102 lines
2.9 KiB
Markdown
102 lines
2.9 KiB
Markdown
\[ [Index](index.md) | [Exercise 5.2](ex5_2.md) | [Exercise 5.4](ex5_4.md) \]
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# Exercise 5.3
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*Objectives:*
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- Higher order functions
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*Files Modified:* `reader.py`
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## (a) Using higher-order functions
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At the moment, the `reader.py` program consists of two core functions, `csv_as_dicts()` and
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`csv_as_instances()`. The code in these two functions is almost identical. For example:
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```python
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def csv_as_dicts(lines, types, *, headers=None):
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'''
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Convert lines of CSV data into a list of dictionaries
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'''
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records = []
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rows = csv.reader(lines)
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if headers is None:
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headers = next(rows)
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for row in rows:
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record = { name: func(val)
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for name, func, val in zip(headers, types, row) }
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records.append(record)
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return records
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def csv_as_instances(lines, cls, *, headers=None):
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'''
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Convert lines of CSV data into a list of instances
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'''
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records = []
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rows = csv.reader(lines)
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if headers is None:
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headers = next(rows)
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for row in rows:
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record = cls.from_row(row)
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records.append(record)
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return records
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```
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Unify the core of these functions into a single function `convert_csv()` that accepts a user-defined
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conversion function as an argument. For example:
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```python
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>>> def make_dict(headers, row):
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return dict(zip(headers, row))
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>>> lines = open('Data/portfolio.csv')
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>>> convert_csv(lines, make_dict)
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[{'name': 'AA', 'shares': '100', 'price': '32.20'}, {'name': 'IBM', 'shares': '50', 'price': '91.10'},
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{'name': 'CAT', 'shares': '150', 'price': '83.44'}, {'name': 'MSFT', 'shares': '200', 'price': '51.23'},
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{'name': 'GE', 'shares': '95', 'price': '40.37'}, {'name': 'MSFT', 'shares': '50', 'price': '65.10'},
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{'name': 'IBM', 'shares': '100', 'price': '70.44'}]
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>>>
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```
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Rewrite the `csv_as_dicts()` and `csv_as_instances()` functions in terms of the new `convert_csv()`
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function.
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## (b) Mapping
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One of the most common operations in functional programming is the `map()` operation that maps a function
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to the values in a sequence. Python has a built-in `map()` function that does this. For
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example:
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```python
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>>> nums = [1,2,3,4]
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>>> squares = map(lambda x: x*x, nums)
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>>> for n in squares:
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print(n)
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1
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4
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9
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16
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>>>
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```
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`map()` produces an iterator so if you want a list, you'll need to create it explicitly:
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```python
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>>> squares = list(map(lambda x: x*x, nums))
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>>> squares
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[1, 4, 9, 16]
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>>>
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```
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Try to use `map()` in your `convert_csv()` function.
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\[ [Solution](soln5_3.md) | [Index](index.md) | [Exercise 5.2](ex5_2.md) | [Exercise 5.4](ex5_4.md) \]
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----
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`>>>` Advanced Python Mastery
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`...` A course by [dabeaz](https://www.dabeaz.com)
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`...` Copyright 2007-2023
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. This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)
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