Why Confidence Without Python Skills Still Fails

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

print(a == b)   # True  - same values
print(a is b)   # False - two different list objects
print(a is c)   # True  - c points to the same object

x = None
print(x is None)   # the correct way to check for None

# Small integers may appear to work with 'is'
# because CPython caches some of them. Never rely on it:
# use == for values, 'is' for identity and None checks.

Candidate: I am very strong in Python.

Interviewer: Good. What is the difference between
is and ==?

Candidate: Both check if things are equal.

Interviewer: Then why does a is b give False for
two lists with the same items?

Candidate: ... maybe a bug?

(The claim set a high bar. The answer fell well
below it. The gap is now the main memory.)

from collections import Counter
import re

text = """Python is easy to start. Python is hard to master.
Mastery takes practice."""

words = re.findall(r"[a-z]+", text.lower())
counts = Counter(words)

print(counts.most_common(3))
# [('python', 2), ('is', 2), ('to', 2)]

# Without Counter:
manual = {}
for w in words:
    manual[w] = manual.get(w, 0) + 1

# Spoken: "Counter is a dict subclass for counting.
# most_common returns the top items. The manual version
# is O(n). Calling words.count(w) for each word would be
# O(n squared)."

def error_lines(path):
    """Yield ERROR lines one at a time, without loading the file."""
    with open(path, encoding="utf-8") as f:
        for line in f:              # file objects are lazy iterators
            if "ERROR" in line:
                yield line.rstrip("\n")

# Count errors in a very large log using little memory
total = sum(1 for _ in error_lines("app.log"))
print("errors:", total)

# Spoken: "Iterating over the file reads one line at a time,
# and the generator passes lines on without storing them.
# The with block closes the file even if something fails
# halfway through."

"I am an expert in Python, Django, Flask, FastAPI,
pandas, NumPy, machine learning and automation."

What the interviewer thinks:
- Eight areas, no depth signal
- Will test the hardest one
- Probably shallow in most

"My strongest area is backend Python. I built a
Flask API for an expense tracker with SQLAlchemy and
pytest tests, and I am comfortable with core Python:
data structures, generators, file handling.

I have used pandas for small data-cleaning tasks.
I am still learning async Python."

What the interviewer thinks:
- Clear strong zone to test
- Evidence to ask about
- Honest edges

CLAIM CHECK (per skill)

Skill: ______________
[ ] I can write a small example from memory
[ ] I can explain why the key line works
[ ] I can name one edge case or pitfall
[ ] I have used it in a project I can show

4 ticks: call it strong
2-3 ticks: say 'have worked with'
0-1 ticks: practise first, or remove it

LIVE TASKS (each in under 10 minutes, no notes)

[ ] Word frequency with Counter and without
[ ] Read a large file line by line with a generator
[ ] Parse a CSV with the csv module, skip bad rows
[ ] Remove duplicates while keeping order
[ ] Merge two dicts and handle key conflicts
[ ] Write a class with __init__, __repr__ and a method
[ ] Handle a ValueError from int() on user input
[ ] Sort a list of dicts by two keys

After each: explain it aloud in one minute.

WHY QUESTIONS (answer aloud, 45 seconds each)

1. Why use 'is None' instead of '== None'?
2. Why is a generator better for a huge file?
3. Why does with open() matter if I call close()?
4. Why can a tuple be a dict key but a list cannot?
5. Why is ''.join(parts) better than += in a loop?
6. Why does Counter need hashable items?

If any answer starts with 'because that is how
it is', study that one first.