What is common between Pandas, Scipy, Sklearn, Matplotlib, Keras? Apart from the fact that they are famous Python libraries? Well, all of these along with 1.56K other packages , have Numpy as a dependency. This is a huge feat! It will not be wrong to say that Numpy is the biggest reason for the success of Machine Learning in Python. But how did Numpy achieve this position? How is Numpy is able to handle both the scale and dimension of data with ease? While there are many factors that have gone into the design of this library, this talk will focus on 3 design decisions, that makes Numpy the magical, powerful library we know of.