Lists and loops

Square brackets enclose a list and commas separate its values. Here one name, labels, refers to four integers in order. len(labels) returns the count of elements, four. It does not return the highest value or the final index. A list can be empty ([]) and can contain repeated values. The two eights are two observations; Python does not remove them. This is the same reason repeated examples remain separate rows in a dataset.

Run and compare the displayed collection with the four source values.

An index selects one element. Index zero selects the first, one the second, and negative one the last. With four elements, valid nonnegative indexes are 0, 1, 2, and 3. labels[4] is therefore outside the list. The count and final index differ by one because counting starts at zero. Keeping that distinction explicit prevents selecting the wrong true label when comparing predictions.

Predict each line before running. Point to the matching value in the list.

A slice selects a range. The start is included and the stop is excluded, so [1:3] takes positions 1 and 2. Omitting the start means begin at zero. Indexing [0] produces one integer; slicing [:1] produces a list containing one integer. The printed square brackets reveal the difference. Later, a model needs a batch containing one image rather than a bare image, and this same distinction appears in array shapes.

Run and identify which outputs are lists and which output is a single value.

The dot in predictions.append(3) requests the append method belonging to this list. A method is a function associated with an object. Append changes the existing list by adding one value at its end. It returns None, so writing predictions = predictions.append(3) would replace your useful list with None. That is a common beginner mistake: changing an object and returning a new object are different operations.

Run, then add a third append line before the display.

The for statement takes each value in labels and gives the current value the local working name label. The colon introduces the body; four spaces put print inside it. Finished is aligned with for, so it runs after the loop, once. In the block editor the surrounding repeat or for-each block showed the body. Here indentation draws that boundary. The singular name label is a convention that helps readers distinguish one item from the collection labels.

Count the lines you expect before running. Then inspect the indentation.

The counter is initialised before the loop so earlier matches survive each iteration. The if statement is one indentation level inside the loop, and the counter change is two levels inside because it belongs to both structures. == compares rather than assigns. When the current label is not eight, Python skips the increment and continues with the next value. Moving count = 0 inside the loop would repeatedly erase the total.

Trace the counter after each label: 0, 1, 1, then 2. Run to check.

range(len(truth)) supplies positions 0 through 3. The same index selects the prediction and true label for one example. += 1 is shorthand for adding one and assigning the result back. Matching lengths are necessary but insufficient: both lists must still refer to examples in the same order. Sorting predictions would break that connection even if it kept four entries. Later we retain IDs to protect the same relationship when exporting files.

Read the position-by-position display and identify the one mismatch before checking the total.

A list can contain other lists. The outer loop chooses one row, and the inner loop visits the pixels in that row. The named argument end=" " asks print to finish with a space instead of its usual newline. The empty print after the inner loop starts the next row. It is indented inside the outer loop, but outside the inner loop, so each row gets exactly one newline. This small grid is an image made of numerical brightness values.

Run, then draw boxes around the two loop bodies in your notes or trace their indentation aloud.

This combines a list, a loop, a decision, a running total, and a calculation. The two checks reject mismatched lengths and an empty evaluation. raise ValueError(...) deliberately stops with a helpful message when an assumption is broken; it is better than computing a misleading number. The program still cannot prove the lists belong in the same order. That meaning must come from how you prepared the data. The final ratio uses the number evaluated as its denominator.

Build and run the complete version, then test the length check by adding an unmatched prediction. Repair the data afterward.