---
id: practical-python-3.9
source_exercise_id: "3.9"
title: "Catching exceptions"
section: "3.3 Error Checking"
source_path: "03_Program_organization/03_Error_checking.md"
source_repo: "https://github.com/dabeaz-course/practical-python"
source_commit: "93dca856b41c61a0a0f85ae334116e4c125629ea"
student_visible_solution: false
has_private_solution: false
skip: false
---

# Exercise 3.9: Catching exceptions

> Source: Practical Python Programming, `03_Program_organization/03_Error_checking.md`.

### Exercise 3.9: Catching exceptions

The `parse_csv()` function you wrote is used to process the entire
contents of a file.  However, in the real-world, it’s possible that
input files might have corrupted, missing, or dirty data.  Try this
experiment:

```python
>>> portfolio = parse_csv('Data/missing.csv', types=[str, int, float])
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "fileparse.py", line 36, in parse_csv
    row = [func(val) for func, val in zip(types, row)]
ValueError: invalid literal for int() with base 10: ''
>>>
```

Modify the `parse_csv()` function to catch all `ValueError` exceptions
generated during record creation and print a warning message for rows
that can’t be converted.

The message should include the row number and information about the
reason why it failed.  To test your function, try reading the file
`Data/missing.csv` above.  For example:

```python
>>> portfolio = parse_csv('Data/missing.csv', types=[str, int, float])
Row 4: Couldn't convert ['MSFT', '', '51.23']
Row 4: Reason invalid literal for int() with base 10: ''
Row 7: Couldn't convert ['IBM', '', '70.44']
Row 7: Reason invalid literal for int() with base 10: ''
>>>
>>> portfolio
[{'price': 32.2, 'name': 'AA', 'shares': 100}, {'price': 91.1, 'name': 'IBM', 'shares': 50}, {'price': 83.44, 'name': 'CAT', 'shares': 150}, {'price': 40.37, 'name': 'GE', 'shares': 95}, {'price': 65.1, 'name': 'MSFT', 'shares': 50}]
>>>
```

## 关联来源

- [[summaries/03_Error_checking]]