Day 25 - Transition to Python 1

TODO: actually show how to RUN pytest tests.

Functions, Types, and Conditionals in Python vs. Pyret

1. Introduction (10 minutes)

Motivation:
- Explain that we’ve learned the basics in Pyret—expressions, functions, and data (including structured and conditional data).
- Today we “transition” to Python, which—while conceptually similar—uses different notation and conventions.
Learning Goals:
- Recognize notational differences in function definitions and type annotations.
- Understand how return values are specified in Python.
- Compare conditionals in Pyret and Python.
Do Now:
- Show a simple Pyret function (e.g., a gadget cost calculator) and show how it changes into Python.

2. Functions, Types, and Returning Values

Example: Calculating Gadget Cost

Pyret Version:

fun gadget-cost(num-gadgets :: Number, label :: String) -> Number:
  doc: ```Each gadget costs 0.50,
         plus 0.05 per label character```
  num-gadgets * (0.50 + (string-length(label) * 0.05))
end

Python Version:

def gadget_cost(num_gadgets: int, label: str) -> float:
    """Each gadget costs 0.50 plus 0.05 per label character"""
    return num_gadgets * (0.50 + (len(label) * 0.05))

Discussion Points:
- Notice def vs. fun and underscores vs. hyphens.
- Python uses a single colon for type annotations and requires an explicit return statement.
- Python uses indentation instead of explicit end keywords.
Do Now:
- Ask: “What would happen if you omit the return statement in Python? (Hint: Try a small experiment.)”

3. Conditionals in Python

Example: Shipping Cost on an Order

Pyret (for review):

fun add-shipping(order-amt :: Number) -> Number:
  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 Version:

def add_shipping(order_amt: float) -> float:
    """Increase order price by shipping cost"""
    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

Do Now:
- Have students identify how elif in Python corresponds to Pyret’s else if.
Interactive Exercise:
- Ask students to write a Python function that computes a “discounted price” based on a condition (e.g., if the order is over $50, apply a 10% discount; otherwise, no discount).

NOTE: do we need pytest.approx? I guess we should talk about numerics, Pyret is arbitrary precision, which is pretty important.

4. Testing and Examples

Pyret’s where: Blocks vs. Python’s Testing with pytest:

Pyret Example:

fun gadget-cost(num-gadgets :: Number, label :: String) -> Number:
  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 Example Using pytest:

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)

Discussion:
- Python does not have “where:” blocks; tests are written separately.
- Note that type annotations in Python are not enforced at runtime.

5. Wrap-Up (10 minutes)

Recap:
- Reviewed how expressions, functions, and conditionals are written in Python versus Pyret.
- Highlighted key notational differences and the importance of the return statement in Python.

Functions, Types, and Conditionals in Python vs. Pyret​

1. Introduction (10 minutes)​

2. Functions, Types, and Returning Values​

3. Conditionals in Python​

4. Testing and Examples​

5. Wrap-Up (10 minutes)​