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Differentiation of NumPy and SciPy

NumPy and SciPy both are very important libraries in Python. They have a wide range of functions and contrasting operations.

NumPy is short for Numerical Python while SciPy is an abbreviation of Scientific Python. Both are modules of Python and are used to perform various operations with the data.

The prerequisite of working with both the libraries is to understand the python basics.


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 and 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.

Differences Between NumPy and Scipy

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.

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.

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.


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.

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