Python Lists and Dictionaries: 10 Assignments With Answers in Python

Lists and dicts carry 80% of early Python logic. This workbook gives you 10 focused assignments — with worked solutions — so indexing, slicing, and key handling become reflex.

Who is this workbook for?

Career-switchers who can read a for loop but still pause on "do I append or extend?" and "why did my dict lookup KeyError?" Pair with Python fundamentals master for strings/regex and String assignments for text twin. For tabular next steps, see Pandas fundamentals.

Ingredients: 10 numbered assignments tied to two YouTube references (Lists Basics, Dictionaries Easy), each with a solution cell.

How do you create, access, and modify lists?

# Q1: Create and access
fruits = ["apple", "banana", "cherry"]
print(fruits[0])  # apple — index 0 is first
 
# Q2: Modify index 1
fruits[1] = "orange"
print(fruits)  # ['apple','orange','cherry']
 
# Q3: Append & remove
fruits.append("grape")
fruits.remove("apple")
print(fruits)  # ['orange','cherry','grape']

Rendered output after Q1-Q3: fruits evolves apple/banana/cherry -> apple/orange/cherry -> orange/cherry/grape, showing alias-free reassignment. Question prompt links to Video 1 at 1:17 for .append() vs .remove().

How do slicing and iteration work?

# Q4: Slicing — stop is exclusive
numbers = [0, 1, 2, 3, 4, 5]
print(numbers[1:4])   # [1, 2, 3]
print(numbers[-2:])   # [4, 5]  last 2
 
# Loop a list safely
for fruit in fruits:
    print(fruit.title())

The slice 1:4 returns indexes 1,2,3 — the classic off-by-one interview check. Negative -2: means "from second-last to end".

How do you create and use dictionaries?

# Q5: Create
capitals = {"USA": "Washington DC", "India": "New Delhi", "China": "Beijing", "Russia": "Moscow"}
print(capitals)
 
# Q6: Access
print(capitals["India"])               # New Delhi
print(capitals.get("France", "Unknown"))  # safe default instead of KeyError
 
# Q7: Update & merge
capitals["France"] = "Paris"
capitals.update({"Japan": "Tokyo", "India": "New Delhi"})  # upsert
print(capitals)

Rendered output: the dict grows from 4 to 6 keys; get avoids the crash the unguided solution often hits on missing keys.

# Q8-Q10: Iterate and nest
for country, city in capitals.items():
    print(f"{country}: {city}")
 
# Dict of lists — mini database
students = {"Alice": [85, 90, 78], "Bob": [72, 88, 91]}
print(students["Alice"][1])  # 90 — second score

What next after you finish?

Use lists to collect pd.read_csv chunks and dicts to hold column dtypes before a DataFrame constructor. Then advance to NumPy foundations for vectorised cousins of the same loops.

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.