Python Gave Me a WRONG Answer With No Error (Here's Why) | Python for Data Science #2

Python summed my column and gave me a number. No crash. No warning. Completely wrong answer — and I didn't catch it for days. This is the silent bug that kills real data science work, and it comes down to one thing: data types. In this tutorial you will learn exactly how this happens and how to prevent it. We go through all four primitive types Python uses, why a number stored as a string will concatenate instead of add, how to check and convert types before any calculation, lists and list comprehensions (the one-liner that replaces 3-line for loops), dictionaries and the list-of-dicts pattern that mirrors how pandas stores data internally, and functions — the habit that turns messy notebooks into trustworthy code. By the end you will understand what type every piece of your data is, how to catch type mistakes before they produce plausible-looking wrong answers, and why learning these structures first means you will understand pandas intuitively instead of memorizing methods. ⏱️ TIMESTAMPS 0:00 The silent bug — Python gave me the wrong answer with no error 2:00 What this tutorial covers 3:05 The 4 primitive types: int, float, str, bool 6:30 Type checking and type conversion 9:12 Rule of thumb: CSV data always arrives as strings 9:25 Lists — how to store and access sequences 10:41 Indexing, slicing, and negative indexes 11:10 Append and list comprehension (one-liner that replaces 3 lines) 14:52 F-string formatting inside list comprehensions 15:28 Dictionaries — how data scientists think about records 17:54 Adding and updating dictionary keys safely 18:34 List of dictionaries — the real-world data pattern 21:15 Functions — write once, reuse everywhere 22:50 Default arguments and when to use them 24:39 Building an outlier detector in pure Python 26:20 Why all of this matters before you touch pandas 📋 Free worksheet (practice problems + cheat sheet): https://worksheets-thebreathnetwork.v... This is Tutorial 2 of a 10-part Python for Data Science series built for working professionals. Practical from day one, no fluff. ▶ Tutorial 1: Your First Python Script for Data Science ▶ Tutorial 3: NumPy Arrays — When Your Data Has a Million Rows