NumPy vs SciPy – Difference Between NumPy and SciPy

NumPy and SciPy are the two most important libraries in Python. The operations are relative and hence contrasting. Both libraries have a wide range of functions. The prerequisite of working with both the libraries is to understand the python basics.

NumPy stands for Numerical Python while SciPy stands for Scientific Python. Both of their functions are written in Python language.

We use NumPy for homogenous array operations. We use NumPy for the manipulation of elements of numerical array data. NumPy hence provides extended functionality to work with Python and works as a user-friendly substitute.

SciPy is the most important scientific python library. It consists of a variety of sub-packages and hence has a collection of functions.

The sun-packages support functions including clustering, image processing, integration, etc. It is a very consistent package and hence useful for numerical computations in Python.

Functional Differences between NumPy vs SciPy

1. SciPy builds on NumPy. All the numerical code resides in SciPy. The SciPy module consists of all the NumPy functions. It is however better to use the fast processing NumPy.

2. NumPy has a faster processing speed than other python libraries. NumPy is generally for performing basic operations like sorting, indexing, and array manipulation. The most important feature of NumPy is its compatibility.

The NumPy library contains a variety of functions that aren’t defined in depth. We use a combination of SciPy and NumPy for fast and efficient scientific and mathematical computations.

3. SciPy on the other hand has slower computational speed. It consists of rather detailed versions of the functions. It consists of all the full-fledged versions of the functions. The SciPy module consists of the functions like linear algebra that are completely featured.

Unlike in NumPy which only consists of a few features of these modules. SciPy is an open-source library. Hence, all the newer features are available in SciPy.

However, it is the best option to use both libraries together. Both when used hand-in-hand complement each other.

Array Concepts in NumPy and SciPy

The arrays in NumPy are different from Python arrays. It consists of a multidimensional array object. The elements of the array are homogenous. The array object points to a specific memory location. The NumPy array object keeps track of the array data type, its shape, and the dimensions.

SciPy on the other hand has no such type restrictions on its array elements. It does not follow any array concepts like in the case of NumPy. The arrays in SciPy are independent to be heterogeneous or homogeneous. There are no shape, size, memory, or dimension restrictions.

Specific Usage of NumPy and SciPy

NumPy is written in C language and hence has a faster computational speed. It is most suitable when working with data science and statistical concepts. Although all the NumPy features are in SciPy yet we prefer NumPy when working on basic array concepts.

SciPy is written in python. It has a slower execution speed but has vast functionality. We use SciPy when performing complex numerical operations. SciPy has a vast scope in machine learning and data science.

Summary

NumPy and SciPy are two very important libraries to deal with the upcoming technological concepts. They are useful in the fields of data science, machine learning, etc. Both are convenient options due to their functions, modules, and packages.

They are different conceptually but have similar functionality The combined functions of both are necessary to work on different concepts.