Python Basics

1. Overview#

Python is a dynamically typed, interpreted, high-level language built around readability. This note covers the true foundation: how Python stores values, how it makes decisions, and how it repeats work. Everything else in the language — OOP, decorators, async — is built on top of these core mechanics.

Everything in Python is an object, including integers, functions, and modules. Each object carries a reference count, a type descriptor, and a value in memory.

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2. The Type System#

2.1 Built-in Data Types#

2.2 Dynamic Typing#

Python doesn't require declaring a variable's type — the type is attached to the value, not the variable name.

🐍 PythonInteractive WebAssembly
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x = 10 # x refers to an int
x = "ten" # now x refers to a str — completely legal
print(type(x)) # <class 'str'>

2.3 Type Checking#

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value = 42
print(type(value) == int) # Works, but fragile with subclasses
print(isinstance(value, int)) # Preferred — respects inheritance

2.4 Type Conversion (Casting)#

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age_str = "25"
age_int = int(age_str) # "25" -> 25
price = float("19.99") # "19.99" -> 19.99
flag = bool(0) # 0 -> False (falsy)
items = list("abc") # "abc" -> ['a', 'b', 'c']

Falsy values in Python: 0, 0.0, "", [], {}, (), set(), None, False. Everything else is truthy.

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3. Memory Model: Mutability vs Immutability#

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# Immutable strings
text = "hello"
print(id(text))
text += " world"
print(id(text)) # New memory location!

# Mutable lists
data_points = [10, 20, 30]
print(id(data_points))
data_points.append(40)
print(id(data_points)) # Same memory location

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4. Operators#

4.1 Arithmetic & Comparison#

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a, b = 17, 5
print(a // b) # 3 — floor division
print(a % b) # 2 — modulo
print(a ** b) # 1419857 — exponentiation
print(a != b) # True

4.2 Logical Operators#

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age = 25
has_id = True
print(age >= 18 and has_id) # True
print(age < 18 or has_id) # True
print(not has_id) # False

4.3 Identity vs Equality#

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a = [1, 2, 3]
b = [1, 2, 3]
c = a

print(a == c) # True — same value
print(a is c) # True — same object
print(a == b) # True — same value
print(a is b) # False — different objects in memory

4.4 The Walrus Operator (:=)#

Introduced in Python 3.8 — assigns and returns a value in the same expression, reducing repeated computation.

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data = [1, 2, 3, 4, 5, 6, 7, 8]

# Without walrus
n = len(data)
if n > 5:
 print(f"List is long: {n} items")

# With walrus
if (n := len(data)) > 5:
 print(f"List is long: {n} items")

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5. Control Flow#

5.1 Conditionals#

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score = 82

if score >= 90:
 grade = "A"
elif score >= 75:
 grade = "B"
elif score >= 60:
 grade = "C"
else:
 grade = "F"

print(grade) # B

5.2 for Loops & range#

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for i in range(0, 10, 2): # start, stop, step
 print(i) # 0, 2, 4, 6, 8

for index, name in enumerate(["Asha", "Ravi", "Meera"]):
 print(index, name)

5.3 while Loops#

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attempts = 0
while attempts < 3:
 print(f"Attempt {attempts + 1}")
 attempts += 1

5.4 break, continue, and the Loop else#

The else block on a loop runs only if the loop completes without hitting break — useful for search patterns.

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numbers = [4, 8, 15, 16, 23, 42]
target = 99

for n in numbers:
 if n == target:
 print("Found it!")
 break
else:
 print("Target not found") # This runs, since break never triggered

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6. Core Data Structures#

6.1 Lists — Ordered, Mutable#

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fruits = ["apple", "banana", "cherry"]
fruits.append("date")
fruits.insert(1, "avocado")
fruits.remove("banana")
print(fruits[-1]) # date — negative indexing
print(fruits[1:3]) # slicing

6.2 Tuples — Ordered, Immutable#

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coordinates = (12.9716, 77.5946) # lat, long
lat, long = coordinates # unpacking
print(f"Lat: {lat}, Long: {long}")

6.3 Dictionaries — Key-Value Pairs#

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user = {"name": "Kamal", "role": "Engineer"}
user["company"] = "Hyperthink Systems" # add new key
print(user.get("email", "Not provided")) # safe access with default

for key, value in user.items():
 print(f"{key}: {value}")

6.4 Sets — Unique, Unordered#

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tags_a = {"python", "ai", "backend"}
tags_b = {"python", "ml", "frontend"}

print(tags_a & tags_b) # intersection: {'python'}
print(tags_a | tags_b) # union
print(tags_a - tags_b) # difference: {'ai', 'backend'}

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7. String Formatting#

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name = "Kamal"
score = 95.5

# f-strings (preferred, Python 3.6+)
print(f"{name} scored {score:.1f}%")

# .format() method
print("{} scored {:.1f}%".format(name, score))

# % formatting (legacy, still seen in older code)
print("%s scored %.1f%%" % (name, score))

7.1 Useful f-string Tricks#

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value = 3.14159265
print(f"{value:.2f}") # 3.14 — 2 decimal places
print(f"{1000000:,}") # 1,000,000 — thousands separator
print(f"{'text':>10}") # right-align within 10 chars
print(f"{value=}") # value=3.14159265 — debug-friendly, Python 3.8+

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8. Idiomatic Python Patterns#

8.1 List & Dict Comprehensions#

Comprehensions run at C-level speed inside the interpreter, making them faster and more readable than manual loops.

🐍 PythonInteractive WebAssembly
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raw_scores = [45, 88, 92, 31, 78, 99, 100]
high_scores = [score for score in raw_scores if score >= 80]

feature_names = ["age", "income", "credit_score"]
feature_idx_map = {name: idx for idx, name in enumerate(feature_names)}

8.2 Context Managers (with statement)#

Ensures deterministic resource cleanup (files, DB connections, locks).

🐍 PythonInteractive WebAssembly
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class ModelArtifactManager:
 def __init__(self, filepath):
 self.filepath = filepath

 def __enter__(self):
 self.file = open(self.filepath, "w")
 return self.file

 def __exit__(self, exc_type, exc_val, exc_tb):
 self.file.close()
 return False

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9. Common Pitfalls#

INCORRECT: Mutable Default Arguments#

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def append_prediction(val, container=[]):
 container.append(val)
 return container

print(append_prediction(1)) # [1]
print(append_prediction(2)) # [1, 2] -> shared across calls!

CORRECT: Fix#

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def append_prediction(val, container=None):
 if container is None:
 container = []
 container.append(val)
 return container

INCORRECT: Modifying a List While Iterating#

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nums = [1, 2, 3, 4, 5]
for n in nums:
 if n % 2 == 0:
 nums.remove(n) # skips elements — unpredictable results

CORRECT: Fix#

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nums = [1, 2, 3, 4, 5]
nums = [n for n in nums if n % 2 != 0]

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10. Summary & Best Practices Checklist#

• Use is for identity/None checks, == for value equality.
• Prefer f-strings for readability and performance.
• Never use a mutable object (list, dict) as a default argument.
• Use enumerate() instead of manual index counters.
• Use comprehensions for simple transforms; fall back to loops when logic gets complex.
• Use dict.get() with a default instead of risking a KeyError.
• Reach for tuples when data shouldn't change (e.g., coordinates, RGB values).