Difference between NumPy and SciPy
Types of Differences |
NumPy |
SciPy |
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Primary Focus |
NumPy primarily focuses on providing efficient array manipulation and fundamental numerical operations. |
On the other hand, SciPy contains all the functions that are present in NumPy to some extent. |
Use Cases |
NumPy is often used when you need to work with arrays, and matrices, or perform basic numerical operations. It is commonly used in tasks like data manipulation, linear algebra, and basic mathematical computations. |
SciPy becomes essential for tasks like solving complex differential equations, optimizing functions, conducting statistical analysis, and working with specialized mathematical functions. |
Module Structure |
NumPy provides a single, comprehensive library for array manipulation and basic numerical operations. It doesn’t have a modular structure like SciPy. |
SciPy is organized into submodules, each catering to a specific scientific discipline. This modular structure makes it easier to find and use functions relevant to your specific scientific domain. |
Capabilities |
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Domain |
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Evolution |
NumPy is originated from the older Numeric and Numarray libraries. It was designed to provide an efficient array computing utility for Python. |
Scipy is started with Travis Oliphant wanting to combine the functionalities of Numeric and another library called “scipy.base”. The result was the more comprehensive and integrated library we know today. |
Difference between NumPy and SciPy in Python
There are two important packages in Python: NumPy and SciPy. In this article, we will delve into the key differences between NumPy and SciPy, their features, and their integration into the ecosystem. and also get to know which one is better.