Word2Vec is a class of neural network models that as being trained from a large corpus of texts, they can produce for each unique word a corresponding vector in a continuous space in which linguistic contexts of words can be observed. In this work, we study the characteristics of Word2Vec vectors, called API 2 VEC or API embeddings, for the API elements within the API sequences in source code. Our empirical study shows that the close proximity of the API 2 VEC vectors for API elements reflects the similar usage contexts containing the surrounding APIs of those API elements. Moreover, API 2 VEC can capture several similar semantic relations between API elements in API usages via vector offsets. We demonstrate the usefulness of API 2 VEC vectors for API elements in three applications. First, we build a tool that mines the pairs of API elements that share the same usage relations among them. The other applications are in the code migration domain. We develop API 2 API , a tool to automatically learn the API mappings between Java and C# using a characteristic of the API 2 VEC vectors for API elements in the two languages: semantic relations among API elements in their usages are observed in the two vector spaces for the two languages as similar geometric arrangements among their API 2 VEC vectors. Our empirical evaluation shows that API 2 API relatively improves 22.6% and 40.1% top-1 and top-5 accuracy over a state-of-the-art mining approach for API mappings. Finally, as another application in code migration, we are able to migrate equivalent API usages from Java to C# with up to 90.6% recall and 87.2% precision.