In this lab, we'll put our new knowledge about map and filter to the test. We'll also get back to working with Yelp again. Let's get started!
from restaurants import yelp_restaurants # in this line we are simply importing our data we gathered from Yelp.restaurants = list(map(lambda restaurant: dict(name=restaurant['name'],
price=restaurant['price'],
is_closed=restaurant['is_closed'],
review_count=restaurant['review_count'],
), yelp_restaurants))We have a list of five restaurants from the Yelp Api. Let's take a look at the list.
restaurantsAs you can see, it's a little tricky to see the names of all of the restaurants. Assign a variable names to equal the list of names of the functions. Use the map function to do so.
names = None
names
# ['Fork & Fig',
# 'Salt And Board',
# 'Frontier Restaurant',
# 'Nexus Brewery',
# "Devon's Pop Smoke",
# 'Cocina Azul',
# 'Philly Steaks',
# 'Stripes Biscuit']Let's get a sense of how many reviews were written for each of the restaurants. Assign a variable review_counts to equal a list of the review_count for each restaurant.
review_counts = None
review_counts # [610, 11, 1373, 680, 54, 647, 25, 20]Now add up the elements in the list, and assign the result to a variable named total_reviews.
total_reviews = None
total_reviews # 3420It's a little tricky to work with the price in the format of dollars signs. So write a function called format_restaurants that changes each restaurant to have the attribute 'price' point to the number of dollar signs. We'll get you started with the function, format_restaurant.
def format_restaurant(restaurant):
if type(restaurant['price']) == str:
restaurant['price'] = len(restaurant['price'])
return restaurantformat_restaurant(restaurants[0]) # {'is_closed': False, 'name': 'Fork & Fig', 'price': 2, 'review_count': 610}Now write a function called format_restaurants, that returns a list of restaurants with each of them formatted with price pointing to the respective number.
def format_restaurants(restaurants):
passformat_restaurants(restaurants)
# [{'is_closed': False, 'name': 'Fork & Fig', 'price': 2, 'review_count': 610},
# {'is_closed': False,
# 'name': 'Salt And Board',
# 'price': 2,
# 'review_count': 11},
# {'is_closed': False,
# 'name': 'Frontier Restaurant',
# 'price': 1,
# 'review_count': 1373},
# {'is_closed': False,
# 'name': 'Nexus Brewery',
# 'price': 2,
# 'review_count': 680},
# {'is_closed': False,
# 'name': "Devon's Pop Smoke",
# 'price': 2,
# 'review_count': 54},
# {'is_closed': True, 'name': 'Cocina Azul', 'price': 2, 'review_count': 647},
# {'is_closed': False, 'name': 'Philly Steaks', 'price': 2, 'review_count': 25},
# {'is_closed': True,
# 'name': 'Stripes Biscuit',
# 'price': 2,
# 'review_count': 20}]Now let's search for restaurants based on specific criteria.
Write a function called open_restaurants that takes in a list of restaurants and only returns those that are open.
def open_restaurants(restaurants):
passopen_restaurants(restaurants)
# [{'is_closed': False, 'name': 'Fork & Fig', 'price': 2, 'review_count': 610},
# {'is_closed': False,
# 'name': 'Salt And Board',
# 'price': 2,
# 'review_count': 11},
# {'is_closed': False,
# 'name': 'Frontier Restaurant',
# 'price': 1,
# 'review_count': 1373},
# {'is_closed': False,
# 'name': 'Nexus Brewery',
# 'price': 2,
# 'review_count': 680},
# {'is_closed': False,
# 'name': "Devon's Pop Smoke",
# 'price': 2,
# 'review_count': 54},
# {'is_closed': False, 'name': 'Philly Steaks', 'price': 2, 'review_count': 25}]Now write a function called cheapest_restaurants that returns the restaurants that have a price of 1, or '$'.
def cheapest_restaurants(restaurants):
passcheapest_restaurants(restaurants)
# [{'is_closed': False,
# 'name': 'Frontier Restaurant',
# 'price': 1,
# 'review_count': 1373}]Next, write a function that filters out only those restaurants that have 100 reviews or more, since we want to make sure there are some solid data points backing the reviews -- we are burgeoning data scientists after all!
def sufficiently_reviewed_restaurants(restaurants):
passsufficiently_reviewed_restaurants(restaurants)
# [{'name': 'Fork & Fig', 'price': 2, 'is_closed': False, 'review_count': 610},
# {'name': 'Frontier Restaurant', 'price': 1, 'is_closed': False, 'review_count': 1373},
# {'name': 'Nexus Brewery', 'price': 2, 'is_closed': False, 'review_count': 680},
# {'name': 'Cocina Azul', 'price': 2, 'is_closed': True, 'review_count': 647}]Neat! In this lab, we successfully proved our prowess when it comes to iterating over each element of a list with both map and filter! We used map to format our data into ways that better help us answer questions and extrapolate insights. We used filter to return subsets of our data like our restaurants that were only one $ or our restaurants that had 100 or more reviews.
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