Current work on parallel programming models are trending towards the dataflow paradigm. Previous works on that topic have shown that dataflow programming is indeed a natural way to exploit parallelism in programs. However, there is still a gap in terms of ease of programming between high level languages adopted by the scientific community and the languages and tools available for dataflow programming. In this paper we present Sucuri: a minimalistic Python library that provides dataflow programming with reasonably simple syntax. To parallelize applications using our library, the programmer needs only to identify functions of his code that are good candidates for parallelization and instantiate a dataflow graph where each node is associated with one of such functions, and the edges between nodes describe data dependencies between functions. We then proceed to implement two benchmarks that represent important parallel programming patterns using our library and execute them on a cluster of multicores. Experimental results are promising, proving that our library can be an interesting first option for parallelization.