what is a reason to use ndarray instead of python array
Another great advantage of using NumPy arrays over built-in lists is the fact that NumPy has a C API that allows native C and C++ code to access NumPy arrays directly. Hence, many Python libraries written in low-level languages like C are expecting you to work with NumPy arrays instead of Python lists.
Reference: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
I build a class with some iteration over coming data. The data are in an array form without use of numpy objects. On my code I often use .append to create another array. At some point I changed one of the big array 1000x2000 to numpy.array. Now I have an error after error. I started to convert all of the arrays into ndarray but comments like .append does not work any more. I start to have a problems with pointing to rows, columns or cells. and have to rebuild all code.
I try to google an answer to the question: "what is and advantage of using ndarray over normal array" I can't find a sensible answer. Can you write when should I start to use ndarrays and if in your practice do you use both of them or stick to one only.
Sorry if the question is a novice level, but I am new to python, just try to move from Matlab and want to understand what are pros and cons. Thanks