coding-mentor-agent/kb/python-course-kb-practical-python/wiki/exercises/1-32-using-a-library-function.md

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id	source_exercise_id	title	section	source_path	source_repo	source_commit	student_visible_solution	has_private_solution	skip
practical-python-1.32	1.32	Using a library function	1.7 Functions	01_Introduction/07_Functions.md	https://github.com/dabeaz-course/practical-python	93dca856b41c61a0a0f85ae334116e4c125629ea	false	false	false

Exercise 1.32: Using a library function

Source: Practical Python Programming, 01_Introduction/07_Functions.md.

Exercise 1.32: Using a library function

Python comes with a large standard library of useful functions. One library that might be useful here is the csv module. You should use it whenever you have to work with CSV data files. Here is an example of how it works:

>>> import csv
>>> f = open('Data/portfolio.csv')
>>> rows = csv.reader(f)
>>> headers = next(rows)
>>> headers
['name', 'shares', 'price']
>>> for row in rows:
        print(row)

['AA', '100', '32.20']
['IBM', '50', '91.10']
['CAT', '150', '83.44']
['MSFT', '200', '51.23']
['GE', '95', '40.37']
['MSFT', '50', '65.10']
['IBM', '100', '70.44']
>>> f.close()
>>>

One nice thing about the csv module is that it deals with a variety of low-level details such as quoting and proper comma splitting. In the above output, you’ll notice that it has stripped the double-quotes away from the names in the first column.

Modify your pcost.py program so that it uses the csv module for parsing and try running earlier examples.

关联来源

• summaries/07_Functions