In this exercise, we will see how we can process the data that is stored in a CSV file.
When working with a file that contains a large amount of data, generating every possible record using
tuple() is inefficient. Recall from the previous exercise that
map() returns an iterator that we can use to bring in data. We can apply the higher-order functions to this iterator to process the data and only bring in the relevant set of records. For example, let’s say we only want to retain tuples for houses built after 1985. We can do this by applying
filter() with the lambda
lambda q: q > 1985 like so:
h = filter(lambda q: q > 1985, map(lambda x: house(int(x), int(x), int(x), int(x)), reader)) houses = tuple(h) print(houses) # Print out tuples of house built after 1985
Now let’s say we want the most expensive house built after 1985. We can use
reduce() to do this for us like so:
h = filter(lambda q: q > 1985, map(lambda x: house(int(x), int(x), int(x), int(x)), reader)) houses = tuple(h) from functools import reduce most_expensive = reduce(lambda x, y: x if x.list_price < y.list_price else y, houses) print(most_expensive) # Print out tuples of house built after 1985
When working with CSV files, it is important to ensure they are properly formatted; some entries might have leading or trailing whitespaces that we must remove, for example.
Create a tuple called
trees and populate it with data of trees that have a height greater than 75.
First, create an iterator called
t where you filter out trees that don’t meet the height requirement. Then create the
trees tuple like so:
trees = tuple(t)
Create a variable called
widest and store in it the record of the widest tree in
trees. You may want to print the answer to see which tree is the widest!