Practical Python Practice

Practise real-world tasks, working with SQLite databases, CSV files, folders of files, JSON from APIs, and HTML pages, all without needing an internet connection.

Intermediate7 problemsUpdated October 23, 2026

Review first: Database Connections, Automation, Working With APIs, Web Scraping, Useful Python Scripts

1.Store and query data with SQLite
Intermediate
Using the built-in sqlite3 module and an in-memory database, create a products table, insert three products with a parameterised query, then print the products cheaper than 1000, cheapest first.
Show hint
Use ? placeholders and pass the values separately: cur.execute("... WHERE price < ?", (1000,)). executemany inserts a list of rows.
Show solution
Python
import sqlite3 conn = sqlite3.connect(":memory:") cur = conn.cursor() cur.execute("CREATE TABLE products (name TEXT, price INTEGER)") cur.executemany( "INSERT INTO products (name, price) VALUES (?, ?)", [("Notebook", 60), ("Headphones", 2500), ("Pen Set", 250)], ) conn.commit() for name, price in cur.execute( "SELECT name, price FROM products WHERE price < ? ORDER BY price", (1000,) ): print(name, price) conn.close()
Output
Notebook 60 Pen Set 250
Never build SQL by joining strings with user input (for example f"... WHERE name = '{name}'"). Placeholders keep the values separate from the SQL, which prevents SQL injection.

2.Totals from a CSV file

Intermediate

Write this CSV to sales.csv, then read it with csv.DictReader and print the total amount for each region (in alphabetical order):

Text

region,amount
North,1200
South,800
North,450
East,990
South,300

Show hint

DictReader gives each row as a dictionary like {"region": "North", "amount": "1200"}. The amounts are strings, so convert them.

Show solution

Python

import csv

data = """region,amount
North,1200
South,800
North,450
East,990
South,300
"""

with open("sales.csv", "w", encoding="utf-8", newline="") as f:
    f.write(data)

totals = {}
with open("sales.csv", encoding="utf-8", newline="") as f:
    for row in csv.DictReader(f):
        totals[row["region"]] = totals.get(row["region"], 0) + int(row["amount"])

for region in sorted(totals):
    print(region, totals[region])

Output

East 990
North 1650
South 1100

Open CSV files with newline="", as the csv module documentation recommends, so line endings are handled correctly on every operating system.

3.Organise files by extension

Intermediate

Create a folder downloads containing report.pdf, photo.jpg, notes.txt, scan.pdf and holiday.jpg. Then move each file into a subfolder named after its extension (pdf, jpg, txt) and print the resulting layout.

Show hint

pathlib.Path makes this easy: path.suffix gives ".pdf", folder.mkdir(exist_ok=True) creates a folder, and path.rename(target) moves a file.

Show solution

Python

from pathlib import Path

downloads = Path("downloads")
downloads.mkdir()
for name in ["report.pdf", "photo.jpg", "notes.txt", "scan.pdf", "holiday.jpg"]:
    (downloads / name).write_text("sample")

for path in list(downloads.iterdir()):
    if path.is_file():
        folder = downloads / path.suffix.lstrip(".")
        folder.mkdir(exist_ok=True)
        path.rename(folder / path.name)

for path in sorted(downloads.rglob("*")):
    if path.is_file():
        print(path.relative_to(downloads).as_posix())

Output

jpg/holiday.jpg
jpg/photo.jpg
pdf/report.pdf
pdf/scan.pdf
txt/notes.txt

The loop uses list(downloads.iterdir()) so the folder listing is read once, before files start moving. as_posix() prints forward slashes on every operating system.

4.Read a JSON API response

Intermediate

An API returned the JSON below. Print each user's name and city, and print unknown for a missing city. Then print how many users are active.

JSON

{"users": [
  {"name": "Asha", "address": {"city": "Pune"}, "active": true},
  {"name": "Ravi", "address": {}, "active": false},
  {"name": "Meera", "address": {"city": "Delhi"}, "active": true}
]}

Show hint

json.loads(text) turns the JSON into dictionaries and lists. Use .get("city", "unknown") for keys that might be missing. With the requests library, response.json() does the parsing for you.

