Python Numpy array

Apr 3, 2023

• 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
• ndarray.size
  • The total number of elements of the array.
• ndarray.dtype
  • An object describing the type of the elements in the array.
  • One can create or specify dtype’s using standard Python types.
• ndarray.data
  • The buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities.
• ndarray.itemsize
  • The size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize.

• Dot product
  @, *, a.dot(b)

Medium

NumPy arrays are:

• more compact, especially when there’s more than one dimension
• faster than lists when the operation can be vectorized
• slower than lists when you append elements to the end
• usually homogeneous: can only work fast with elements of one type

NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers.
In NumPy dimensions are called axes.