57 lines
1.5 KiB
Markdown
57 lines
1.5 KiB
Markdown
---
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id: practical-python-1.32
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source_exercise_id: "1.32"
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title: "Using a library function"
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section: "1.7 Functions"
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source_path: "01_Introduction/07_Functions.md"
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source_repo: "https://github.com/dabeaz-course/practical-python"
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source_commit: "93dca856b41c61a0a0f85ae334116e4c125629ea"
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student_visible_solution: false
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has_private_solution: false
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skip: false
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---
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# Exercise 1.32: Using a library function
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> Source: Practical Python Programming, `01_Introduction/07_Functions.md`.
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### Exercise 1.32: Using a library function
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Python comes with a large standard library of useful functions. One
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library that might be useful here is the `csv` module. You should use
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it whenever you have to work with CSV data files. Here is an example
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of how it works:
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```python
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>>> import csv
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>>> f = open('Data/portfolio.csv')
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>>> rows = csv.reader(f)
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>>> headers = next(rows)
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>>> headers
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['name', 'shares', 'price']
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>>> for row in rows:
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print(row)
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['AA', '100', '32.20']
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['IBM', '50', '91.10']
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['CAT', '150', '83.44']
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['MSFT', '200', '51.23']
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['GE', '95', '40.37']
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['MSFT', '50', '65.10']
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['IBM', '100', '70.44']
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>>> f.close()
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>>>
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```
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One nice thing about the `csv` module is that it deals with a variety
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of low-level details such as quoting and proper comma splitting. In
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the above output, you’ll notice that it has stripped the double-quotes
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away from the names in the first column.
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Modify your `pcost.py` program so that it uses the `csv` module for
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parsing and try running earlier examples.
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## 关联来源
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- [[summaries/07_Functions]]
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