This shot will discuss the difference between NumPy and SciPy in Python.
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.
NumPy: It provides extended functionalities to Python and provides a user-friendly ambiance. It allows efficient operations on homogeneous data stored in specially designed arrays called NumPy arrays. It also helps manipulate numerical data.
SciPy: It contains a variety of sub-packages and has a collection of scientific functions, including clustering, image processing, integration, differentiation, gradient optimization, etc. The reason it is preferred over other tools is its speed. All the numerical computing in Python is done via SciPy.
The differences between these libraries are as follows:
Point of Difference | NumPy | SciPy |
Type of operations | Performs basic operations such as sorting, indexing, etc. It is mostly used when working with data science and statistical concepts. | Used for complex operations such as algebraic functions, various numerical algorithms, etc. |
Functions | Contains a variety of functions but these are not defined in depth. | Contains detailed versions of the functions like linear algebra that are completely featured. |
Arrays | NumPy Arrays are multi-dimensional arrays of objects which are of the same type i.e. homogeneous. | SciPy does not have any such array concepts as it is more functional. It has no constraints of homogeneity. |
Base Language of creation and speed | NumPy is written in C and so has a faster computational speed. | SciPy is written in Python and so has a slower execution speed but vast functionality. |
NumPy and SciPy are both important Python libraries in terms of convenience and their wide range of functions, modules, and packages. They deal with mathematical computations and are useful in data science, machine learning, deep learning, etc.
Although conceptually different, they have similar functionalities. Their combined functions are necessary and helpful to work on various numerical/mathematical technologies, making our lives a lot more simple.