coding-mentor-agent/kb/python-course-kb-practical-python/wiki/exercises/2-22-data-extraction.md

id, source_exercise_id, title, section, source_path, source_repo, source_commit, student_visible_solution, has_private_solution, skip

id	source_exercise_id	title	section	source_path	source_repo	source_commit	student_visible_solution	has_private_solution	skip
practical-python-2.22	2.22	Data Extraction	2.6 List Comprehensions	02_Working_with_data/06_List_comprehension.md	https://github.com/dabeaz-course/practical-python	93dca856b41c61a0a0f85ae334116e4c125629ea	false	false	false

Exercise 2.22: Data Extraction

Source: Practical Python Programming, 02_Working_with_data/06_List_comprehension.md.

Exercise 2.22: Data Extraction

Show how you could build a list of tuples (name, shares) where name and shares are taken from portfolio.

>>> name_shares =[ (s['name'], s['shares']) for s in portfolio ]
>>> name_shares
[('AA', 100), ('IBM', 50), ('CAT', 150), ('MSFT', 200), ('GE', 95), ('MSFT', 50), ('IBM', 100)]
>>>

If you change the square brackets ([,]) to curly braces ({, }), you get something known as a set comprehension. This gives you unique or distinct values.

For example, this determines the set of unique stock names that appear in portfolio:

>>> names = { s['name'] for s in portfolio }
>>> names
{ 'AA', 'GE', 'IBM', 'MSFT', 'CAT' }
>>>

If you specify key:value pairs, you can build a dictionary. For example, make a dictionary that maps the name of a stock to the total number of shares held.

>>> holdings = { name: 0 for name in names }
>>> holdings
{'AA': 0, 'GE': 0, 'IBM': 0, 'MSFT': 0, 'CAT': 0}
>>>

This latter feature is known as a dictionary comprehension. Let’s tabulate:

>>> for s in portfolio:
        holdings[s['name']] += s['shares']

>>> holdings
{ 'AA': 100, 'GE': 95, 'IBM': 150, 'MSFT':250, 'CAT': 150 }
>>>

Try this example that filters the prices dictionary down to only those names that appear in the portfolio:

>>> portfolio_prices = { name: prices[name] for name in names }
>>> portfolio_prices
{'AA': 9.22, 'GE': 13.48, 'IBM': 106.28, 'MSFT': 20.89, 'CAT': 35.46}
>>>

关联来源

• summaries/06_List_comprehension