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Python words in plain language

Assignment and type
frames = 96 binds a name to an integer. "96" is text, not the same numeric value. Values and names.
List and index
A list keeps items in order. Positions start at zero; the final nonnegative index is length minus one. List lookups.
Loop and indentation
A for loop processes each item. Indentation groups its repeated statements; a statement outside the block runs separately. Loops.
Parameter, argument and Return
A parameter names an input in a definition; an argument supplies its value in a call. Return gives a result to the caller; print displays it. A function that only prints returns None. Functions.
Dictionary and schema
A dictionary labels values with keys. A schema states the required fields and types; a missing fact is not automatically zero. Dictionaries · Validation.
File, CSV and JSONL
A file stores bytes/text. CSV needs a quoting-aware parser; JSONL contains one JSON value per line. Browser practice files are temporary, not computer files. CSV · JSONL.
Denominator and evidence
A rate divides a count by its declared eligible total. Keep exclusions visible. A quiz score, practical check and self-review are different evidence. Quality rates.

Read the error, then make one repair

SyntaxError / IndentationError
Python could not parse the code. Inspect the named line and the preceding line for a missing colon, unmatched quote/bracket or inconsistent indentation. Review blocks.
NameError
A name has no value here. Compare case and spelling, and check that assignment happens before use. Do not replace an unknown parameter with a fixed sample.
TypeError / ValueError
A wrong kind of value or an unacceptable value can fail an operation. Inspect the supplied input and declared policy; do not broadly suppress the error. Narrow error handling.
IndexError / KeyError
The requested position or key is missing. Inspect the collection and interface instead of guessing a fallback. Indices · Keys.
Run stays loading / runtime or checker unavailable
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Expected sample prints, but a check fails
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← Route Week 7 · Day 32 of 40

Reproducible sampling

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Lesson idea Tap to fold
Recall

Why might a lead inspect a sample instead of every row?

Show the answer

To audit quality efficiently when a full review is too costly or slow.

Today

Reproduce a sample, not a quality claim

random.sample(population, k) selects occurrences without replacement and leaves the original list unchanged. With unique input IDs, the result has no repeated IDs. k cannot exceed the population size. random.Random(7) creates a private seeded generator; random.seed(7) instead resets the module generator. Repeatability here requires the same inputs, input order, call sequence and runtime version. A seed is neither a security tool nor proof of a fair sample.

The table task uses df.groupby("model", group_keys=False).sample(n=1, random_state=7). It takes one row from each present model group, then sort_values("model") orders the report. Each group needs at least n rows when sampling without replacement. This is stratified coverage: equal numbers per group do not preserve their proportions in the full dataset.

The list tasks use standard Python; the table task loads pandas in the browser. The pinned runtime is Python 3.12 with pandas 2.2.3 and NumPy 2.0.2. Keep a record of the sampling rule and population, and inspect disagreements rather than claiming universal quality from a tiny sample.

# Repeat the same sampling rule on the same IDs.
import random

ids = ["Museum", "Canal", "Plaza", "Rooftop"]
first = random.Random(7).sample(ids, 2)
second = random.Random(7).sample(ids, 2)
print(first == second)
print(len(first), len(set(first)))
print(len(ids))

The independently seeded generators agree, two unique IDs are selected, and the four-item population remains intact.

Choose the sampling rule before looking at outcomes. One row per group helps coverage, but cannot estimate overall prevalence without accounting for group sizes.

Official reference for this lesson · Python sample and seed reference

Predict, explain, then test

Answer these three self-reviewed checks before opening the comparisons. They are not scored. Coding checks use unfamiliar inputs under each task’s stated assumptions.

Explain a seed

What must stay the same to reproduce this seeded sample?

Compare your answer · self-reviewed

The ordered population, sampling rule, call sequence and runtime version. A seed alone does not promise a representative or secure sample.

Check feasibility

Can sample draw three without replacement from two occurrences?

Compare your answer · self-reviewed

No; random.sample raises ValueError when k exceeds the population size.

Compare designs

Does one row per model preserve the full dataset’s model proportions?

Compare your answer · self-reviewed

Not usually. It provides group coverage, not an automatically population-weighted estimate.

Now

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day-32.py Python · packages shown per task · runs here
Output

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