Unit 2: Functions and data structures

pythondef area(l, b=1):            # default parameter
    return l * b
print(area(5), area(5, 4))   # 5 20

square = lambda x: x * x     # anonymous function
print(square(6))             # 36

count = 0                    # global
def increment():
    global count             # needed to change a global
    count += 1

• Scope: a variable created inside a function is local and exists only during the call; variables at the top level are global.
• assert checks a condition while debugging: assert marks >= 0, "Marks cannot be negative".
• Command-line arguments: import sys; print(sys.argv) — sys.argv[0] is the script name.
• Modules: a .py file of functions; use import mymodule or from mymodule import area.

pythonmarks = [70, 82, 65]
marks.append(90); marks.insert(0, 55); marks.remove(65); marks.sort()
print(max(marks), min(marks), sum(marks) / len(marks))
student = {"name": "Ana", "roll": 12}
student["course"] = "BCA"
for k, v in student.items():
    print(k, v)

• Mutable objects (list, dict, set) can be changed in place; when passed to a function, changes inside the function affect the caller's object.
• Immutable objects (int, float, str, tuple) cannot be changed; "changing" creates a new object.

pythonimport math
print(math.sqrt(16), math.factorial(5), math.pi, math.ceil(4.2))

import numpy as np
a = np.array([1, 2, 3, 4, 5, 6])
print(a.max(), a.min(), a.mean())
b = a.reshape(2, 3)              # 2 rows, 3 columns
print(np.dot([1, 2], [3, 4]))    # 11
z = np.zeros((2, 2)); f = np.full((2, 2), 7)

NumPy arrays are faster than lists for numerical work and support element-wise operations (a * 2 doubles every element).