handyobj 0.1.3

Add your description here

Handy

Installation

pip install handyobj

Why you might consider using this package ?

In javascript when you work with arrays, you can do some chaining operation in a functional programming style like :

jsList = [1, 2, 3, 4]
console.log(jsList.map(el => el * 2))
>>> [ 2, 4, 6, 8 ]
jsList = [1, 2, 3, 4]
console.log(jsList.filter(el => el % 2 == 0).map(el => el + 1))
[ 3, 5 ]

Python offers functools module with map and filter but they are not as handy as in javascript when we want to chain them in FP style.

This is why i'm proposing this package that offers 2 objects, SmartList that act as a simple wrapper on standard list and offers methods like (map,sort, filter_by_redicates, filter_by_val_attributes etc...) and ObjectDict that act as a wrapper on a normal dict, and allow access to it's values directly as if they were attributes of the object (works also with nested dict).

So SmartList used in combination with ObjectDict offers you the same experience as you would have in javascript. In some scenarios i found this pattern really handy to extract the data i want from 3rd party apis when they were not granular enough.

Usage

# Condider we have the following data list of dict
ldict = [{'name': 'Kenneth Thomas',
  'job': 'Broadcast presenter',
  'company': 'Williams-Miller'},
 {'name': 'Edward Hamilton',
  'job': 'Quarry manager',
  'company': 'Anderson-Kline'},
 {'name': 'Brandon Weeks',
  'job': 'Theme park manager',
  'company': 'Williams, Garcia and Allen'},
 {'name': 'Kim Little',
  'job': 'Insurance risk surveyor',
  'company': 'Frank-Hutchinson'},
 {'name': 'David Gutierrez',
  'job': 'Community pharmacist',
  'company': 'Allen, Herman and Ellis'},
 {'name': 'Matthew Cook',
  'job': 'Chief Operating Officer',
  'company': 'Hansen-Young'},
 {'name': 'Mark Watson',
  'job': 'Doctor, general practice',
  'company': 'Vargas PLC'},
 {'name': 'Jessica Ellis',
  'job': 'Tourist information centre manager',
  'company': 'Williams-Reeves'},
 {'name': 'Jessica Duncan',
  'job': 'Administrator, Civil Service',
  'company': 'Hobbs Group'},
 {'name': 'Diana Butler',
  'job': 'Scientist, research (maths)',
  'company': 'Olsen, Maddox and Smith'}]

# Now consider i want extract just people that work as managers, here is how i do 

from handyobj import SmartList, ObjectDict

managers = SmartList(ldict).\
    map(lambda el : ObjectDict(el)).\
    filter_by_predicates(lambda el : "manager" in  el.job.lower())


>>> [{'name': 'Edward Hamilton',
  'job': 'Quarry manager',
  'company': 'Anderson-Kline'},
 {'name': 'Brandon Weeks',
  'job': 'Theme park manager',
  'company': 'Williams, Garcia and Allen'},
 {'name': 'Jessica Ellis',
  'job': 'Tourist information centre manager',
  'company': 'Williams-Reeves'}]

# There are more things you can do with the methods of smart list, like aggregation by predicates, or check attribute equality or pattern matching : 


# len -> gives you lenght of your list 
# map -> map your list to a new list
# first -> give you first element of list (index error if doesnt exist)
# last -> give you last element of list (index error if doesnt exist)
# last_or_none  -> give you last element of list (None if doesn't exist)
# first_or_none  -> give you first element of list (None if doesn't exist)
# one_or_none -> one element or None otherwise
# sort -> sort your list to a new list 
# filter_by_predicates -> filter by predicates (functions that return true or false)
# filter_by_attribute_values -> filter by keyword attribute values()
# filter_by_matched_attributes -> filter by keyword patterns values()
# group_by_labeled_predicates -> group by [(label, predicate)], you obtain a SmartList of tuples [(label, filtered_smart_list)]