Numpy: Elemental for Scientific Computing
Everyone is now familiar with the fact that evolution in the field of Technology is rising rapidly. One of the reasons in this kind of development is an amalgamation of advanced science and mathematics.
We know Python is the simplest programming language which we have come across and it greatly helps in problem-solving scenarios, but when it comes to deal with a large number of data it fails to reach to the expectation. So that’s why Numpy comes into existence to accelerate the speed of Python.
NumPy(Numerical Python) is considered as a very important element for scientific computing all over the world in order to work with data because it is the next big thing which we will witness. Numpy provides a multidimensional array ( Multi-dimension arrays are used if you want to put arrays inside an array)object, derived objects and methods that's work in the domain of linear algebra, Fourier transforms, statistics, etc.
Numpy is comprised of Python, C and C++. C++ are one of the reasons for fast computation. It also has tools for integrating with C, C++ and Fortran code.
Why use Numpy?
- Faster compared to Python’s List.
- Easy to implement.
- Precise.
- Uses less memory.
- Optimize
- Supports many mathematical, logical and scientific functionalities.
Numpy arrays vs Python’s List.
The above results show you how NumPy calculates the results faster than Python’s List.
The Numpy arrays consist of homogenous data types which result in the usage of contiguous memory while python’s List uses a non-contiguous form of memory and have heterogeneous data types.
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Two of the main features in Numpy: Vectorization and BroadCasting
Vectorization: Vectorization is used to speed up the Python code without using a loop. Operations are performed on matrixes results in a square matrix.
Operations such as Dot product, Cross product, etc.
Broadcasting: Certainly used when arrays size is different.
As I said earlier data can be significant in the coming future, to study different kinds of data, tech is going to play a crucial role in it. Data Science, Machine learning and Data Analysis are certainly needed such kind of libraries to perform well with data.
For more information about Numpy:https://numpy.org/
