In this lesson, we have decided to visit one of our travel destinations! This time we have chosen to visit Albuquerque, but we aren't very familiar with this city and are quite hungry after our long flight. We will be working with information we pulled from the Yelp database to help us find a restaurant where we can satisfy our hunger. While Yelp is great for learning about what to do in Albuquerque, it gives us back a lot of information. We'll use what we know about functions and dictionaries to format and read our data more easily.
You will be able to:
- Create and use custom functions with arguments
- Understand how function arguments can make functions more flexible and reusable
Let's take a quick look at the information Yelp provides for a single restaurant:
fork_fig = {'categories': [{'alias': 'burgers', 'title': 'Burgers'},
{'alias': 'sandwiches', 'title': 'Sandwiches'},
{'alias': 'salad', 'title': 'Salad'}],
'coordinates': {'latitude': 35.10871, 'longitude': -106.56739},
'display_phone': '(505) 881-5293',
'distance': 3571.724649307866,
'id': 'fork-and-fig-albuquerque',
'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/_-DpXKfS3jv6DyA47g6Fxg/o.jpg',
'is_closed': False,
'location': {'address1': '6904 Menaul Blvd NE',
'address2': 'Ste C',
'address3': '',
'city': 'Albuquerque',
'country': 'US',
'display_address': ['6904 Menaul Blvd NE', 'Ste C', 'Albuquerque, NM 87110'],
'state': 'NM',
'zip_code': '87110'},
'name': 'Fork & Fig',
'phone': '+15058815293',
'price': '$$',
'rating': 4.5,
'review_count': 604,
'transactions': [],
'url': 'https://www.yelp.com/biz/fork-and-fig-albuquerque?adjust_creative=SYc8R4Gowqru5h4SBKZXsQ&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=SYc8R4Gowqru5h4SBKZXsQ'}Above is the information provided about Fork & Fig, but all restaurants are provided with this information. For example, here is the information provided by Yelp for another restaurant, Frontier Restaurant.
frontier_restaurant = {'categories': [{'alias': 'mexican', 'title': 'Mexican'},
{'alias': 'diners', 'title': 'Diners'},
{'alias': 'tradamerican', 'title': 'American (Traditional)'}],
'coordinates': {'latitude': 35.0808088832532, 'longitude': -106.619402244687},
'display_phone': '(505) 266-0550',
'distance': 4033.6583235266075,
'id': 'frontier-restaurant-albuquerque-2',
'image_url': 'https://s3-media4.fl.yelpcdn.com/bphoto/M9L2z6-G0NobuDJ6YTh6VA/o.jpg',
'is_closed': False,
'location': {'address1': '2400 Central Ave SE',
'address2': '',
'address3': '',
'city': 'Albuquerque',
'country': 'US',
'display_address': ['2400 Central Ave SE', 'Albuquerque, NM 87106'],
'state': 'NM',
'zip_code': '87106'},
'name': 'Frontier Restaurant',
'phone': '+15052660550',
'price': '$',
'rating': 4.0,
'review_count': 1369,
'transactions': [],
'url': 'https://www.yelp.com/biz/frontier-restaurant-albuquerque-2?adjust_creative=SYc8R4Gowqru5h4SBKZXsQ&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=SYc8R4Gowqru5h4SBKZXsQ'}As we already know, one way to quickly view the attributes of a dictionary is to look at the keys of the dictionary.
fork_fig.keys()frontier_restaurant.keys()fork_fig.keys() == frontier_restaurant.keys()As we can see from our above comparison, Yelp provides us with the same information for both restaurants.
