Updated On : Jul-10,2020 Time Investment : ~10 mins

Learning Numpy - Simple Tutorial For Beginners - NumPy - Advance Indexing - Part 9

It is possible to make a selection from ndarray that is a non-tuple sequence, ndarray object of integer or Boolean data type, or a tuple with at least one item being a sequence object. Advanced indexing always returns a copy of the data. As against this, the slicing only presents a view.

There are two types of Advanced Indexing − Integer and Boolean.

Integer Indexing This mechanism helps in selecting any arbitrary item in an array based on its Ndimensional index. Each integer array represents the number of indexes into that dimension. When the index consists of as many integer arrays as the dimensions of the target ndarray, it becomes straightforward.

In the following example, one element of specified column from each row of ndarray object is selected. Hence, the row index contains all row numbers, and the column index specifies the element to be selected.

Example 1

import numpy as np

x = np.array([[1, 2], [3, 4], [5, 6]])
y = x[[0,1,2], [0,1,0]]
print (y)

[1 4 5]

Example 2

import numpy as np
x = np.array([[ 0,  1,  2],[ 3,  4,  5],[ 6,  7,  8],[ 9, 10, 11]])

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

rows = np.array([[0,0],[3,3]])
cols = np.array([[0,2],[0,2]])
y = (x[rows,cols])

print ('The corner elements of this array are:')
print (y)

Our array is:
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]


The corner elements of this array are:
[[ 0  2]
 [ 9 11]]

Example 3

import numpy as np
x = np.array([[ 0,  1,  2],[ 3,  4,  5],[ 6,  7,  8],[ 9, 10, 11]])

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

# slicing 
z = x[1:4,1:3]

print ('After slicing, our array becomes:')
print (z)
print ('\n')

# using advanced index for column 
y = x[1:4,[1,2]]

print ('Slicing using advanced index for column:')
print (y)

Our array is:
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]


After slicing, our array becomes:
[[ 4  5]
 [ 7  8]
 [10 11]]


Slicing using advanced index for column:
[[ 4  5]
 [ 7  8]
 [10 11]]

Boolean Array Indexing

This type of advanced indexing is used when the resultant object is meant to be the result of Boolean operations, such as comparison operators.

Example 1

import numpy as np
x = np.array([[ 0,  1,  2],[ 3,  4,  5],[ 6,  7,  8],[ 9, 10, 11]])

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

# Now we will print the items greater than 5 
print ('The items greater than 5 are:')
print (x[x > 5])

Our array is:
[[ 0  1  2]
 [ 3  4  5]
 [ 6  7  8]
 [ 9 10 11]]


The items greater than 5 are:
[ 6  7  8  9 10 11]

Example 2

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

[1. 2. 3. 4. 5.]

Example 3

import numpy as np
a = np.array([1, 2+6j, 5, 3.5+5j])
print (a[np.iscomplex(a)])

[2. +6.j 3.5+5.j]

References

Dolly Solanki  Dolly Solanki

Share Views Stuck Somewhere? Need Help with Coding? Have Doubts About the Topic/Code?

When going through coding examples, it's quite common to have doubts and errors.

If you have doubts about some code examples or are stuck somewhere when trying our code, send us an email at coderzcolumn07@gmail.com. We'll help you or point you in the direction where you can find a solution to your problem.

You can even send us a mail if you are trying something new and need guidance regarding coding. We'll try to respond as soon as possible.

Share Views Want to Share Your Views? Have Any Suggestions?

If you want to

  • provide some suggestions on topic
  • share your views
  • include some details in tutorial
  • suggest some new topics on which we should create tutorials/blogs
Please feel free to contact us at coderzcolumn07@gmail.com. We appreciate and value your feedbacks. You can also support us with a small contribution by clicking DONATE.


Tags numpy, basics
Dolly Solanki

Dolly Solanki

Founder | Digital Marketer | Android Developer | Python Developer

Most Popular Python Tutorials

1. ipywidgets - An In-depth Guide to Interactive Widgets in Jupyter Notebook
2. Simple Guide to Style Display of Pandas DataFrames
3. imaplib - Simple Guide to Manage Mailboxes using Python
4. logging.config - Simple Guide to Configure Loggers from Dictionary and Config Files
5. line_profiler: Line by Line Profiling of Python Code

Tutorial Categories

Artificial Intelligence (81)
Data Science (73)
Digital Marketing (8)
Machine Learning (38)
Python (130)

Newsletter Subscription