Handling Finance Data in Python | Python for Trading | Quantra Free Course

Learn to use Numpy on financial markets data for trading without any installations or downloads and for free: https://quantra.quantinsti.com/course/python-trading-basic

NumPy is an acronym for “Numeric Python” or “Numerical Python”. It is an open source extension module for python. NumPy provides us with two major things:

It provides fast precompiled functions for mathematical and numerical routines on any variable or object.
Further, it enriches the Python programming language with powerful data structures such as multi-dimensional arrays and matrices. Using NumPy, one can even manipulate these arrays & matrices to perform high-level mathematical operations on them.

So a natural question here is, what is an array and why are they important?

An array is a “list” of related values i.e. every value in an array is of the same data type and is only differentiated by the position within the array.

The reason why arrays are important are:

They help us understand the rules of a dataset which exhibit a dimension greater than 1. In python for finance, understanding array manipulation are a natural predecessor to manipulating financial data frames. Arrays helps us build our fundamentals on higher dimensional datasets, especially two dimension datasets.

They help us to conduct an operation or apply a function to a complex object like an array, "at once" rather than iterating over the single elements of the object. This increases the efficiency of the programming language, at the same time makes numerical computations on objects more capable and efficient. This is known as ‘vectorization’.


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