Pipelines in Functional Programming

from functools import *

bands = [{'name': 'sunset rubdown', 'country': 'UK', 'active': False},
         {'name': 'women', 'country': 'Germany', 'active': False},
         {'name': 'a silver mt. zion', 'country': 'Spain', 'active': True}]

def assoc(_d, key, value):
    from copy import deepcopy
    d = deepcopy(_d)
    d[key] = value
    return d

def call(fn, key): #this one does not do anything
    def apply_fn(record): #this is generated function
        return assoc(record, key, fn(record.get(key))) #record=band
    return apply_fn

def pluck(keys):
    def pluck_fn(record):
        return reduce(lambda elements, key: assoc(elements, key, record[key]), keys, {})
    return pluck_fn

def pipeline_each(data, functions):
    return reduce(lambda elements, fn: map(fn, elements), #this _elements_ are the data after transformation
                  functions,
                  data)

set_canada_as_country = call(lambda x: 'Canada', 'country')
strip_punctuation_from_name = call(lambda x: x.replace('.', ''), 'name')
capitalize_names = call(str.title, 'name')
extract_names_and_countries = pluck(['name', 'country'])

new_bands = pipeline_each(bands, [set_canada_as_country,
                                  strip_punctuation_from_name,
                                  capitalize_names,
                                  extract_names_and_countries])

for band in new_bands:
    print(band)