Being able to perform actions on a collection is incredibly useful in any type of programming, and it is pretty common to need to perform a single action on each item. We could do this by using a loop, but there are built-in collection functions that can take a collection and a function or lambda to run each item through. In this hands-on lab, we utilize higher-order functions to process some existing lists by using lambdas.
To feel comfortable completing this lab you’ll want to know how to do the following:
– Define and use lambdas. Watch “Defining and Using Lambdas” from the Certified Associate in Python Programming Certification course.
– Use higher-order functions and collection functions. Watch “Using Collection Functions” from the Certified Associate in Python Programming Certification course.
Learning Objectives
Successfully complete this lab by achieving the following learning objectives:
- Create the `sorted_by_name` List by Sorting the `people` List of Dictionaries
To sort our list of dictionaries we’re going to use the
sortedfunction. Not all of the values for thenamekey in our dictionaries start with a capital letter, and because of that, we’re going to want to convert all of the strings to lowercase before making the comparison. Thankfully, we can do this by passing a lambda for thekeyparameter. Here’s how we can complete the first task:collection_processing.py# 1) Sort the `people` list of dictionaries alphabetically based on the # 'name' key from each dictionary using the `sorted` function and store # the new list as `sorted_by_name` people = [ {"name": "Kevin Bacon", "age": 61}, {"name": "Fred Ward", "age": 77}, {"name": "finn Carter", "age": 59}, {"name": "Ariana Richards", "age": 40}, {"name": "Victor Wong", "age": 74}, ] sorted_by_name = sorted(people, key=lambda d: d['name'].lower()) assert sorted_by_name == [ {"name": "Ariana Richards", "age": 40}, {"name": "finn Carter", "age": 59}, {"name": "Fred Ward", "age": 77}, {"name": "Kevin Bacon", "age": 61}, {"name": "Victor Wong", "age": 74}, ], f"Expected sorted_by_name to equal '{sorted_by_name}' to equal '{[{'name': 'Ariana Richards', 'age': 40}, {'name': 'finn Carter', 'age': 59}, {'name': 'Fred Ward', 'age': 77}, {'name': 'Kevin Bacon', 'age': 61}, {'name': 'Victor Wong', 'age': 74}]}''"Running the script, we should see the following error. This indicates that we’ve successfully met the requirements for the first task.
$ python3.7 collection_processing.py Traceback (most recent call last): File "collection_processing.py", line 36, in <module> ], f"Expected name_declarations to equal '{name_declarations}' to equal '{['Ariana Richards is 40 years old', 'finn Carter is 59 years old', 'Fred Ward is 77 y ears old', 'Kevin Bacon is 61 years old', 'Victor Wong is 74 years old']}'" AssertionError: Expected name_declarations to equal 'None' to equal '['Ariana Richards is 40 years old', 'finn Carter is 59 years old', 'Fred Ward is 77 years old', 'Kevin Bacon is 61 years old', 'Victor Wong is 74 years old']'- Create the `name_declarations` List by Mapping over `sorted_by_name`
Our second task is to create a list where each string uses the
nameandageparameters to build strings of<NAME> is <AGE> years old.Let’s create thename_declarationsvariable:collection_processing.py# 2) Use the `map` function to iterate over `sorted_by_name` to generate a # new list called `name_declarations` where each value is a string with # `<NAME> is <AGE> years old.` where the `<NAME>` and `<AGE>` values are from # the dictionaries. name_declarations = list( map(lambda d: f"{d['name']} is {d['age']} years old", sorted_by_name) ) assert name_declarations == [ "Ariana Richards is 40 years old", "finn Carter is 59 years old", "Fred Ward is 77 years old", "Kevin Bacon is 61 years old", "Victor Wong is 74 years old", ], f"Expected name_declarations to equal '{name_declarations}' to equal '{['Ariana Richards is 40 years old', 'finn Carter is 59 years old', 'Fred Ward is 77 years old', 'Kevin Bacon is 61 years old', 'Victor Wong is 74 years old']}'"Note that we need to convert the result of
mapto be a list, otherwise ourname_declarationsvariable will be amapobject.Running our script again, this is what we should see:
python3.7 collection_processing.py Traceback (most recent call last): File "collection_processing.py", line 50, in <module> ], f"Expected under_seventy to equal '{under_seventy}' to equal '{[{'name': 'Ariana Richards', 'age': 40}, {'name': 'finn Carter', 'age': 59}, {'name': 'Kevin Bacon', 'age': 61}]}'" AssertionError: Expected under_seventy to equal 'None' to equal '[{'name': 'Ariana Richards', 'age': 40}, {'name': 'finn Carter', 'age': 59}, {'name': 'Kevin Bacon', 'age': 61}]'- Create the `under_seventy` List by Filtering and Sorting on the `sorted_by_name` List
Our last task is to filter the values in
sorted_by_nameso our output list only includes dictionaries that have anagevalue less than 70. To achieve this, we’re going to combinefilterandsortedto create our final list.collection_processing.py# 3) Combine the `filter` and `sorted` functions to iterate over `sorted_by_name` to generate a # new list called `under_seventy` that only contains the dictionaries where the # 'age' key is less than 70, sorting the list by age. under_seventy = sorted( filter(lambda d: d['age'] < 70, sorted_by_name), key=lambda d: d['age'] ) assert under_seventy == [ {"name": "Ariana Richards", "age": 40}, {"name": "finn Carter", "age": 59}, {"name": "Kevin Bacon", "age": 61}, ], f"Expected under_seventy to equal '{under_seventy}' to equal '{[{'name': 'Ariana Richards', 'age': 40}, {'name': 'finn Carter', 'age': 59}, {'name': 'Kevin Bacon', 'age': 61}]}'"Running our script one last time we should see no output because all of the assertions were true.
