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What is the difference between SciPy and NumPy?
NumPy, stands for Numerical Python, is used for the manipulation of elements of numerical array data. SciPy, stands for Scientific Python, is used for numerical computations in Python. Both these packages provide extended functionality to work with Python. Let’s understand some basic differences between NumPy and SciPy −
Functional differences − NumPy has a faster processing speed than SciPy. The functions defined in NumPy library are not in depth whereas SciPy library consists of detailed versions of the functions. SciPy is built on NumPy and it is recommended to use both libraries altogether for fast and efficient scientific and mathematical computations.
Array concept − NumPy consists of multidimensional array objects which are different from Python arrays. The advantage of using NumPy array is that the array object points to a specific memory location and keeps track of the array data type, its shape, and the dimension as well. On the other hand, SciPy array elements are independent to be homogeneous or heterogeneous and there are no restrictions for shape, size, memory, and dimension.
Usage − NumPy, written in C language, is most suitable for working with statistical concepts. We know SciPy has all the features of NumPy, but it is recommended to work alone with NumPy when working on basic array concepts. The reason behind is the computational speed of NumPy. On the other hand, SciPy, written in Python, is most suitable working with machine learning. Due to its vast functionality, it is recommended to use SciPy when you want to perform complex numerical operations.
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