python-mastery/Exercises/ex5_3.md

[ Index | Exercise 5.2 | Exercise 5.4 ]

Exercise 5.3

Objectives:

• Higher order functions

Files Modified: reader.py

(a) Using higher-order functions

At the moment, the reader.py program consists of two core functions, csv_as_dicts() and csv_as_instances(). The code in these two functions is almost identical. For example:

def csv_as_dicts(lines, types, *, headers=None):
    '''
    Convert lines of CSV data into a list of dictionaries
    '''
    records = []
    rows = csv.reader(lines)
    if headers is None:
        headers = next(rows)
    for row in rows:
        record = { name: func(val)
                   for name, func, val in zip(headers, types, row) }
        records.append(record)
    return records

def csv_as_instances(lines, cls, *, headers=None):
    '''
    Convert lines of CSV data into a list of instances
    '''
    records = []
    rows = csv.reader(lines)
    if headers is None:
        headers = next(rows)
    for row in rows:
        record = cls.from_row(row)
        records.append(record)
    return records

Unify the core of these functions into a single function convert_csv() that accepts a user-defined conversion function as an argument. For example:

>>> def make_dict(headers, row):
        return dict(zip(headers, row))

>>> lines = open('Data/portfolio.csv')
>>> convert_csv(lines, make_dict)
[{'name': 'AA', 'shares': '100', 'price': '32.20'}, {'name': 'IBM', 'shares': '50', 'price': '91.10'}, 
 {'name': 'CAT', 'shares': '150', 'price': '83.44'}, {'name': 'MSFT', 'shares': '200', 'price': '51.23'}, 
 {'name': 'GE', 'shares': '95', 'price': '40.37'}, {'name': 'MSFT', 'shares': '50', 'price': '65.10'}, 
 {'name': 'IBM', 'shares': '100', 'price': '70.44'}]
>>>

Rewrite the csv_as_dicts() and csv_as_instances() functions in terms of the new convert_csv() function.

(b) Mapping

One of the most common operations in functional programming is the map() operation that maps a function to the values in a sequence. Python has a built-in map() function that does this. For example:

>>> nums = [1,2,3,4]
>>> squares = map(lambda x: x*x, nums)
>>> for n in squares:
        print(n)

1
4
9
16
>>>

map() produces an iterator so if you want a list, you’ll need to create it explicitly:

>>> squares = list(map(lambda x: x*x, nums))
>>> squares
[1, 4, 9, 16]
>>>

Try to use map() in your convert_csv() function.

[ Solution | Index | Exercise 5.2 | Exercise 5.4 ]

---

>>> Advanced Python Mastery
... A course by dabeaz
... Copyright 2007-2023

. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License