- In the Numpy matrix and vector operations are efficiently implemented. - NumPy array is faster and You get a lot built in with NumPy, FFTs, convolutions, fast searching, basic statistics, linear algebra, histograms, etc. - I used machine learning libraries like sci-kit-learn or tensorflow use numpy arrays as input which makes the computation faster - It supports vectorized computation - Efficient descriptive statistics and aggregating/summarizing data - In general, Numpy processes faster and uses less code compared to lists.

We can perform any types of operation in array using numpy

Numpy is one of the best libraries to deal with scientific calculation. what i feel the best in it they provide multiple function and we can say powerful to deal with big calculation and also makes thing easy to programmer. example: i fetch some data from website like quandle or NSE in CSV format and read that csv file and load that data in single list. so what if i want to change the dimension of that list. numpy provide that type of function we can change the dimension by using single function.