Array Indexing

Accessing elements in the array is similar to accessing elements in a Python list:

print(r1[0])         # 1
print(r1[1])         # 2 

The following code snippet creates another array named r2, which is two‐dimensional:

list2 = [6,7,8,9,0]
r2 = np.array([list1,list2])     # rank 2 array
print(r2)
'''
[[1 2 3 4 5]
 [6 7 8 9 0]]
'''
print(r2.shape)             # (2,5) - 2 rows and 5 columns
print(r2[0,0])              # 1
print(r2[0,1])              # 2
print(r2[1,0])              # 6 

Here, r2 is a rank 2 array, with two rows and five columns.

Besides using an index to access elements in an array, you can also use a list as the index as follows:

list1 = [1,2,3,4,5]
r1 = np.array(list1)
print(r1[[2,4]])    # [3 5] 

Boolean Indexing

In addition to using indexing to access elements in an array, there is another very cool way to access elements in a NumPy array. Consider the following:

print(r1>2)     # [False False  True  True  True]...