Show solution

Python

import json

text = """{"users": [
  {"name": "Asha", "address": {"city": "Pune"}, "active": true},
  {"name": "Ravi", "address": {}, "active": false},
  {"name": "Meera", "address": {"city": "Delhi"}, "active": true}
]}"""

data = json.loads(text)

for user in data["users"]:
    city = user.get("address", {}).get("city", "unknown")
    print(user["name"], city)

print(sum(1 for user in data["users"] if user["active"]), "active users")

Output

Asha Pune
Ravi unknown
Meera Delhi
2 active users

Real API data is often incomplete, so reading nested values with .get() and sensible defaults keeps your program from crashing on one bad record. Notice that JSON's true becomes Python's True.

5.Extract data from HTML

Intermediate

Using BeautifulSoup, extract each product's name and price from this HTML (already downloaded), and print the cheapest product.

HTML

<ul id="products">
  <li class="product"><h2>Laptop</h2><span class="price">₹55,000</span></li>
  <li class="product"><h2>Headphones</h2><span class="price">₹2,500</span></li>
  <li class="product"><h2>Monitor</h2><span class="price">₹12,000</span></li>
</ul>

Show hint

soup.select("li.product") finds the items; item.select_one(".price").get_text() reads the price text. Remove ₹ and commas before converting to int.

Show solution

Python

from bs4 import BeautifulSoup

html = """
<ul id="products">
  <li class="product"><h2>Laptop</h2><span class="price">₹55,000</span></li>
  <li class="product"><h2>Headphones</h2><span class="price">₹2,500</span></li>
  <li class="product"><h2>Monitor</h2><span class="price">₹12,000</span></li>
</ul>
"""

soup = BeautifulSoup(html, "html.parser")

products = []
for item in soup.select("li.product"):
    name = item.h2.get_text(strip=True)
    price_text = item.select_one(".price").get_text(strip=True)
    price = int(price_text.replace("₹", "").replace(",", ""))
    products.append((name, price))
    print(name, price)

cheapest = min(products, key=lambda p: p[1])
print("Cheapest:", cheapest[0])

Output

Laptop 55000
Headphones 2500
Monitor 12000
Cheapest: Headphones

Install BeautifulSoup with python -m pip install beautifulsoup4. On a live website you would first download the page with requests.get(url).text, after checking that the site allows scraping.

6.Top words in a text

Intermediate

Print the three most common words in text, ignoring case and punctuation, and skipping the common words the, a, and and of.

Show hint

re.findall(r"[a-z']+", text.lower()) extracts the words. Filter out the stop words, then use Counter.most_common(3).

Show solution

Python

import re
from collections import Counter

text = """The cat sat on the mat. The cat was happy, and the mat was warm.
A dog saw the cat and the mat, and the dog sat too."""

stop_words = {"the", "a", "and", "of"}

words = re.findall(r"[a-z']+", text.lower())
counts = Counter(w for w in words if w not in stop_words)

for word, count in counts.most_common(3):
    print(word, count)

Output

cat 3
mat 3
sat 2

sat, was and dog all appear twice. When counts tie, most_common keeps the order in which the words were first seen, so sat comes first.

7.Add dates to file names

Intermediate

Create the files invoice.pdf and receipt.pdf in a folder bills. Rename each one so its name starts with a fixed date, giving 2026-10-23_invoice.pdf, and print the new names. (A real script would use today's date.)

Show hint

path.with_name(new_name) builds the new path in the same folder; date.isoformat() gives YYYY-MM-DD.

Show solution

Python

from datetime import date
from pathlib import Path

bills = Path("bills")
bills.mkdir()
for name in ["invoice.pdf", "receipt.pdf"]:
    (bills / name).write_text("sample")

stamp = date(2026, 10, 23).isoformat()  # use date.today() in a real script

for path in sorted(bills.iterdir()):
    path.rename(path.with_name(f"{stamp}_{path.name}"))

print(sorted(p.name for p in bills.iterdir()))

Output

['2026-10-23_invoice.pdf', '2026-10-23_receipt.pdf']

Putting dates in YYYY-MM-DD form at the start of a file name makes files sort in date order automatically.

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