The rank correlation ξ(X,Y), recently established by Sourav Chatterjee and already popular in the statistics literature, takes values in [0,1], where 0 characterizes independence of X and Y, and 1 characterizes perfect dependence of Y on X. Unlike concordance measures such as Spearman's ρ, which capture the degree of positive or negative dependence, ξquantifies the strength of functional dependence. In this paper, we study the attainable set of pairs (ξ(X,Y),ρ(X,Y)). The resulting ξ-\r{ho}-region is a convex set whose boundary is characterized by a novel family of absolutely continuous, asymmetric copulas having a diagonal band structure. Moreover, we prove that ξ(X,Y)\leq|ρ}(X,Y)| whenever Y is stochastically increasing or decreasing in X, and we identify the maximal difference ρ(X,Y)-ξ(X,Y) as exactly 0.4. Our proofs rely on a convex optimization problem under various equality and inequality constraints, as well as on ordering properties for ξand ρ. Our results contribute to a better understanding of Chatterjee's rank correlation, which typically yields substantially smaller values than Spearman's ρwhen quantifying positive dependencies. In particular, when interpreting the values of Chatterjee's rank correlation on the scale of ρ, the quantity \sqrtξ appears to be more appropriate.