2 Numeric types

This chapter introduces Python’s core numeric types and shows how they underpin everyday programming tasks. While Python supports integers, floating-point numbers, and complex numbers, the practical focus is on ints and floats—how they’re stored, compared, converted, and displayed. Through discussion of assignment, function arguments, loops, user input, and string formatting, the chapter emphasizes the habits you need when mixing numbers with other common types (like strings) and working with iteration and comparisons. It also highlights f-strings for readable output and illustrates where numeric handling conventions in Python can differ from expectations (for example, how ranges and inclusivity work).

The first set of exercises builds fluency with control flow, input handling, and type conversion. A number-guessing game reinforces generating random integers, converting user input from string to int, comparing values, and looping until a condition is met, with a nod to the walrus operator as an alternative looping pattern. A reimplementation of sum demonstrates function design with variable-length argument lists using the splat operator, iterating over tuples of inputs, and the practical differences between receiving an iterable versus expanding it into arguments. The guidance stresses clear, Pythonic usage of parameters and iteration when accumulating numeric results.

The later exercises deepen numeric reasoning with floats and bases. A run-timing task collects repeated floating-point inputs, exits cleanly on empty input, computes an average, and discusses numeric robustness: using try/except for validation, the limits of floating-point precision, formatting with f-strings, and when integers or Decimal are safer choices. A hexadecimal conversion task then ties iteration concepts together—reversing a string, enumerating positions, applying powers, and converting digits with base-aware int—to produce a decimal result. Together, these exercises reinforce practical numeric workflows in Python: iterate cleanly, convert explicitly, format thoughtfully, and choose numeric representations that match the problem’s precision needs.

FAQ

s = input('Enter a number: ')
n = int(s)  # or float(s)

import random
n = random.randint(0, 100)  # 0 ≤ n ≤ 100

name = 'World'
msg = f'Hello, {name}!'

x = 0.1 + 0.7
print(f'{x:.2f}')  # prints 0.80

import random

answer = random.randint(0, 100)
while True:
    guess = int(input('Your guess: '))
    if guess == answer:
        print('Just right')
        break
    elif guess < answer:
        print('Too low')
    else:
        print('Too high')

def mysum(*numbers):
    total = 0
    for n in numbers:
        total += n
    return total

mysum(1, 2, 3)          # 6
nums = [10, 20, 30]
mysum(*nums)            # 60 (list expanded into args)

count = 0
total = 0.0
while True:
    s = input('Enter 10 km run time: ')
    if not s:
        break
    total += float(s)
    count += 1
avg = total / count if count else 0.0
print(f'Average of {avg}, over {count} runs')

from decimal import Decimal
print(Decimal('0.1') + Decimal('0.2'))  # 0.3

hexnum = '50'
dec = 0
for power, digit in enumerate(reversed(hexnum)):
    dec += int(digit, 16) * (16 ** power)
print(dec)  # 80

for idx, ch in enumerate('abcd'):
    print(idx, ch)  # 0 a, 1 b, ...

for ch in reversed('abcd'):
    print(ch)       # d c b a