10 Python Projects for Placement Interviews (with Source Code)

The interviewer does not care that you built a to-do app. They have seen 400 to-do apps this quarter. What they care about is whether you can talk for 15 minutes about a technical decision you made — why you chose SQLite over Postgres, why you used threading instead of asyncio, what broke in production and how you debugged it.

That is what separates the shortlisted resume from the rejected one. So this list is not the 100 generic project ideas you have already seen. It is 10 projects ranked by difficulty, each with a real code stub, and the exact interview-worthy angle that makes it stand out.

Level 1 — warm-up projects that show you can ship

1. Command-line weather app (Beginner, ~2 hours)

import requests, sys

city = sys.argv[1] if len(sys.argv) > 1 else "Bangalore"
url = f"https://wttr.in/{city}?format=j1"
data = requests.get(url).json()
current = data["current_condition"][0]
print(f"{city}: {current['temp_C']}C, {current['weatherDesc'][0]['value']}")

Why it impresses: You can talk about API rate limits, timeout handling, and why you did not hardcode the API key. Interviewer's follow-up: "what happens if wttr.in is down?" — have an answer (retry with backoff, fallback to a cache).

2. PDF merger and splitter (Beginner, ~3 hours)

from pypdf import PdfWriter

writer = PdfWriter()
for path in ["resume.pdf", "marksheet.pdf", "projects.pdf"]:
    writer.append(path)
writer.write("placement_packet.pdf")
writer.close()

Why it impresses: Solves a real problem. Bonus points for a Tkinter GUI or a drag-and-drop web version.

3. Personal spending tracker with CSV export (Beginner, ~4 hours)

import sqlite3, datetime

conn = sqlite3.connect("spending.db")
conn.execute("""CREATE TABLE IF NOT EXISTS txn
    (id INTEGER PRIMARY KEY, date TEXT, category TEXT, amount REAL, note TEXT)""")
conn.execute("INSERT INTO txn (date, category, amount, note) VALUES (?, ?, ?, ?)",
    (datetime.date.today().isoformat(), "food", 240, "swiggy dinner"))
conn.commit()

Why it impresses: Shows schemas, transactions, and why you did not just use a CSV.

Level 2 — projects that actually get callbacks

4. Hacker News scraper with SQLite storage (Intermediate, ~6 hours)

Scrape the front page every hour, dedupe by URL, expose a Flask endpoint returning the top stories from the last 24 hours ranked by score.

Why it impresses: Real scraping, real storage, real API. Interviewer follow-ups: "How do you handle HN blocking your IP?", "How do you dedupe across runs?"

5. Todo REST API with FastAPI + JWT auth (Intermediate, ~8 hours)

Not a todo app. A todo API. Users, auth, CRUD, deployed to Railway or Fly.io with a real URL you can share.

from fastapi import FastAPI, Depends
from pydantic import BaseModel

app = FastAPI()

class Todo(BaseModel):
    title: str
    done: bool = False

@app.post("/todos")
def create_todo(todo: Todo, user=Depends(get_current_user)):
    return db.insert(user.id, todo)

Why it impresses: You can whiteboard REST design. You know why bcrypt beats sha256 for password hashing.

6. Instagram auto-reply bot (Intermediate, ~6 hours)

Uses the official Instagram Graph API. OAuth flow, webhook handling, rate limits. Add a small LLM call for context-aware replies and now you have an AI project too.

7. Quiz app with spaced repetition (Intermediate, ~5 hours)

SM-2 algorithm (the one Anki uses). You implemented a real algorithm. This is the project that gets the interviewer curious.

Level 3 — projects that move you ahead in the pipeline

8. Real-time stock portfolio dashboard (Advanced, ~12 hours)

WebSocket connection to a stock API, live updating dashboard. P&L calculated in real time.

import asyncio, websockets, json

async def stream_prices(symbols):
    async with websockets.connect(WS_URL) as ws:
        await ws.send(json.dumps({"subscribe": symbols}))
        async for msg in ws:
            tick = json.loads(msg)
            await update_portfolio(tick)

9. YouTube video summarizer with LLM (Advanced, ~10 hours)

Download transcript via youtube-transcript-api, chunk it, send to a free LLM (Groq or Gemini), return bullet summary + timestamped chapters.

10. Distributed web crawler with Redis queue (Advanced, ~15 hours)

Multiple worker processes pulling URLs from a Redis queue, respecting robots.txt, rate-limiting per domain. This is a system-design interview in code form.

Which three to actually build (not all 10)

Do not build all 10. Build one from each level, deeply. Three well-shipped, well-tested projects with real README files, real deployments, and real GitHub commits over 2–3 weeks beat 10 half-finished repos every time.

PyRun's Projects track walks through three flagship builds — HN scraper, spending tracker API, and Telegram summarizer bot — with the exact interview follow-ups an SDE-1 interviewer will ask. You can run every project in the browser without setting up a local Python environment.

The takeaway: Interviewers judge depth over breadth. Three real projects with commit history and defensible decisions beat ten half-abandoned repos every single time.

Next: Start with Project #1 on PyRun — the HN Scraper walkthrough →