- 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.
Calculation
- Dot product
@,*,a.dot(b)
Visual Guide
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
1. Vectors, the 1D Arrays
Matrices, the 2D Arrays
The Basics
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.