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Data partitioning that maximizes or minimizes Shannon entropy is a crucial subroutine in data compression, columnar storage, and cardinality estimation algorithms. These partition algorithms can be accelerated if we have a data structure to find the entropy in different subsets of data when the algorithm needs to decide what block to construct. While it is generally known how to compute the entropy of a discrete distribution efficiently, we want to efficiently derive the entropy among the data items that lie in a specific area. We solve this problem in a typical setting when we deal with real data, where data items are geometric points and each requested area is a query (hyper)rectangle. More specifically, we consider a set P of n weighted and colored points in ℝ^d. The goal is to construct a low space data structure, such that given a query (hyper)rectangle R, it computes the entropy based on the colors of the points in P∩ R, in sublinear time. We show a conditional lower bound for this problem proving that we cannot hope for data structures with near-linear space and near-constant query time. Then, we propose exact data structures for d = 1 and d > 1 with o(n^{2d}) space and o(n) query time. We also provide a tune parameter t that the user can choose to bound the asymptotic space and query time of the new data structures. Next, we propose near linear space data structures for returning either an additive or a multiplicative approximation of the entropy. Finally, we show how we can use the new data structures to efficiently partition time series and histograms with respect to entropy.