coding-mentor-agent/kb/python-course-kb-practical-python/wiki/exercises/2-16-using-the-zip-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-2.16	2.16	Using the zip() function	2.4 Sequences	02_Working_with_data/04_Sequences.md	https://github.com/dabeaz-course/practical-python	93dca856b41c61a0a0f85ae334116e4c125629ea	false	true	false

Exercise 2.16: Using the zip() function

Source: Practical Python Programming, 02_Working_with_data/04_Sequences.md.

Exercise 2.16: Using the zip() function

In the file Data/portfolio.csv, the first line contains column headers. In all previous code, we’ve been discarding them.

>>> f = open('Data/portfolio.csv')
>>> rows = csv.reader(f)
>>> headers = next(rows)
>>> headers
['name', 'shares', 'price']
>>>

However, what if you could use the headers for something useful? This is where the zip() function enters the picture. First try this to pair the file headers with a row of data:

>>> row = next(rows)
>>> row
['AA', '100', '32.20']
>>> list(zip(headers, row))
[ ('name', 'AA'), ('shares', '100'), ('price', '32.20') ]
>>>

Notice how zip() paired the column headers with the column values. We’ve used list() here to turn the result into a list so that you can see it. Normally, zip() creates an iterator that must be consumed by a for-loop.

This pairing is an intermediate step to building a dictionary. Now try this:

>>> record = dict(zip(headers, row))
>>> record
{'price': '32.20', 'name': 'AA', 'shares': '100'}
>>>

This transformation is one of the most useful tricks to know about when processing a lot of data files. For example, suppose you wanted to make the pcost.py program work with various input files, but without regard for the actual column number where the name, shares, and price appear.

Modify the portfolio_cost() function in pcost.py so that it looks like this:

# pcost.py

def portfolio_cost(filename):
    ...
        for rowno, row in enumerate(rows, start=1):
            record = dict(zip(headers, row))
            try:
                nshares = int(record['shares'])
                price = float(record['price'])
                total_cost += nshares * price
            # This catches errors in int() and float() conversions above
            except ValueError:
                print(f'Row {rowno}: Bad row: {row}')
        ...

Now, try your function on a completely different data file Data/portfoliodate.csv which looks like this:

name,date,time,shares,price
"AA","6/11/2007","9:50am",100,32.20
"IBM","5/13/2007","4:20pm",50,91.10
"CAT","9/23/2006","1:30pm",150,83.44
"MSFT","5/17/2007","10:30am",200,51.23
"GE","2/1/2006","10:45am",95,40.37
"MSFT","10/31/2006","12:05pm",50,65.10
"IBM","7/9/2006","3:15pm",100,70.44

>>> portfolio_cost('Data/portfoliodate.csv')
44671.15
>>>

If you did it right, you’ll find that your program still works even though the data file has a completely different column format than before. That’s cool!

The change made here is subtle, but significant. Instead of portfolio_cost() being hardcoded to read a single fixed file format, the new version reads any CSV file and picks the values of interest out of it. As long as the file has the required columns, the code will work.

Modify the report.py program you wrote in Section 2.3 so that it uses the same technique to pick out column headers.

Try running the report.py program on the Data/portfoliodate.csv file and see that it produces the same answer as before.

关联来源

• summaries/04_Sequences