When working with unfamiliar APIs, programmers frequently resort to learning resources and code examples to understand API usage sequences. Programmers often encounter obstacles finding the appropriate information due to either poor quality of API documentation or ineffective query-based searching strategy. To help solve this issue, researchers have proposed various methods to suggest the sequence of APIs given natural language queries representing the information needs from programmers. Among such efforts, Gu et al. adopts a deep learning method, in particular, an RNN Encoder-Decoder architecture, to perform this task and obtained promising results on common APIs in Java. In this work, we aim to reproduce their results and apply the same methods for APIs in Python. Additionally, we compare the performance with a more recent Transformer-based method, i.e. CodeBERT for the same task. Our experiment reveals a clear drop in performance measure when careful data cleaning is performed. Owing to the pretraining from a large number of source code files and effective encoding technique, CodeBERT outperforms the method by Gu et al, to a large extent.