coding-mentor-agent/kb/python-course-kb-practical-python/wiki/exercises/2-24-first-class-data.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-2.24	2.24	First-class Data	2.7 Objects	02_Working_with_data/07_Objects.md	https://github.com/dabeaz-course/practical-python	93dca856b41c61a0a0f85ae334116e4c125629ea	false	false	false

Exercise 2.24: First-class Data

Source: Practical Python Programming, 02_Working_with_data/07_Objects.md.

Exercise 2.24: First-class Data

In the file Data/portfolio.csv, we read data organized as columns that look like this:

name,shares,price
"AA",100,32.20
"IBM",50,91.10
...

In previous code, we used the csv module to read the file, but still had to perform manual type conversions. For example:

for row in rows:
    name   = row[0]
    shares = int(row[1])
    price  = float(row[2])

This kind of conversion can also be performed in a more clever manner using some list basic operations.

Make a Python list that contains the names of the conversion functions you would use to convert each column into the appropriate type:

>>> types = [str, int, float]
>>>

The reason you can even create this list is that everything in Python is first-class. So, if you want to have a list of functions, that’s fine. The items in the list you created are functions for converting a value x into a given type (e.g., str(x), int(x), float(x)).

Now, read a row of data from the above file:

>>> import csv
>>> f = open('Data/portfolio.csv')
>>> rows = csv.reader(f)
>>> headers = next(rows)
>>> row = next(rows)
>>> row
['AA', '100', '32.20']
>>>

As noted, this row isn’t enough to do calculations because the types are wrong. For example:

>>> row[1] * row[2]
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: can't multiply sequence by non-int of type 'str'
>>>

However, maybe the data can be paired up with the types you specified in types. For example:

>>> types[1]
<type 'int'>
>>> row[1]
'100'
>>>

Try converting one of the values:

>>> types[1](row[1])     # Same as int(row[1])
100
>>>

Try converting a different value:

>>> types[2](row[2])     # Same as float(row[2])
32.2
>>>

Try the calculation with converted values:

>>> types[1](row[1])*types[2](row[2])
3220.0000000000005
>>>

Zip the column types with the fields and look at the result:

>>> r = list(zip(types, row))
>>> r
[(<type 'str'>, 'AA'), (<type 'int'>, '100'), (<type 'float'>,'32.20')]
>>>

You will notice that this has paired a type conversion with a value. For example, int is paired with the value '100'.

The zipped list is useful if you want to perform conversions on all of the values, one after the other. Try this:

>>> converted = []
>>> for func, val in zip(types, row):
          converted.append(func(val))
...
>>> converted
['AA', 100, 32.2]
>>> converted[1] * converted[2]
3220.0000000000005
>>>

Make sure you understand what’s happening in the above code. In the loop, the func variable is one of the type conversion functions (e.g., str, int, etc.) and the val variable is one of the values like 'AA', '100'. The expression func(val) is converting a value (kind of like a type cast).

The above code can be compressed into a single list comprehension.

>>> converted = [func(val) for func, val in zip(types, row)]
>>> converted
['AA', 100, 32.2]
>>>

关联来源

• summaries/07_Objects