Python Practice

Notebook Structure

Each generated notebook must follow this structure:

1. Title cell (markdown): includes category, difficulty, date, and number of questions.
2. Setup cell (code): import statements and sample datasets. For pandas notebooks, embed real market data fetched with fetch_price_data.py.
3. For each question:
   • A markdown cell with the challenge prompt, numbered (e.g. "### Challenge 1"). Include:
     • A clear problem statement
     • Any sample data or expected output
     • When appropriate include a hidden hint wrapped in a <details><summary>💡 Hint</summary>...</details> tag (initially collapsed).
   • An empty code cell for the user to write their solution.
   • A hidden solution cell wrapped in a markdown <details><summary>✅ Solution</summary>...</details> tag so the user can reveal it.

Scripts

All scripts live in scripts/ and are run from that directory.

generate_notebook.py

Builds a .ipynb file from a category template + question data. Uses nbformat.

# From question bank:
python scripts/generate_notebook.py --bank questions/bank.json \
  --category pandas --subcategory series --difficulty easy --count 5 --output notebooks/practice

# From a JSON file of custom questions:
python scripts/generate_notebook.py --questions /tmp/my_questions.json \
  --category algorithms --difficulty hard --output notebooks/practice

# From stdin:
cat questions.json | python scripts/generate_notebook.py --category pandas --output notebooks/practice

random_tickers.py

Pick random ticker symbols to use in questions. Avoids repeating the same tickers.

python scripts/random_tickers.py --count 4                  # 4 random tickers
python scripts/random_tickers.py --count 3 --sector tech    # from tech sector
python scripts/random_tickers.py --count 5 --diverse        # one per sector
python scripts/random_tickers.py --list-sectors              # show all sectors

Use this every time you generate a notebook to ensure fresh ticker sets.

fetch_price_data.py

Fetch real stock price data from Yahoo Finance via yfinance. Embed the output in notebook setup cells so students work with real market data.

# Get a code snippet to embed in the notebook
python scripts/fetch_price_data.py --tickers AAPL MSFT TSLA --period 6mo --format code

# Get daily returns as a code snippet
python scripts/fetch_price_data.py --tickers AAPL MSFT --period 3mo --format returns

# Random tickers + real data
python scripts/fetch_price_data.py --random 4 --period 1y --format code

# Save as CSV
python scripts/fetch_price_data.py --tickers SPY QQQ --start 2023-01-01 --end 2024-01-01 --output prices.csv

random_data.py

Generate random numbers, date ranges, names, and messy DataFrames for question variety.

python scripts/random_data.py --type integers --count 10 --min 1 --max 100
python scripts/random_data.py --type floats --count 5 --min -0.05 --max 0.05
python scripts/random_data.py --type date_range --start 2020-01-01 --periods 30 --freq B
python scripts/random_data.py --type names --count 8
python scripts/random_data.py --type messy_dataframe --rows 20

Use --type messy_dataframe to generate data-cleaning exercises with realistic problems (missing values, inconsistent casing, whitespace, bad types, duplicates).

Templates

Category-specific notebook templates live in templates/. Each template provides:

• A title cell with {{difficulty}}, {{date}}, {{count}}, {{subcategories}} placeholders
• A setup cell with appropriate imports and sample datasets

Question Bank

Pre-written questions are stored in questions/bank.json. The bank is organized as:

{ category: { subcategory: { difficulty: [question, ...] } } }

Each question has: prompt, solution, and optionally hint and setup.

Categories and Subcategories

Pandas

Refer to references/PANDAS_NOTE.md for the topic tree and references/PANDAS_FOR_DATA_SCIENCE.md for functions and techniques. Use examples/PANDAS_EXAMPLES.md for sample data patterns.

Subcategories:

• Series: creation, retrieval, modification, alignment, built-in methods
• DataFrames: retrieval (loc/iloc, slicing, boolean masks), modification, column operations
• Data exploration: apply/applymap, correlations, covariance, DataFrame-Series arithmetic
• Missing data: detection, dropping, filling, interpolation
• Reindexing and sorting: reindex, drop, sort_index, sort_values
• Categorical data: unique values, value_counts, isin, dummy variables
• File I/O: reading/writing CSV, Excel, pickle
• Multi-indexing: multi-index DataFrames and Series, stack/unstack, swaplevel
• Time series: timestamps, offsets, date ranges, resampling, shifting
• GroupBy, pivot, merge: groupby, pivot_table, merge/join, concat
• Rolling and quantiles: rolling windows, qcut, shift for returns
• Plotting: line, bar, scatter, histograms, heatmaps with seaborn

Algorithms

Focused on vanilla Python (no external libraries).

Subcategories:

• Data structures: lists, dicts, sets, tuples, deques, heaps
• Sorting: bubble, merge, quick, insertion sort implementations
• Searching: linear search, binary search
• Recursion: factorial, fibonacci, tree traversal, divide and conquer
• String manipulation: reversal, palindromes, anagrams, pattern matching
• Math and logic: primes, GCD, modular arithmetic, bit manipulation

Difficulty Levels

• Easy: Single-concept problems. Provide hints and sample data. Expect 1-5 lines of code per solution.
• Medium: Combine 2-3 concepts. Provide sample data but no hints. Expect 5-15 lines of code.
• Hard: Multi-step problems requiring chaining operations or designing algorithms. No hints. Expect 10-30+ lines of code.

Additional Resources

• For pandas topic details, see references/PANDAS_NOTE.md
• For pandas functions reference, see references/PANDAS_FOR_DATA_SCIENCE.md
• For numpy context, see references/NUMPY.md
• For code examples and sample data, see examples/PANDAS_EXAMPLES.md