Nature-inspired algorithms are a very popular tool for solving optimization problems (Yang 2014), (Hassanien and Emary 2016). Numerous variants of nature-inspired algorithms have been developed (Iztok Fister Jr. and Fister 2013) since the beginning of their era. To prove their versatility, those were tested in various domains on various applications, especially when they are hybridized, modified or adapted. However, implementation of nature-inspired algorithms is sometimes a difficult, complex and tedious task. In order to break this wall, NiaPy is intended for simple and quick use, without spending time for implementing algorithms from scratch.