Homework: Text Statistics

作业: 文本统计

≈ 45 min · python-strings python-functions python-lists · Open in WolfCode

Six functions — five tiny helpers and one assembler. The shape of every text-processing pipeline.

The six functions

char_count(text) — non-whitespace chars
word_count(text) — whitespace-split words
longest_word(words) — first on ties
unique_word_count(words) — distinct (use len(set(words)))
starts_with_count(words, prefix) — filter comprehension
text_stats(text) — assembles dict, calls the helpers

The orchestrator

Python · runnable

def text_stats(text):
    words = text.split()
    return {
        "chars":         char_count(text),
        "words":         word_count(text),
        "longest":       longest_word(words),
        "unique_words":  unique_word_count(words),
    }

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One-line idioms used in the helpers

Python · runnable

# unique count
print(len(set(["a", "b", "a"])))                   # 2

# starts-with count
print(len([w for w in ["the", "two", "ten"] if w.startswith("t")]))    # 3

# longest_word (tracking-variable from Ch.3)
def longest_word(words):
    if not words: return ""
    best = words[0]
    for w in words[1:]:
        if len(w) > len(best):
            best = w
    return best

print(longest_word(["cat", "bird", "dog"]))        # "bird"
print(longest_word(["one", "two", "ten"]))         # "one"  (first on ties)

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Acceptance criteria

All 10 tests pass
text_stats calls at least 3 of the 4 helpers
All helpers handle empty input