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Exercises/ex5_3.md

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+\[ [Index](index.md) | [Exercise 5.2](ex5_2.md) | [Exercise 5.4](ex5_4.md) \]
+
+# Exercise 5.3
+
+*Objectives:*
+
+- Higher order functions
+
+*Files Modified:* `reader.py`
+
+## (a) Using higher-order functions
+
+At the moment, the `reader.py` program consists of two core functions, `csv_as_dicts()` and
+`csv_as_instances()`.   The code in these two functions is almost identical.  For example:
+
+```python
+def csv_as_dicts(lines, types, *, headers=None):
+    '''
+    Convert lines of CSV data into a list of dictionaries
+    '''
+    records = []
+    rows = csv.reader(lines)
+    if headers is None:
+        headers = next(rows)
+    for row in rows:
+        record = { name: func(val)
+                   for name, func, val in zip(headers, types, row) }
+        records.append(record)
+    return records
+
+def csv_as_instances(lines, cls, *, headers=None):
+    '''
+    Convert lines of CSV data into a list of instances
+    '''
+    records = []
+    rows = csv.reader(lines)
+    if headers is None:
+        headers = next(rows)
+    for row in rows:
+        record = cls.from_row(row)
+        records.append(record)
+    return records
+```
+
+Unify the core of these functions into a single function `convert_csv()` that accepts a user-defined
+conversion function as an argument.  For example:
+
+```python
+>>> def make_dict(headers, row):
+        return dict(zip(headers, row))
+
+>>> lines = open('Data/portfolio.csv')
+>>> convert_csv(lines, make_dict)
+[{'name': 'AA', 'shares': '100', 'price': '32.20'}, {'name': 'IBM', 'shares': '50', 'price': '91.10'}, 
+ {'name': 'CAT', 'shares': '150', 'price': '83.44'}, {'name': 'MSFT', 'shares': '200', 'price': '51.23'}, 
+ {'name': 'GE', 'shares': '95', 'price': '40.37'}, {'name': 'MSFT', 'shares': '50', 'price': '65.10'}, 
+ {'name': 'IBM', 'shares': '100', 'price': '70.44'}]
+>>>
+```
+
+Rewrite the `csv_as_dicts()` and `csv_as_instances()` functions in terms of the new `convert_csv()`
+function.
+
+## (b) Mapping
+
+One of the most common operations in functional programming is the `map()` operation that maps a function
+to the values in a sequence.  Python has a built-in `map()` function that does this.  For
+example:
+
+```python
+>>> nums = [1,2,3,4]
+>>> squares = map(lambda x: x*x, nums)
+>>> for n in squares:
+        print(n)
+
+1
+4
+9
+16
+>>>
+```
+
+`map()` produces an iterator so if you want a list, you'll need to create it explicitly:
+
+```python
+>>> squares = list(map(lambda x: x*x, nums))
+>>> squares
+[1, 4, 9, 16]
+>>>
+```
+
+Try to use `map()` in your `convert_csv()` function.
+
+\[ [Solution](soln5_3.md) | [Index](index.md) | [Exercise 5.2](ex5_2.md) | [Exercise 5.4](ex5_4.md) \]
+
+----
+`>>>` Advanced Python Mastery  
+`...` A course by [dabeaz](https://www.dabeaz.com)  
+`...` Copyright 2007-2023  
+
+![](https://i.creativecommons.org/l/by-sa/4.0/88x31.png). This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)