7.2.0
h5py==2.10.0
matplotlib==3.1.3
seaborn==0.10.1
bidict==0.19.0
dacite==1.5.0
nltk==3.4.5
pycocotools==2.0.0
tqdm==4.43.0
attrs==19.3.0
attr==0.3.1
rouge_score==0.0
from dataclasses import dataclass
from dacite import from_dict
@dataclass
class User:
name: str
age: int
is_active: bool
data = {
'name': 'john',
'age': 30,
'is_active': True,
}
user = from_dict(data_class=User, data=data
Copyright (c) 2017, Dennis Hamester
Permission to use, copy, modify, and/or distribute this software for any
purpose with or without fee is hereby granted, provided that the above
copyright notice and this permission notice appear in all copies.
T
from __future__ import annotations
from dataclasses import dataclass
from dataclass_wizard import JSONWizard
@dataclass(frozen=True)
class Data(JSONWizard):
parameters: dict[str, TimeSeries]
@dataclass(frozen=True)
class TimeSerie
from dataclasses import dataclass
from dacite import from_dict
@dataclass
class C:
a:int = 0
d1 = {'a':3}
c1: C = from_dict(C,d1)
print(c1)
print(C.__annotations__)
d2 = {'a':'3'}
c2: C = from_dict(C,d2)
C(a=
from dataclasses import dataclass, asdict
from typing import List
from dacite import from_dict
@dataclass
class Address(object):
city: str
postcode: str
@dataclass
class Person():
name: str
addresses: List[Ad
from dataclasses import dataclass
from datetime import datetime
from uuid import UUID
from typing import List
from dacite import from_dict
@dataclass
class BusinessUnit:
code: int
type: str
@dataclass
clas
def find_anagrams(seek_word):
sorted_seek_word = sorted(seek_word.lower())
for word in open("/usr/share/dict/words"):
word = word.strip() # remove trailing newline
sorted_word = sorted(word.lower())
if sort
from dataclasses import dataclass
from dacite import from_dict
@dataclass
class User:
name: str
age: int
is_active: bool
data = {
'name': 'john',
'age': 30,
'is_active': True,
}
user = from_dict(data_class=User,
from dacite import from_dict
def create_objects(**kwargs):
obj1 = from_dict(data_class=Object1, data=kwargs)
obj2 = from_dict(data_class=Object2, data=kwargs)
Community Discussions
Trending Discussions on dacite
QUESTION
I am using dacite to transform a Python dictionary into a dataclass. Is there a way to dynamically add fields to a dataclass? Like in the example below, where the dataclass "Parameters" has defined only one timeseries "timeseriesA", but there might be additional ones (provided through the dictionary) that cannot be declared.
...ANSWER
Answered 2022-Mar-04 at 05:41In general, dynamically adding fields to a dataclass, after the class is defined, is not good practice. However, this does present a good use case for using a dict
within a dataclass, due to the dynamic nature of fields in the source dict
object.
Here is a straightforward example of using a dict
field to handle a dynamic mapping of keys in the source object, using the dataclass-wizard
which is also a similar JSON serialization library. The approach outlined below handles extraneous data in the dict object like timeseriesB
for instance.
QUESTION
For getting the list of installed libraries, I run the following command in Jupyter Notebook:
...ANSWER
Answered 2020-Nov-17 at 11:03We can use os
module to create the pip list, then we use pandas.read_csv
with \s+
as seperator to read the pip list into a dataframe:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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