Software developers spend about 20% of their programming time searching for relevant code. They often spend a lot of time to manually choose queries for their code search. Unfortunately, due to vocabulary mismatch problems, the accurate answer may not be always retrieved, which leads to numerous trials and errors. Furthermore, many answers might not contain the relevant code examples that the developers look for. In this thesis, we extend RACK, an existing solution for code search, and attempt to solve the code search problem effectively. First, we replicate RACK in Python language from its original implementation in Java. Second, we construct a token-API database by analyzing thousands of Python posts from Stack Overflow. Third, we determine the relevance between a natural language query and API classes using three co-occurrence based heuristics -- KKC, KAC and KPAC. Then we return a list of relevant API classes against a natural language query. Finally, we integrate our RACK implementation into a VS-code plugin. The plug-in accepts a natural language query and retrieves relevant code examples from GitHub by leveraging its search API and the API classes from RACK. Software developers can use these code examples to solve their programming problems much faster.