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Session 7 | Homework

In this homework, you will use pandas to inspect, clean, and summarise a dataset. Work slowly and check your output after every step.

1. Goal

You will:

  • load a CSV file with pandas
  • inspect rows, columns, and data types
  • clean column names
  • handle missing values
  • calculate basic statistics
  • group data by category
  • create a new calculated column
  • save a cleaned dataset

2. Dataset

Use this file:

session7/datasets/Pokemon.csv

This CSV uses an older text encoding. Load it like this:

import pandas as pd

pokemon = pd.read_csv("datasets/Pokemon.csv", encoding="cp1252")

Run your script from the session7 folder:

python solutions/exercise-07-homework.py

3. File to submit

Create this file:

session7/solutions/exercise-07-homework.py

4. Tasks

Complete all 10 tasks in your Python file.

  1. Load Pokemon.csv into a DataFrame called pokemon.
  2. Print the first 10 rows and the last 5 rows.
  3. Print the number of rows and columns.
  4. Print the column names and data types.
  5. Rename the columns so they are easier to use in Python:
    • # -> pokemon_id
    • Name -> name
    • Type 1 -> type_1
    • Type 2 -> type_2
    • Total -> total
    • HP -> hp
    • Attack -> attack
    • Defense -> defense
    • Sp. Atk -> sp_atk
    • Sp. Def -> sp_def
    • Speed -> speed
    • Stage -> stage
    • Legendary -> legendary
  6. Check missing values in every column. Fill missing type_2 values with "None".
  7. Print summary statistics for the numeric columns.
  8. Find and print:
    • the Pokemon with the highest attack
    • the Pokemon with the highest defense
    • the Pokemon with the highest speed
  9. Group by type_1 and print:
    • the number of Pokemon per type
    • the average total score per type, sorted from highest to lowest
  10. Create a new column called power_score using this formula:
power_score = attack + defense + speed

Then print the top 10 Pokemon by power_score and save the cleaned DataFrame to:

session7/solutions/pokemon_clean.csv

5. Reflection

At the bottom of your Python file, add comments answering these questions:

  1. Which column needed the most obvious cleaning?
  2. Why is it useful to rename columns before analysis?
  3. Which type_1 has the highest average total score?
  4. What is one limitation of ranking Pokemon only by power_score?

6. Rules

  • Use pandas.
  • Do not edit the original dataset in datasets/.
  • Keep your solution in solutions/.
  • Use clear variable names.
  • Print enough output to show that each task works.
  • Add short comments where you make a cleaning decision.

7. Reminder

Try the homework yourself before checking the reference solution.