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This article mainly introduces "Numpy or Python which is better". In daily operation, I believe many people have doubts about which is better between Numpy and Python. Xiaobian consulted various materials and sorted out simple and easy operation methods. I hope to help you answer the question of "Numpy or Python which is better"! Next, please follow the small series to learn together!

Will Numpy be the next Python?

Python is one of the most popular statistical computing languages in the world.

What's interesting is that Python was never designed as a statistical language by developers. Although Python does have some functional features, such as classless computation, it is largely an object-oriented language. Python is more similar to languages like C++ than traditional statistical languages like R. So Python's codebase wasn't originally created for data science.

Languages such as R, Julia, and even Go typically implement basic functions and operands for performing linear algebra operations, whereas the Python base library does not have such functions. If you've been using Python for linear algebra for some time, you're likely to declare this classic statement in your code:

import numpy as np

Numpy increased Python's statistical power from 20% to 100%. It's hard for data scientists to avoid Numpy.

Numpy not only has linear algebra features that Python lacks, but it also has many math and data management features that make work easier.

Not only that, Pandas(Python's database management library) is built on Numpy. This makes the integration of the two almost seamless. Numpy and Pandas 'stable, powerful codebase makes data science tasks smooth. This is all the reason Python is ahead of many other languages in data science. It's simple and easy to understand.

Given that most high-level languages implement their own linear algebraic functions, it seems odd that Python is a little lower and requires more than one operand or function.

comparison

Without Numpy, how would Python do math between arrays?

How does it compete with other statistical languages today? In fact, Python's array iterating capabilities are great. The zip() function can traverse both lists simultaneously.

array =[] for f, bin zip(array1, array2): res = f * b array.append(res)

Although this may seem easy, comparing Python to another language (such as R or Julia) reveals an easier way to obtain the product of two arrays.

For example, in R:

array

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