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Coming from a Lists background in Python and that of programming languages like C++/Java, one is used to the notation of extracting elements using a[i][j] approach. But in NumPy, one usually does a[i,j]. Both of these would return the same result.
What is the fundamental difference between the two and which should be preferred?
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NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the size of an ndarray will create a new array and delete the original. The elements in a NumPy array are all required to be of the same data type, and thus will be the same size in memory.
In the NumPy with the help of shape() function, we can find the number of rows and columns. In this function, we pass a matrix and it will return row and column number of the matrix. Return: The number of rows and columns.
NumPy is fast because it can do all its calculations without calling back into Python. Since this function involves looping in Python, we lose all the performance benefits of using NumPy. For a 10,000,000-entry NumPy array, this functions takes 2.5 seconds to run on my computer.
The array object in NumPy is called ndarray . We can create a NumPy ndarray object by using the array() function.
The main difference is that a[i][j] first creates a view onto a[i] and then indexes into that view. On the other hand, a[i,j] indexes directly into a, making it faster:
In [9]: a = np.random.rand(1000,1000)
In [10]: %timeit a[123][456]
1000000 loops, best of 3: 586 ns per loop
In [11]: %timeit a[123,456]
1000000 loops, best of 3: 234 ns per loop
For this reason, I'd prefer the latter.
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