Figures such as a), b) in this drawing are used to explain the idea of (in)dependence between two random variables (named X,Y).
In figure a), knowing the value of X tells us nothing about Y, so X,Y are independent. In figure b), if X is near the right side of its range of data, tracing the "marginal" line upward show restricted range of Y values that are possible, with fewer values as the X value approaches the "corner" of the data.
Ok, that makes sense!
But what about the case shown in Figure c)? The figure show a case where there is a high probability that X,Y occur at one of the four indicated points, with a much lower (but still nonzero) probability of data occuring anywhere in the X-Y square, like in case a). The latter uniform-in-X,Y probabilty is hard to show in the quick sketch, but please imagine it. I attempted to draw it with faint dots.
I believe this case c) also show independent RVs; the joint probability factorizes $$ P(X,Y) = P(X)P(Y) $$ But according to the visual logic of the previous explanation, this case must be dependent I think. Looking at the marginal pdf of X, there are two high probability values, and the rest low probability. Knowing that X is at one of the high-probability values means that P(Y|X) has two high-probability values, and any other value is low-probability. But if X is at one of the low-probability values, P(Y|X) is uniform. So knowing X tells us something about Y, $$ P(Y|X) != P(Y) $$
Figure d) shows a case with the same marginal probabilities as figure c), but which is not independent (I believe). This may not be needed for our discussion.