Welcome to the first Python Challenge in my #PythonToML journey!
In this post, I break down each mind-bending expression from the challenge and explain what’s really happening under the hood.
Let’s dive right into the puzzle pieces..
Answer: False
Explanation:
Due to floating-point precision errors in binary, 0.1 + 0.2 results in 0.30000000000000004, not exactly 0.3. So the expression evaluates to:
x = False
Answer: [False, 'T', 'r', 'u', 'e']
Explanation:
You’re concatenating:
An empty list []
A list with a single Boolean: [False]
A list made from the string "True" → ['T', 'r', 'u', 'e']
Result: [False, 'T', 'r', 'u', 'e']
Answer:
True
False
Explanation:
Python caches small integers (from -5 to 256) as a performance optimization. So:
256 is 256 → True (both point to the same object)
257 is 257 → False (different objects beyond the cached range)
Answer: "No"
Explanation:
Since a = 5 and b = 7, a > b and a == b are both false. So it returns "No" from the last else.
Answer: (9,)
Explanation:
Notice the comma at the end! That makes it a tuple with a single element.
So the value inside is 9, but it’s wrapped in a tuple.
Answer: [1, 2, 3, 1, 2, 3, 1, 2, 3]
Explanation:
x * 2 duplicates the list: [1, 2, 3, 1, 2, 3]
Then you concatenate it with x: [1, 2, 3]
Result: [1, 2, 3, 1, 2, 3, 1, 2, 3]
Answer: 2 1
Explanation:
This is a chained assignment. The rightmost a = 1, b = 2 executes first.
Then a, b = b, a becomes a = 2, b = 1
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🧵 Takeaway
These tricky Python expressions aren’t just clever gotchas, they reveal deeper truths about how Python handles memory, data types, and syntax quirks.
Want more?
👉 Follow the series and challenge yourself with fresh Python & ML puzzles.
Let’s learn, debug, and grow together.
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