Foundation·Concept·3 min read

Python basics, lists, dictionaries, and sets

Practice this question

WANT TO UNDERSTAND THE CONCEPT FIRST?Learn Python basics, lists, dictionaries, and sets

🔴 MUST KNOW · Learn enough to operate AI services; keep existing MERN responsibilities in TypeScript when appropriate.

Overview

Python is a dynamically typed language widely used by AI libraries.
A list is an ordered collection, a dictionary maps keys to values, and a set stores unique hashable values.
Python uses indentation to define code blocks rather than braces.

Why this matters

Many document-processing, model-training, numerical, and evaluation libraries are easiest to use in Python.

An everyday example

A list is a queue of documents; a dictionary is a labeled filing cabinet; a set is a guest list without duplicates.

A technical example

chunks = ["policy", "claim"]; metadata = {"tenant_id": "t1"}; unique_ids = {"C1", "C2"}.

How it works

Create values
Use indexing or key lookup
Loop or use comprehensions
Pass results to functions.

Lists and dictionaries are mutable; sharing them can create accidental changes.

Architecture and flow

Parsed documents → lists of chunks → dictionaries of metadata → unique IDs for deduplication.

The insurance assistant example

A worker extracts policy paragraphs, attaches source metadata, and deduplicates document identifiers.

When to use it

For Python-facing data preparation and AI libraries.

When to avoid it

Do not rewrite a working Node service merely to use a hosted LLM API.

Interview answer

“I use Python where its AI ecosystem provides practical benefits and keep collection types explicit so ingestion and evaluation code remain readable.”

Benefits, trade-offs, and production considerations

TypeScript interfaces and Python type hints do not provide runtime validation.
Python's None, True, False, and, or, and not correspond roughly to JS's null, booleans, and logical operators, but truthiness and equality rules differ.

TypeScript/JavaScript	Python	Practical difference
`const x = null`	`x = None`	Check absence with `x is None`
`string[]` / array	`list[str]`	Lists may contain mixed types unless validated
object / `Map`	`dict[str, str]`	Dictionary keys must be hashable
`Set<string>`	`set[str]`	Empty set is `set()`, not `{}`
`===`	`==` for value equality	`is` checks identity, not general equality
`arr.map(fn)`	`[fn(x) for x in arr]`	Comprehensions are common
`try/catch/finally`	`try/except/finally`	Catch specific exceptions
template literal	`f"claim {claim_id}"`	Never use interpolation to construct SQL
braces	indentation	Usually four spaces per indentation level

# No third-party dependencies: basic data preparation.
documents = [
    {"id": "p1", "text": "Claim reporting instructions", "active": True},
    {"id": "p2", "text": "Old wording", "active": False},
]
active_texts: list[str] = [d["text"] for d in documents if d["active"]]
seen_ids: set[str] = {d["id"] for d in documents}
for index, text in enumerate(active_texts):
    print(index, text)

Adapted from 13_AI.MD · “Python basics, lists, dictionaries, and sets”. Code examples are study fragments. Difficulty and core labels are editorial estimates; version-specific behavior should be checked against your project.