Numerical Python

Similar to pythons list object.

• All objects in an ndarray must have same datatype
• All math operators work on each individual element in an ''ndarray'

# Properties
a = np.ndarray((2,3,4))
 
a.shape      # Tuple describing shape, e.g. (2,3,4)
a.ndim       # Number of dimensions, equal to len(a.shape), e.g. 3
a.size       # Total number of elements
a.dtype      # Datatype of the elements
a.itemsize   # Size in byte of an element

a = np.arange(10)   # a => [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
a1 = a[:6:2]        # a1 => [0,2,4]
a[:6:2] = 30        # a => [30, 1, 30, 3, 30, 5, 6, 7, 8, 9]

# 2d-array
B = A[row_start: row_stop : (row_step) , col_start : col_stop : (col_step)] 

# Using a list
a = np.arange(10)   # a => [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
 
# a list naming the indices
l = [0, 2, 8, 9]
a[l]
a[[0,2,8,9]]
 
# or a list of bool, masking desired elements. Must be same dimension as the array.
bl = [False]*10
bl[3] = True
a[bl]            # => [3]

import numpy as np
 
a1 = np.zeros((4,3,2)) # Create 3-dimensional array with zeros
 
a2 = np.ones((3,4,5)) # Create 3-dimensional array with ones
 
a3 = np.arange(1,10) # Array with values 1..9
a4 = np.arange(10) # Array with values 0..9
a5 = np.arange(1,20,5) # Each 5th value between 1 to 19; [1, 6, 11, 16]