Scientific Python or SciPy is a framework built on top of NumPy and ndarray and was essentially developed for advanced scientific operations such as optimization, integration, algebraic operations, and Fourier transforms.
The concept was to efficiently use ndarrays to provide some of these common scientific algorithms in a memory-efficient manner. Because of NumPy and SciPy, we are in a state where we can focus on writing libraries such as scikit-learn and NLTK, which focus on domain-specific problems, while NumPy / SciPy do the heavy lifting for us. We will give you a brief overview of the data structures and common operations provided in SciPy. We get the details of some of the black-box libraries, such as scikit-learn and understand what goes on behind the scenes.
>>>import scipy as sp
This is how you import SciPy. I am using sp as an alias but you can import everything.
Let's start with something we are more familiar with. Let's see how integration can be achieved here,...