Building Something From All of It

The project: a command-line word-frequency counter. Give it a sentence, and it reports how many times each word appears — the smallest program that needs a loop, a dictionary, and a function working together rather than in isolation.

It is a deliberately small choice. A word counter is not an impressive-sounding project, and that is the point — every piece of it maps onto something a previous chapter already taught, so nothing about finishing it depends on learning something new mid-build. What it asks of you is putting those pieces together correctly, which turns out to be its own separate skill from knowing each one in isolation.

1. 1

   Split the text into words

   A string method turns one long sentence into a list of individual words.

2. 2

   Count each word with a dictionary

   Loop over the words, using each one as a key and a running count as its value.

3. 3

   Wrap it in a function

   word_counts(text) takes a sentence in and returns the finished dictionary out.

4. 4

   Test it before trusting it

   One normal sentence, and one edge case — an empty string — each with an assertion.

$ >>> text = "the cat sat on the mat the cat ran"$ >>> words = text.split()$ >>> words$ ['the', 'cat', 'sat', 'on', 'the', 'mat', 'the', 'cat', 'ran']

split() with no argument breaks on any run of whitespace — the comprehensions and loops chapters both used exactly this kind of list as their starting point.

def word_counts(text):    counts = {}    for word in text.split():        counts[word] = counts.get(word, 0) + 1    return counts

counts.get(word, 0) returns the running total if word has been seen before, or 0 if this is its first appearance — the same dictionary-with-a-default pattern from the dictionaries chapter, now doing real work.

Both of those checks are worth writing as real assert statements before moving on, not just typed into a shell and read by eye — the testing chapter's whole argument was that a check you write down catches a regression a check you only glance at will not.

def test_counts_repeated_words():    result = word_counts("the cat sat on the mat the cat ran")    assert result == {"the": 3, "cat": 2, "sat": 1, "on": 1, "mat": 1, "ran": 1} def test_empty_string_returns_empty_dict():    assert word_counts("") == {}

The minimum version above is finished, honestly — but it has a known gap. Feed it two sentences that repeat a word with different punctuation attached, and it counts them as different words entirely, because "cat." and "cat" are different strings as far as a dictionary key is concerned.

$ >>> word_counts("The cat, calm, sat. The cat ran fast.")$ {'The': 2, 'cat,': 1, 'calm,': 1, 'sat.': 1, 'cat': 1, 'ran': 1, 'fast.': 1}

Seven keys for what a person reading the sentence would call six distinct words — 'cat,' and 'cat' land as two separate entries purely because one of them happened to sit next to a comma. Fixing it needs two small additions, both already covered: str.lower() from the strings chapter to fold case, and str.strip(string.punctuation) to remove punctuation from the edges of each word before it becomes a key.

import string def word_counts(text):    counts = {}    for word in text.lower().split():        word = word.strip(string.punctuation)        counts[word] = counts.get(word, 0) + 1    return counts

With that change, word_counts("The cat, calm, sat. The cat ran fast.") returns {'the': 2, 'cat': 2, 'calm': 1, 'sat': 1, 'ran': 1, 'fast': 1} — six keys instead of seven, and 'cat' correctly counted twice. It is a genuine improvement, and also a reminder that "finished" for a real program is a judgement call, not a fixed line: the version without this fix was complete enough to call done a section ago, and this version is more correct without either one being the objectively right answer for every use.

One more extension worth trying alone, using only what this track already covers: the most common word. max(counts, key=counts.get) finds the key whose value is largest without writing a loop yourself — the same key= argument idea the sorting built-ins in Python use throughout the standard library.

Nothing about this program is specific to counting words — the same shape, a loop filling a dictionary, is the core of a shopping cart total, a vote tally, or a log file summary. The twenty-four chapters behind this one are not separate tools; they are the vocabulary this one program was written in.

They are also not everything. This track never touched a database, never built anything a browser could talk to, and never covered the tooling that turns a script into something you could hand to a stranger and have it just work on their machine. That is not an oversight to feel behind on — it is simply where this particular map ends, and where the next ones start.

Pick whichever one of those solves a problem you actually have, rather than the one that sounds most impressive — the word counter above did not become useful because it was ambitious. It became useful because every idea in it was one you had already proven you understood on its own, put to work together.