Ok, now let's write our functions. Write a function called restaurant_name that, provided a dictionary representing a restaurant like you saw above, returns that restaurant's name.
def restaurant_name(restaurant):
passrestaurant_name(frontier_restaurant) # 'Frontier Restaurant'restaurant_name(fork_fig) # 'Fork & Fig'Now write a function called restaurant_rating that returns the rating of the provided restaurant.
def restaurant_rating(restaurant):
passrestaurant_rating(frontier_restaurant) # 4.0restaurant_rating(fork_fig) # 4.5Now let's write a function called is_better that returns True if a restaurant has a higher rating than an alternative restaurant. The first argument should be called restaurant and the second argument should be called alternative. The function returns False if the two ratings are equal.
def is_better(restaurant, alternative):
passis_better(frontier_restaurant, fork_fig) # Falseis_better(fork_fig, frontier_restaurant) # Trueis_better(fork_fig, fork_fig) # FalseNow let's write a function called is_cheaper that returns True if a restaurant has a lower price, that is the restaurant has fewer '$' signs, than an alternative restaurant. The first argument should be called restaurant and the second argument should be called alternative. The function returns False if the two prices are equal.
Hint: Strings in Python respond to then
lenfunction.
def is_cheaper(restaurant, alternative):
passis_cheaper(fork_fig, frontier_restaurant) # Falseis_cheaper(frontier_restaurant, fork_fig) # Trueis_cheaper(fork_fig, fork_fig) # FalseNow write a function called high_rating that takes a restaurant as a first argument and a rating (in the form of a number) as the second argument and returns True if the given restaurant's rating is greater than or equal to the provided rating and returns False otherwise.
def high_rating(restaurant, rating):
passhigh_rating(fork_fig, 4) # Truehigh_rating(fork_fig, 5) # Falsehigh_rating(frontier_restaurant, 4) # TrueAwesome! We have built out some pretty cool functions so far. Let's now think about a case where we have more than just two data points to operate on. We have added some more "restaurants" below and are going to add them to our list of restauarants. Don't worry that they have a slightly different amount of data.
We are going to need a function mean_review_count to give us an idea what the ideal range for review_count is. This function should take in a list of restaurant dictionaries and return the mean of the review counts for the collection of restaurant dictionaries.
dennys = {'categories': [{'alias': 'breakfast', 'title': 'Breakfast'},
{'alias': 'diners', 'title': 'Diners'},
{'alias': 'tradamerican', 'title': 'American (Traditional)'}],
'is_closed': False,
'name': "Denny's",
'price': '$',
'rating': 3.0,
'review_count': 1200}
ihop = {'categories': [{'alias': 'breakfast', 'title': 'Breakfast'},
{'alias': 'diners', 'title': 'Diners'},
{'alias': 'tradamerican', 'title': 'American (Traditional)'}],
'is_closed': False,
'name': "IHOP: Internation House of Pancakes",
'price': '$',
'rating': 3.45,
'review_count': 1588}
mcdonalds = {'categories': [{'alias': 'breakfast', 'title': 'Breakfast'},
{'alias': 'burgers', 'title': 'Burgers'},
{'alias': 'fast food', 'title': 'Good Food Fast'}],
'is_closed': False,
'name': "McDonalds",
'price': '$',
'rating': 3.45,
'review_count': 2455}
pearl_street_oyster_bar = {'categories': [{'alias': 'seafood', 'title': 'Seafood'},
{'alias': 'gourmet', 'title': 'Gourmet'},
{'alias': 'Shellfish', 'title': 'Shellfish'}],
'is_closed': False,
'name': "Pear Street Oyster Bar",
'price': '$$$',
'rating': 4.75,
'review_count': 350}restaurant_list = [pearl_street_oyster_bar, mcdonalds, ihop, dennys, fork_fig, frontier_restaurant]# code goes here
def mean_review_count(list_of_restaurants):
passmean_review_count(restaurant_list)Next, let's maybe look at the median review, since, we want to make sure that there isn't any outliers in our data. Ideally the median and mean will be somewhat close, but obviously this would me more accurate given a larger sample size. Define a function median_review_count that again takes in a list of restaurant dictionaries and returns the median count of reviews. Remember that if a data set is even, to get the median we average the two middle data points.
# code goes here
def median_review_count(list_of_restaurants):
passmedian_review_count(restaurant_list)Great! In this lab we got practice working with functions - including passing both single and multiple arguments to them.
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