Alternative to locals() in printing a table with a header

[Python 3.1]

Edit: mistake in the original code.

I need to print a table. The first row should be a header, which consists of column names separated by tabs. The following rows should contain the data (also tab-separated).

To clarify, let's say I have columns "speed", "power", "weight". I originally wrote the following code, with the help from a related question I asked earlier:

column_names = ['speed', 'power', 'weight']

def f(row_number):
  # some calculations here to populate variables speed, power, weight
  # e.g., power = retrieve_avg_power(row_number) * 2.5
  # e.g., speed = math.sqrt(power) / 2
  # etc.
  locals_ = locals()
  return {x : locals_[x] for x in column_names}

def print_table(rows):
  print(*column_names, sep = '\t')
  fo开发者_如何学编程r row_number in range(rows):
    row = f(row_number)
    print(*[row[x] for x in component_names], sep = '\t')

But then I learned that I should avoid using locals() if possible.

Now I'm stuck. I don't want to type the list of all the column names more than once. I don't want to rely on the fact that every dictionary I create inside f() is likely to iterate through its keys in the same order. And I don't want to use locals().

Note that the functions print_table() and f() do a lot of other stuff; so I have to keep them separate.

How should I write the code?

class Columns:
    pass

def f(row_number):
    c = Columns()
    c.power = retrieve_avg_power(row_number) * 2.5
    c.speed = math.sqrt(power) / 2
    return c.__dict__

This also lets you specify which of the variables are meant as columns, instead of rather being temporary in the function.

You could use an OrderedDict to fix the order of the dictionaries. But as I see it that isn't even necessary. You are always taking the keys from the column_names list (except in the last line, I assume that is a typo), so the order of the values will always be the same.

an alternative to locals() will be to use the inspect module

import inspect

def f(row_number):
    # some calculations here to populate variables speed, power, weight
    # e.g., power = retrieve_avg_power(row_number) * 2.5
    # e.g., speed = math.sqrt(power) / 2
    # etc.
    locals_ = inspect.currentframe().f_locals
    return {x : locals_[x] for x in column_names }