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

```python
>>> 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]]