Week 10 — Intermediate Capstone: Text Analysis Tool

What you'll learn

• 1Integrate file I/O, regex, OOP, and collections in one project
• 2Design a class hierarchy for the analysis pipeline
• 3Handle command-line arguments with argparse
• 4Produce formatted tabular output
• 5Write unit tests for the tokenizer and statistics functions

1. Project Specification

python analyze.py [OPTIONS] FILE

Options:
  --top N       Show top N words (default 10)
  --format FMT  Output format: text|csv|json (default text)
  --no-stop     Exclude common stop words

• Total words, unique words, average word length
• Top-N word frequency (with ASCII bar chart)
• Sentence count, average sentence length
• Most common bigrams
• Readability score (Flesch Reading Ease approximation)

2. Architecture

• Tokenizer class — regex-based word/sentence splitter
• TextStats dataclass — holds all computed metrics
• Analyzer class — orchestrates tokenization and metric computation
• Reporter class — formats and prints TextStats (text/csv/json)
• main() — argparse + Analyzer + Reporter pipeline

3. Sample Output

$ python analyze.py hamlet.txt --top 5

File: hamlet.txt  (5447 words, 1234 unique)

Top 5 words:
  the     ████████████████  250
  and     ██████████████    215
  to      ████████████      180
  of      ██████████        150
  a       ████████          130

Sentences: 423  (avg 12.9 words)
Readability: 68.4 (Standard)

📝 Exercises

Try them yourself first, then open the solution to compare.