Python Fundamentals Master: Strings, Regex, and Data Structures in 30 Questions

Thirty questions that turn scattered Python syntax into a coherent base — strings, regex, lists, dicts, comprehensions, and file handling — with solutions you rerun until they are one-liners.

What ladder do the 30 questions climb?

Strings & regex (Q1-10) -> Lists (Q11-18) -> Dicts/sets (Q19-24) -> Comprehensions & functions (Q25-30), each mapped to focused video references (Strings & Methods, Regex, Lists Basics). If you prefer a smaller set first, do String assignments 5Q then return — the vocab repeats by design. Tabular continuation is Pandas fundamentals.

Ingredients: 30 prompts with starter variables (course = "Python Programming", text = " hello world " etc.) so you only write the targeted verb.

How do you master strings and regex?

# Q1-Q4: Slicing, negative indexing, methods, f-strings
course = "Python Programming"
print(course[0:6])        # Python — stop exclusive
print(course[-1])         # g — last char
text = "   hello world   "
print(text.strip().upper())  # HELLO WORLD
 
name="Alice"; age=30
print(f"My name is {name} and I am {age} years old.")
 
# Q5-Q6: Regex — digits & email
import re
print(re.findall(r'\d+', "Order 42 has 7 items"))  # ['42','7']
email_pat = r'[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}'
print(bool(re.match(email_pat, "student.name@university.edu")))  # True
 
# Q7: Split & join
print("a,b,c".split(","))
print("-".join(["Top","folio"]))

Rendered output showcases the four string invariants: slicing never IndexErrors out of range (it truncates), strip() removes only ends, f-strings beat +, and findall returns a list that may be empty — always check length before indexing.

How do you use lists, dicts, and comprehensions?

# Lists — append/extend/slice
fruits = ["apple","banana","cherry"]
fruits[1] = "orange"
fruits.append("grape")
print(fruits[1:3])  # ['orange','cherry']
 
# Dicts — get, update, iteration
capitals = {"USA":"Washington DC", "India":"New Delhi"}
capitals["France"] = "Paris"
print(capitals.get("Japan","Unknown"))
for k,v in capitals.items():
    print(k, "->", v)
 
# Comprehension: squares of evens
print([x*x for x in range(6) if x%2==0])  # [0, 4, 16]
print({k: len(v) for k,v in capitals.items()})  # city name lengths

# File handling — minimal safe pattern
path = "/tmp/demo.txt"
with open(path, "w") as f:
    f.write("Topfolio\n")
with open(path) as f:
    print(f.read())

Where do you go after fundamentals?

Use comprehensions to build DataFrame inputs and regex to clean Review text per VoC text mining. Then start tabular work in Pandas fundamentals — the syntax you just drilled reappears as .str and apply.

Download the Notebook and Practise

This article is a walkthrough of a runnable Jupyter notebook. Download the original .ipynb and run it locally or on Colab — every code block above appears in order.

Continue your track: Data Analyst Roadmap · Python and Pandas Guide · SQL NULL Handbook · SQL JOIN Fan-Out · Topfolio Practice · Data Analyst vs Engineer

Dataset generators where applicable are in courses/workbooks/generators/ — see citations atop for the exact *.py source for this notebook.

Frequently Asked Questions

What is the difference between slicing and indexing?

Indexing returns one element (s[0]), slicing returns a subsequence (s[0:6]). Slicing never raises IndexError out of range — it truncates safely.

How do f-strings work?

Prefix with f and embed {expr}: f"{name} is {age}" evaluates expressions inline. Faster and clearer than + concatenation.

When do you use regex vs string methods?

String methods (.split, .replace) for fixed patterns; re for variable patterns like emails or digits (re.findall(r'\d+', text)).

What is the gotcha with mutable defaults?

def f(lst=[]): reuses the same list across calls. Use def f(lst=None): lst = [] if lst is None.