Numpy (Numerical Python), the backbone of all Data Science and Machine Learning
Numpy aka Numerical Python is the foundational base for all the data science and machine learning projects. Numpy is very similar to lists in python. But what makes it different from lists is that Numpy is 50 times faster than lists in python.
Arrays are very frequently used in data science, where speed and resources are very important. Since Numpy arrays are stored in memory unlike lists so processing and manipulating them is very efficient and fast.
The main purpose of the Machine Learning model is to train the model for predicting a certain level of accuracy. The machine can only understand numbers and unlike human beings, it cannot understand strings, regular words, phrases, all of them must be converted to numerical form before feeding it to the machine. This idea of converting the data into something that machine can understand like numbers allow us to make the machine learn by itself and that is how the machine learns.
By storing the information in an array-like Numpy, it is a great way for us to represent that data from what humans can understand to one computer can understand. So, Numpy can be considered as the backbone of all the data science and machine learning.
Also, another thing that it makes it so fast is that it is backed up with C, the programming language which is very close to the machine and everyone knows about the speed of C programming language.
Now I will show you the best example to demonstrate how much Numpy is faster than that of traditional python.
Here you can see that doing sum using NumPy (np), where np is an alias name for Numpy, it makes it much faster which took it around 9.56 microsecond +/- 137 nanosecond which is very much less if we compare it with normal python. And in the Machine Learning and Data Science world, time plays a vital role. So, Numpy helps us to achieve this for us. All thanks to Numpy for its robustness.
Well, these were the things I liked to talk about Numpy which the core part of Machine Learning and Data Science.
