coding-mentor-agent/kb/python-course-kb-practical-python/wiki/exercises/8-2-adding-logging-to-a-module.md

id, source_exercise_id, title, section, source_path, source_repo, source_commit, student_visible_solution, has_private_solution, skip

id	source_exercise_id	title	section	source_path	source_repo	source_commit	student_visible_solution	has_private_solution	skip
practical-python-8.2	8.2	Adding logging to a module	8.2 Logging	08_Testing_debugging/02_Logging.md	https://github.com/dabeaz-course/practical-python	93dca856b41c61a0a0f85ae334116e4c125629ea	false	true	false

Exercise 8.2: Adding logging to a module

Source: Practical Python Programming, 08_Testing_debugging/02_Logging.md.

Exercise 8.2: Adding logging to a module

In fileparse.py, there is some error handling related to exceptions caused by bad input. It looks like this:

# fileparse.py
import csv

def parse_csv(lines, select=None, types=None, has_headers=True, delimiter=',', silence_errors=False):
    '''
    Parse a CSV file into a list of records with type conversion.
    '''
    if select and not has_headers:
        raise RuntimeError('select requires column headers')

    rows = csv.reader(lines, delimiter=delimiter)

    # Read the file headers (if any)
    headers = next(rows) if has_headers else []

    # If specific columns have been selected, make indices for filtering and set output columns
    if select:
        indices = [ headers.index(colname) for colname in select ]
        headers = select

    records = []
    for rowno, row in enumerate(rows, 1):
        if not row:     # Skip rows with no data
            continue

        # If specific column indices are selected, pick them out
        if select:
            row = [ row[index] for index in indices]

        # Apply type conversion to the row
        if types:
            try:
                row = [func(val) for func, val in zip(types, row)]
            except ValueError as e:
                if not silence_errors:
                    print(f"Row {rowno}: Couldn't convert {row}")
                    print(f"Row {rowno}: Reason {e}")
                continue

        # Make a dictionary or a tuple
        if headers:
            record = dict(zip(headers, row))
        else:
            record = tuple(row)
        records.append(record)

    return records

Notice the print statements that issue diagnostic messages. Replacing those prints with logging operations is relatively simple. Change the code like this:

# fileparse.py
import csv
import logging
log = logging.getLogger(__name__)

def parse_csv(lines, select=None, types=None, has_headers=True, delimiter=',', silence_errors=False):
    '''
    Parse a CSV file into a list of records with type conversion.
    '''
    if select and not has_headers:
        raise RuntimeError('select requires column headers')

    rows = csv.reader(lines, delimiter=delimiter)

    # Read the file headers (if any)
    headers = next(rows) if has_headers else []

    # If specific columns have been selected, make indices for filtering and set output columns
    if select:
        indices = [ headers.index(colname) for colname in select ]
        headers = select

    records = []
    for rowno, row in enumerate(rows, 1):
        if not row:     # Skip rows with no data
            continue

        # If specific column indices are selected, pick them out
        if select:
            row = [ row[index] for index in indices]

        # Apply type conversion to the row
        if types:
            try:
                row = [func(val) for func, val in zip(types, row)]
            except ValueError as e:
                if not silence_errors:
                    log.warning("Row %d: Couldn't convert %s", rowno, row)
                    log.debug("Row %d: Reason %s", rowno, e)
                continue

        # Make a dictionary or a tuple
        if headers:
            record = dict(zip(headers, row))
        else:
            record = tuple(row)
        records.append(record)

    return records

Now that you've made these changes, try using some of your code on bad data.

>>> import report
>>> a = report.read_portfolio('Data/missing.csv')
Row 4: Bad row: ['MSFT', '', '51.23']
Row 7: Bad row: ['IBM', '', '70.44']
>>>

If you do nothing, you'll only get logging messages for the WARNING level and above. The output will look like simple print statements. However, if you configure the logging module, you'll get additional information about the logging levels, module, and more. Type these steps to see that:

>>> import logging
>>> logging.basicConfig()
>>> a = report.read_portfolio('Data/missing.csv')
WARNING:fileparse:Row 4: Bad row: ['MSFT', '', '51.23']
WARNING:fileparse:Row 7: Bad row: ['IBM', '', '70.44']
>>>

You will notice that you don't see the output from the log.debug() operation. Type this to change the level.

>>> logging.getLogger('fileparse').setLevel(logging.DEBUG)
>>> a = report.read_portfolio('Data/missing.csv')
WARNING:fileparse:Row 4: Bad row: ['MSFT', '', '51.23']
DEBUG:fileparse:Row 4: Reason: invalid literal for int() with base 10: ''
WARNING:fileparse:Row 7: Bad row: ['IBM', '', '70.44']
DEBUG:fileparse:Row 7: Reason: invalid literal for int() with base 10: ''
>>>

Turn off all, but the most critical logging messages:

>>> logging.getLogger('fileparse').setLevel(logging.CRITICAL)
>>> a = report.read_portfolio('Data/missing.csv')
>>>

关联来源

• summaries/02_Logging