session-01-part-03.md

Session 1 | Part 3

In this part, we move from setup to real dataset processing with pure Python.

1. Goal

In this tutorial, you will:

• download a dataset
• read CSV data using Python's built-in csv module
• practice basic data-processing tasks
• reason about time and space complexity

2. Prerequisites

Before starting:

1. Open the session1 folder in Visual Studio Code.

2. Activate your virtual environment.

3. Confirm dependencies are installed:

pip install -r requirements.txt

For this tutorial, the hf command requires huggingface_hub. Make sure your requirements.txt includes:

huggingface_hub

4. Create (or open) your exercise file:

session1/solutions/exercise-01-03.py

If solutions/ does not exist yet, create it first (see Part 1).

3. Basics you should know

• csv module: built-in Python module for reading/writing CSV files.
• csv.reader(file): returns an iterator over rows.
• Each row is a list of strings.
• Header row: first row with column names.
• break: stops search when the first matching row is found.
• Hugging Face: a platform for hosting datasets and models.
• hf CLI: command-line tool from huggingface_hub used to download datasets.
• hf download: downloads a specific file from a dataset or model repository.

4. Download the movies.csv dataset (public)

Dataset:

• Dataset: Birkbeck/movies

Download the dataset file using the following command in terminal (Mac or Windows):

hf download Birkbeck/movies movies.csv --repo-type dataset --local-dir .

Expected result: movies.csv appears in your current folder.

5. First CSV read example

Let's read the file row by row. This prints each CSV row as a Python list.

Run this script from the session1 folder (where movies.csv was downloaded). If you run from the bda root, use open("session1/movies.csv", "r").

File: session1/solutions/exercise-01-03.py

import csv

with open("movies.csv", "r", newline="", encoding="utf-8") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Run the script using:

python solutions/exercise-01-03.py

Example output:

['movie_id', 'title', 'year', ...]
['1', 'Movie 1', '2020', ...]
['2', 'Movie 2', '1994', '144', ...]

Tip

What are the time and space complexities of this operation?

Show answer

Time: O(n)

Space: O(1)

6. Inspect reader and specific columns

Check what reader is:

File: session1/solutions/exercise-01-03.py

import csv

with open("movies.csv", "r", newline="", encoding="utf-8") as file:
    reader = csv.reader(file)
    print(reader)

Example output:

<_csv.reader object at 0x...>

Print the genres column (index 4):

File: session1/solutions/exercise-01-03.py

import csv

with open("movies.csv", "r", newline="", encoding="utf-8") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row[4])

This script assumes all rows have at least 5 columns. To avoid errors, add a check such as if len(row) > 4: before accessing the column.

Tip

What are the time and space complexities of the above script?

Show answer

Time: O(n)

Space: O(1)

7. Exercise 1

Write all answers in your solutions/ folder:

session1/solutions/exercise-01-03.py

Tasks:

1. Print only the first row (header).
2. Print the first 5 rows only.
3. Find and print the first movie where genres contains Action, then stop.
4. State one benefit and one limitation of using raw csv.reader.
5. What are the time and space complexities of your script(s)?

8. Exercise 2

Use this file:

session1/solutions/exercise-01-03.py

Use the dataset here. You will need to download the data using the appropriate hf command.

There is a data issue (e.g. missing or malformed row). Identify:

• which row is problematic
• which column is affected
• what the issue is

This dataset may also contain a file named movies.csv. If you download it into the same folder, you can overwrite your original movies.csv.

Note

Use enumerate(reader) to track row numbers and detect malformed rows.

Show code

for i, row in enumerate(reader):
    if len(row) != expected_columns:
        print(f"Issue at row {i}: {row}")

Set expected_columns based on the header length.

9. Quizzes

Complete the following quiz.

quizmd quizzes/python-loops-and-indexing-quiz.md

Now complete the imposter quiz:

Note

The next quiz is an imposter quiz 🤥.

You need to identify:

• the correct answer and
• the answer that looks correct but is actually wrong. Read the quiz instructions before you start.

Example:

What is the index of 20 in [10, 20, 30]?

Correct answer: 1 Imposter answer: 2 (common mistake due to misunderstanding 0-based indexing)

quizmd quizzes/python-session-01-imposter-quiz.md