Common Python Programming Career Myths Busted for 2026

from pathlib import Path
import shutil

# Sort files in a folder into subfolders by extension
SOURCE = Path("downloads_practice")
SOURCE.mkdir(exist_ok=True)
(SOURCE / "report.pdf").touch(); (SOURCE / "photo.jpg").touch()

for f in SOURCE.iterdir():
    if f.is_file():
        target = SOURCE / (f.suffix.lstrip(".") or "other")
        target.mkdir(exist_ok=True)
        shutil.move(str(f), target / f.name)
        print(f"moved {f.name} -> {target.name}/")

# pip install flask
from flask import Flask, jsonify, request

app = Flask(__name__)
notes = []

@app.post("/notes")
def add_note():
    text = (request.get_json() or {}).get("text", "").strip()
    if not text:
        return jsonify(error="text is required"), 400
    notes.append(text)
    return jsonify(id=len(notes), text=text), 201

@app.get("/notes")
def list_notes():
    return jsonify(notes=notes)

if __name__ == "__main__":
    app.run(debug=True)

import time
import numpy as np

values = list(range(1_000_000))

start = time.perf_counter()
total = 0
for v in values:
    total += v * v
loop_time = time.perf_counter() - start

arr = np.arange(1_000_000, dtype=np.int64)
start = time.perf_counter()
total_np = int((arr * arr).sum())
np_time = time.perf_counter() - start

print("same result:", total == total_np)
print(f"pure Python loop: {loop_time:.3f}s")
print(f"NumPy vectorised: {np_time:.4f}s")

import cProfile
import pstats

def slow_part():
    return sum(i * i for i in range(300_000))

def fast_part():
    return sum(range(1000))

def main():
    for _ in range(5):
        slow_part()
        fast_part()

cProfile.run("main()", "profile.out")
pstats.Stats("profile.out").sort_stats("cumulative").print_stats(5)
# Measure before optimising; fix the part that actually takes time.

# discounts.py
def final_price(price: float, discount_pct: float) -> float:
    """Return price after discount; discount must be 0-90%."""
    if price < 0:
        raise ValueError("price cannot be negative")
    if not 0 <= discount_pct <= 90:
        raise ValueError("discount must be between 0 and 90")
    return round(price * (1 - discount_pct / 100), 2)

# test_discounts.py  (run: pytest -q)
import pytest

def test_normal_discount():
    assert final_price(1000, 10) == 900.0

def test_zero_discount():
    assert final_price(499.99, 0) == 499.99

def test_invalid_discount():
    with pytest.raises(ValueError):
        final_price(100, 95)

# Suppose an assistant suggested:
# def average(nums):
#     return sum(nums) / len(nums)

# Review questions: What if the list is empty? What about None values?
def average(nums: list) -> float | None:
    clean = [n for n in nums if n is not None]
    if not clean:
        return None
    return sum(clean) / len(clean)

print(average([10, 20, None, 30]))   # 20.0
print(average([]))                   # None instead of ZeroDivisionError

Direction        Core libraries             First project
Web back end     FastAPI or Django, SQL     Tested REST API
Automation       pathlib, requests, openpyxl  Report or file automation
Testing          pytest, Playwright/Selenium  API + UI test suite in CI
Data             pandas, SQL, scikit-learn    Analysis with baseline model

Pick one; learn the others later.

Weeks 1-3   Core Python: data types, functions, classes, errors
Weeks 4-5   Git, virtual environments, pytest
Weeks 6-7   SQL basics and one library for your direction
Weeks 8-10  Main project in your direction, with tests and README
Week 11     CI pipeline, code review practice (including AI output)
Week 12     Portfolio, resume and interview practice

[ ] Write functions with type hints and docstrings
[ ] Handle errors and invalid input
[ ] Test with pytest
[ ] Use Git branches and pull requests
[ ] Query a database with SQL
[ ] Explain one performance trade-off
[ ] Review generated code before using it