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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-2.25 | 2.25 | Making dictionaries | 2.7 Objects | 02_Working_with_data/07_Objects.md | https://github.com/dabeaz-course/practical-python | 93dca856b41c61a0a0f85ae334116e4c125629ea | false | false | false |
Exercise 2.25: Making dictionaries
Source: Practical Python Programming,
02_Working_with_data/07_Objects.md.
Exercise 2.25: Making dictionaries
Remember how the dict() function can easily make a dictionary if you
have a sequence of key names and values? Let’s make a dictionary from
the column headers:
>>> headers
['name', 'shares', 'price']
>>> converted
['AA', 100, 32.2]
>>> dict(zip(headers, converted))
{'price': 32.2, 'name': 'AA', 'shares': 100}
>>>
Of course, if you’re up on your list-comprehension fu, you can do the whole conversion in a single step using a dict-comprehension:
>>> { name: func(val) for name, func, val in zip(headers, types, row) }
{'price': 32.2, 'name': 'AA', 'shares': 100}
>>>