Numpy Notes

Jargon

Numpy = numerical Python

ndarray = n-dimensional array

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.

NumPy axes are the directions along the rows and columns.

axes = dimensions

pay very careful attention to what the axis parameter actually controls for each function.

# 2 axes
# axis 0 has length 2
# axis 1 has length 3
[[1., 0., 0.],
 [0., 1., 2.]]

2D array (matrix) => Oy đi [0, - vô cực], Ox đi [0, + vô cực]
3D array (cube, lists of matrices) => Oy đi [0, - vô cực], Ox đi [0, + vô cực], Oz đi [0, + vô cực] mũi tên hướng về phía đi vào trong màn hình

Khi print 3D array thì nó print last axis (Oz) top to bottom

attributes of an ndarray object:
- ndarray.ndim: the number of axes (dimensions) of the array.
- ndarray.shape: the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m) or (row, column). The length of the shape tuple is therefore the number of axes, ndim.
- ndarray.size: the total number of elements of the array. This is equal to the product of the elements of shape.
- ndarray.dtype: an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples.

# 3 rows, 5 columns
>>> a = np.arange(15).reshape(3, 5)
>>> a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

.reshape(1st axis, 2nd axis, 3rd axis) or .reshape(Oy, Ox, Oz) or .reshape(number of row, number of column, height)