Day 24 - Transition to Python 1

Skills: 8

Pre-reading: 9.1.1, 9.1.2, 9.1.3, 9.1.4, 9.1.5

Setup: Install & configure VSCode following these directions: https://neu-pdi.github.io/cs2100-public-resources/setup/#vscode

Intro (20 mins)

• Today we begin our transition from Pyret to Python.
• Python and Pyret share many core ideas, but use different notation and conventions -- more significantly, the Pyret programming environment is designed to be easy to use, whereas there is more involved setup for Python.

Example: Function to compute gadget cost

• Pyret:

  fun gadget-cost(num-gadgets :: Number, label :: String) -> Number:
    doc: "computes cost, at $0.50 per gadget plus $0.05 per character in label"
    num-gadgets * (0.50 + (string-length(label) * 0.05))
  where:
    gadget_cost(1, "hi") is 0.55
    gadget_cost(10, "tech") is 6.00
  end

• Python:

  def gadget_cost(num_gadgets: int, label: str) -> float:
      """ computes cost, at $0.50 per gadget plus $0.05 per character in label """
      return num_gadgets * (0.50 + (len(label) * 0.05))
  # Testing -- we'll come back to this

Example: Conditionals

• Pyret:

  fun add-shipping(order-amt :: Number) -> Number:
    doc: "adds 4 for orders <= 10 (but non-zero), 8 for orders < 30, 12 for larger orders"
    if order-amt == 0:
      0
    else if order-amt <= 10:
      order-amt + 4
    else if order-amt < 30:
      order-amt + 8
    else:
      order-amt + 12
    end
  end

• Python:

  def add_shipping(order_amt: float) -> float:
      """ adds 4 for orders <= 10 (but non-zero), 8 for orders < 30, 12 for larger orders """
      if order_amt == 0:
          return 0
      elif order_amt <= 10:
          return order_amt + 4
      elif order_amt < 30:
          return order_amt + 8
      else:
          return order_amt + 12
  # Testing -- we'll come back to this

Testing in Python with pytest

• In Pyret, you use where: blocks for examples/tests. In Python, there are multiple ways to write tests, but a simple (and widely used) mechanism is using pytest, where you write separate test functions using assert.
• Example:

  import pytest
  
  def gadget_cost(num_gadgets: int, label: str) -> float:
      return num_gadgets * (0.50 + (len(label) * 0.05))
  
  def test_gadget_cost():
      assert gadget_cost(1, "hi") == pytest.approx(0.55)
      assert gadget_cost(10, "tech") == pytest.approx(6.00)

• Conventions:
  • Test functions must be named starting with test_.
  • Use assert to check expected results. It takes a boolean expression and causes a test failure if it evaluates to False.
  • For floating-point (any decimal in Python) results, use pytest.approx for comparison.
• How to run tests:
  1. Save your code in a file, e.g., gadget.py.
  2. In the terminal, run:

     pytest gadget.py

  3. pytest will find and run all functions named test_* and report results.

Class Exercises (35 mins)

1. Function Syntax Practice

   • Rewrite the following Pyret function in Python:

     fun greet(name :: String) -> String:
       doc: "produces Hello, name!"
       "Hello, " + name + "!"
     where:
       greet("Alice") is "Hello, Alice!"
       greet("Bob") is "Hello, Bob!"
     end

     Be sure to follow full design recipe, including a docstring and test cases.
   • Now, design a Python function that takes a name and an age, and returns a string like "Alice is 20 years old."

2. Conditionals

   • Translate this Pyret function to Python:

     fun shipping-cost(weight :: Number) -> Number:
       doc: "if weight <= 1, cost is $5; if weight <= 5, cost is $10; otherwise, $20"
       if weight <= 1:
         5
       else if weight <= 5:
         10
       else:
         20
       end
     end

   • Design a Python function grade_letter(score: int) -> str that returns "A" for scores 90 and above, "B" for 80–89, "C" for 70–79, "D" for 60–69, and "F" otherwise.

3. Return Statement

   • What happens if you forget the return statement in a Python function? Try it and observe the result.

Wrap-up (5 mins)

• Python and Pyret share core ideas, but differ in syntax and conventions.
• Next time: more on Python data and control structures.