What is NumPy? The centerpiece of the Data & AI line

If you're interested in Data Science, Machine Learning or Million Row Calculation, NumPy is the first Library you need to know! Because without it, data management in Python would be significantly slower.

❓ is NumPy?

NumPy, short for Numerical Python, is a basic Library of the Python language designed to deal with advanced numbers and mathematics, specifically dealing with sequence rows of large data called "Arrays."

Compare to visualize:

* Python List Normal: Like a sundry bag, can fit numbers, letters, but slow to work when a lot of things

* NumPy Array: Like a well-organized shelf, it only stores exactly the same numbers, allows it to be picked up and calculated 10-100 times faster than a normal Python List!

✨ 3 reasons why NumPy is the "mother" of the industry.

1. High Speed Processing Power (High Performance)

NumPy is written in C and Fortran, making it work with numbers at speeds similar to low-level computer languages, even if you write them in a simple-to-read Python.

2.Multi-Dimensional Array Management (N-Dimensional Array)

Whether it's 1D (numerical list), 2D (Excel table), or 3D or more (photo / video data), NumPy manages to run out through something called an ndarray.

3. Complete math functions in one place

There are many finished calculating formulas! Both basic statistics (mean, deviation), linear algebra, to Fourier Transform.

🚀 what can NumPy use to do?

* Data Analysis: It's the basis of a famous Library like Pandas (without NumPy, pandas don't work!)

* Machine Learning: Manage Input data before sending into the AI model (for example, the image will be converted into an Array of Numbers)

* Image Processing: Because one image is a group of numbers (pixels), NumPy is used to color, rotate, or zoom images.

* Scientific Research: Scientific research that requires calculating difficult formulas often uses NumPy as a driver.

💡 Summary for newbies

If Python is a "notebook," NumPy is a free "high-speed scientific calculator." Anyone who goes to the Data line or writes a computational program has to start practicing this first!

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