{}
# Online Python compiler (interpreter) to run Python online.
# Write Python 3 code in this online editor and run it.
from functools import reduce
from collections import Counter
from operator import itemgetter
# Sample data
countries = ['Estonia', 'Finland', 'Sweden', 'Denmark', 'Norway', 'Iceland']
names = ['Asabeneh', 'Lidiya', 'Ermias', 'Abraham']
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# -----------------------------------
# MAP, FILTER, REDUCE BASICS
# -----------------------------------
# map: apply transformation
countries_upper = list(map(str.upper, countries))
numbers_squared = list(map(lambda x: x**2, numbers))
names_upper = list(map(str.upper, names))
# filter: filter based on condition
countries_with_land = list(filter(lambda x: 'land' in x, countries))
six_char_countries = list(filter(lambda x: len(x) == 6, countries))
six_or_more = list(filter(lambda x: len(x) >= 6, countries))
starting_with_E = list(filter(lambda x: x.startswith('E'), countries))
# reduce: combine to a single result
total_sum = reduce(lambda x, y: x + y, numbers)
sentence = reduce(lambda x, y: x + ', ' + y, countries[:-1]) + f", and {countries[-1]} are north European countries"
# chaining
chained_result = reduce(lambda x, y: x + y, filter(lambda x: x > 10, map(lambda x: x ** 2, numbers)))
# -----------------------------------
# HIGHER-ORDER FUNCTION, CLOSURE, DECORATOR
# -----------------------------------
# higher-order function
def apply_twice(fn, value):
return fn(fn(value))
# closure
def multiplier(factor):
def multiply(x):
return x * factor
return multiply
double = multiplier(2)
# decorator
def my_decorator(fn):
def wrapper():
print("Before call")
fn()
print("After call")
return wrapper
@my_decorator
def say_hello():
print("Hello!")
# -----------------------------------
# FOR LOOPS
# -----------------------------------
for country in countries:
print(country)
for name in names:
print(name)
for number in numbers:
print(number)
# -----------------------------------
# FUNCTION UTILITIES
# -----------------------------------
def get_string_lists(lst):
return list(filter(lambda x: isinstance(x, str), lst))
def categorize_countries(pattern):
return [country for country in countries if pattern in country]
def country_starting_letters_dict(countries):
result = {}
for country in countries:
first = country[0]
result[first] = result.get(first, 0) + 1
return result
def get_first_ten_countries(lst):
return lst[:10]
def get_last_ten_countries(lst):
return lst[-10:]
# -----------------------------------
# LEVEL 3 - countries_data mock
# -----------------------------------
countries_data = [
{'name': 'Finland', 'capital': 'Helsinki', 'population': 5527573, 'languages': ['Finnish', 'Swedish']},
{'name': 'Sweden', 'capital': 'Stockholm', 'population': 10099265, 'languages': ['Swedish']},
{'name': 'Norway', 'capital': 'Oslo', 'population': 5421241, 'languages': ['Norwegian']},
{'name': 'Denmark', 'capital': 'Copenhagen', 'population': 5831404, 'languages': ['Danish']},
{'name': 'Iceland', 'capital': 'Reykjavik', 'population': 343599, 'languages': ['Icelandic']},
{'name': 'Estonia', 'capital': 'Tallinn', 'population': 1326535, 'languages': ['Estonian']},
{'name': 'Germany', 'capital': 'Berlin', 'population': 83783942, 'languages': ['German']},
{'name': 'Russia', 'capital': 'Moscow', 'population': 145912025, 'languages': ['Russian']},
{'name': 'India', 'capital': 'New Delhi', 'population': 1393409038, 'languages': ['Hindi', 'English']},
{'name': 'China', 'capital': 'Beijing', 'population': 1444216107, 'languages': ['Mandarin']},
{'name': 'USA', 'capital': 'Washington, D.C.', 'population': 331002651, 'languages': ['English']},
]
# sort by name, capital, population
sorted_by_name = sorted(countries_data, key=itemgetter('name'))
sorted_by_capital = sorted(countries_data, key=itemgetter('capital'))
sorted_by_population = sorted(countries_data, key=itemgetter('population'), reverse=True)
# top 10 spoken languages
all_languages = []
for country in countries_data:
all_languages.extend(country['languages'])
top_languages = Counter(all_languages).most_common(10)
# top 10 populated countries
top_populated = sorted_by_population[:10]
# -----------------------------------
# DISPLAY SAMPLE OUTPUTS
# -----------------------------------
print("Countries Uppercase:", countries_upper)
print("Squared Numbers:", numbers_squared)
print("Names Uppercase:", names_upper)
print("Countries with 'land':", countries_with_land)
print("Countries with 6 letters:", six_char_countries)
print("Countries >= 6 letters:", six_or_more)
print("Countries starting with 'E':", starting_with_E)
print("Chained Result:", chained_result)
print("Total Sum:", total_sum)
print("Sentence:", sentence)
print("Get Strings Only:", get_string_lists([1, 'a', 3.5, 'b', True]))
print("Categorized (land):", categorize_countries('land'))
print("Country Start Dict:", country_starting_letters_dict(countries))
print("First 10 Countries:", get_first_ten_countries(countries))
print("Last 10 Countries:", get_last_ten_countries(countries))
print("Top Languages:", top_languages)
print("Top Populated Countries:", [c['name'] for c in top_populated])