Exercise 3: Why sns.pairplot() Is "Everything ds1-7 Taught Individually" — Possible Solution
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WHAT sns.pairplot() ACTUALLY GENERATES, PER THIS CHAPTER
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Per this chapter, "sns.pairplot() generates a full grid: a scatter
plot for every pair of numeric columns (ds1-7's own scatter plot, run
automatically for every combination) plus a histogram for each column
against itself along the diagonal (ds1-7's own histogram, likewise
automated)."

WHICH ds1-7 CHART TYPE APPEARS ON THE DIAGONAL
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The diagonal cells of the grid — where a column is being compared
against itself — show a histogram. This makes sense structurally: a
scatter plot of a column against itself would just be a straight
diagonal line (every point's x-value equals its own y-value), which
conveys no useful information, so pairplot substitutes ds1-7's own
histogram there instead, showing that single column's own distribution
shape — exactly ds1-7's own "shape of a distribution" chart type.

WHICH ds1-7 CHART TYPE APPEARS OFF THE DIAGONAL
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Every off-diagonal cell — comparing two genuinely different columns
against each other — shows a scatter plot, exactly ds1-7's own "Are
these two variables related?" chart type, generated automatically for
every distinct pair of numeric columns in the dataset rather than one
deliberately chosen pair at a time.

WHY THIS JUSTIFIES "EVERYTHING ds1-7 TAUGHT INDIVIDUALLY"
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ds1-7 taught the scatter plot and the histogram as two separate, hand-
built chart types, each constructed one deliberate ax.scatter() or
ax.hist() call at a time for one specific pair or column chosen in
advance. pairplot doesn't introduce any new kind of chart beyond those
two — it simply automates generating both of them, systematically,
across every combination the dataset actually contains, in a single
function call. Nothing new is being taught; ds1-7's own two techniques
are simply being applied exhaustively rather than selectively.

WHY THIS MATTERS PRACTICALLY, PER THIS CHAPTER
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Per this chapter's own tip-box, this is precisely why a pair plot (along
with the correlation heatmap) is described as "the first two things run
against a genuinely new dataset during real exploratory analysis — a
fast, wide first look before deciding which specific relationships
deserve a closer, more deliberate chart." Automating ds1-7's own two
chart types across every column pair at once gives a fast overview,
after which a specific interesting pair (spotted in the pairplot) can
be revisited with a single, deliberately hand-built ds1-7-style chart
for closer inspection.

WHY THIS WORKS AS AN ANSWER
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It identifies precisely which ds1-7 chart type appears in each part of
the pairplot grid (histogram on the diagonal, scatter plot off it), and
explains why automating those same two, already-taught chart types
across every column combination — rather than introducing a new
technique — is exactly what justifies the chapter's own "everything
ds1-7 taught individually" description.