Mastering NumPy: Initializing Ways, Ndarray Operations & Functions | upGrad blog

Mastering NumPy: Initializing Ways, Ndarray Operations & Functions | upGrad blog

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Numpy is a Python package that allows mathematical and numerical operations to be performed with high-efficiency and abstract functionality on high-dimensional data. While building a Machine Learning solution for a particular business use case, it becomes very important to transform the data in such a way that the preprocessing becomes easy, and the results are interpretable. Numpy is the core library that makes it possible to perform all the related functions in the best possible way. 

** Why NumPy?**

Other Data Science libraries such as Pandas, Matplotlib, and Scikit-learn are built on top of Numpy because of its high-performance nature. This library offers ndarray which is used in place of inbuilt python lists. Python list is great to store values, but it comes at the cost of increased execution time as it stores pointers of the memory locations which adds overhead in terms of memory and execution.

Numpy finds its direct usage in the image processing field where the images are processed as high multidimensional matrices to perform various transformations such as blurring, color change, and object detection. 

Let’s look at some important Numpy functions that every Data Science aspirant should know but before that:

  1. All functions code below assume that the library has been imported with alias np, i.e, import numpy as np.
  2. The term “array” will be used to define ndarray after this point.

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