Learning Numpy - Simple Tutorial For Beginners - NumPy - Indexing & Slicing Part 8

Contents of ndarray object can be accessed and modified by indexing or slicing, just like Python's in-built container objects.

As mentioned earlier, items in ndarray object follows zero-based index. Three types of indexing methods are available − field access, basic slicing and advanced indexing.

Basic slicing is an extension of Python's basic concept of slicing to n dimensions. A Python slice object is constructed by giving start, stop, and step parameters to the built-in slice function. This slice object is passed to the array to extract a part of array.

Example 1

import numpy as np
a = np.arange(10)
s = slice(2,7,2)
print (a[s])

[2 4 6]

Example 2

import numpy as np
a = np.arange(10)
b = a[2:7:2]
print (b)

[2 4 6]

Example 3

# slice single item 
import numpy as np

a = np.arange(10)
b = a[5]
print (b)

5

Example 4

# slice items starting from index 
import numpy as np
a = np.arange(10)
print (a[2:])

[2 3 4 5 6 7 8 9]

Example 5

# slice items between indexes 
import numpy as np
a = np.arange(10)
print (a[2:5])

[2 3 4]

Example 6

import numpy as np
a = np.array([[1,2,3],[3,4,5],[4,5,6]])
print (a)

# slice items starting from index
print ('Now we will slice the array from the index a[1:]')
print (a[1:])

[[1 2 3]
 [3 4 5]
 [4 5 6]]
Now we will slice the array from the index a[1:]
[[3 4 5]
 [4 5 6]]

Example 7

# array to begin with 
import numpy as np
a = np.array([[1,2,3],[3,4,5],[4,5,6]])

print ('Our array is:')
print (a)
print ('\n')

# this returns array of items in the second column 
print ('The items in the second column are:')
print (a[...,1])
print ('\n')

# Now we will slice all items from the second row 
print ('The items in the second row are:')
print (a[1,...])
print ('\n')

# Now we will slice all items from column 1 onwards 
print ('The items column 1 onwards are:')
print (a[...,1:])

Our array is:
[[1 2 3]
 [3 4 5]
 [4 5 6]]


The items in the second column are:
[2 4 5]


The items in the second row are:
[3 4 5]


The items column 1 onwards are:
[[2 3]
 [4 5]
 [5 6]]

References

• Learning Numpy - Array From Existing Data Part 6

• Learning Numpy - Array Creation Routines Part 5

• Learning Numpy - Array Attributes Part 4

• Learning Numpy - Data Types Part 3