# What is the difference between NumPy and SciPy?

- In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, et cetera.
- All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors.
- Thus NumPy contains some linear algebra functions, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms.
- If you are doing scientific computing with python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy.

In an ideal world, NumPy would contain nothing but the array data type and the most basic operations: indexing, sorting, reshaping, basic elementwise functions, et cetera. All numerical code would reside in SciPy. However, one of NumPy’s important goals is compatibility, so NumPy tries to retain all features supported by either of its predecessors. Thus NumPy contains some linear algebra functions and Fourier transforms, even though these more properly belong in SciPy. In any case, SciPy contains more fully-featured versions of the linear algebra modules, as well as many other numerical algorithms. If you are doing scientific computing with Python, you should probably install both NumPy and SciPy. Most new features belong in SciPy rather than NumPy.

Functions – Ideally speaking, NumPy is basically for basic operations such as sorting, indexing, and elementary functioning on the array data type. On the other hand, SciPy contains all the algebraic functions some of which are there in NumPy to some extent and not in full-fledged form. Apart from that, there are various numerical algorithms available that are not properly there in NumPy. However, you cannot rule out any one of them in scientific computing using Python as they are complement one another. But if you are looking for the new features, you are likely to find in in SciPy.

Read more at: https://www.freelancinggig.com/blog/2018/12/09/what-is-the-difference-between-numpy-and-scipy/

Related Concepts – The application of NumPy on data array has given rise to what is referred to as NumPy Array. It is a multi-dimensional array of objects, and the objects are of the same type. Therefore, it is different from the general data array. In reality, the NumPy array is represented as an object that further points to a block of memory. It has the responsibility of tracking the type of data stored, the number of dimensions, spacing between elements and likewise. It has opened up a greater number of possibilities like the use of memory-mapped disk file for storage in the array, the use of record array having a custom data type and much more. But SciPy does not have any such related array or list concepts as it is more functional and has no constraints like only homogeneous data or heterogeneous data applicable.

Read more at: https://www.freelancinggig.com/blog/2018/12/09/what-is-the-difference-between-numpy-and-scipy/

Miscellaneous – NumPy is written in C and it is faster than SciPy is all aspects of execution. It is suitable for computation of data and statistics, and basic mathematical calculation. SciPy is suitable for complex computing of numerical data. There are many who consider NumPy as a part of SciPy as most of the functions of NumPy are present in SciPy directly or indirectly. SciPy’s current application in machine learning has made it more popular than NumPy.

Read more at: https://www.freelancinggig.com/blog/2018/12/09/what-is-the-difference-between-numpy-and-scipy/