functional-streams 1.0.0

Functional Programming Streams ,Similar like Java, for writing concise functions

functional-streams

Streams in Python similar like java for writing concise code

#To Fetch from a list of users
#       Get their firstname , if their salary greater than 80000 and gender is male

#Instead of writing like this

list(map(lambda user: user['first_name'],  filter(lambda user:user['salary'] > 80000, filter(lambda product: product['gender'] == 'Male',users))))

#Write this
from streams.Stream import Stream

results = (Stream
           .create(users)
           .filter(lambda user:user['salary'] > 80000)
           .filter(lambda product: product['gender'] == 'Male')
           .map(lambda user: user['first_name'])
           .asList())

#A concise way to write lambdas,functional code in python

from streams.Stream import Stream
users = [
    {
        "id": 1,
        "first_name": "Mandy",
        "last_name": "Gowan",
        "email": "[e-mail]",
        "gender": "Female",
        "loves": ['Soccer','Cricket','Golf'],
        "salary": 119885
    },
    {
        "id": 2,
        "first_name": "Janessa",
        "last_name": "Cotterell",
        "email": "[e-mail]",
        "gender": "Female",
        "loves": ['Cricket'],
        "salary": 107629
    },
    {
        "id": 6,
        "first_name": "Jasen",
        "last_name": "Franzini",
        "email": "[e-mail]",
        "gender": "Male",
        "loves": ['Soccer','Golf'],
        "salary": 78373
    }
]

#Using Map Filter 
results = (Stream
           .create(users)
           .filter(lambda user:user['salary'] > 80000)
           .map(lambda user: user['first_name'])
           .asList())
#['Mandy', 'Janessa']

#Using flatMap Distinct 
results = (Stream
           .create(users)
           .flatmap(lambda user:user['loves'] )
           .distinct()
           .asList())
#['Cricket', 'Golf', 'Soccer']

#Using skip take 
results = (Stream
           .create(users)
           .skip(1)
           .take(1)
           .map(lambda user: user['first_name'])
           .asList())
#['Janessa']


#Even you can peek results
results = (Stream
           .create(users)
           .peek(lambda data:print("User",data))
           .map(lambda user: user['first_name'])
           .asList())
#Will list out all users

Additional Information

Design

Most of the functions underneath uses the same functions available in python (map uses map , filter uses filter etc..). Only we have added wrapper to make the code concise

Abstractions

If you need to use partial abstraction , try using stream method. as the generators used get corrupted by the very first expansion For Example

stream_of_users = (Stream
                    .create(users)
                    )

#The below code wont work
total_users = (stream_of_users
               .length())

firstname_of_users = (stream_of_users           
                           .map(lambda user: user['first_name'])
                           .asList())


#The above code should be rewritten as
total_users = (stream_of_users
               .length())

firstname_of_users = (stream_of_users
                            .stream()
                           .map(lambda user: user['first_name'])
                           .asList())

# The stream will make use of copying the items which has been expanded