Skip to content

Courses
Tutorials
Practice
Contests

Numpy exercise
pandas
Matplotlib
Data visulisation
EDA
Machin Learning
Deep Learning
NLP
Data science
ML Tutorial
Computer Vision
ML project

numpy.angle() in Python

numpy.around() in Python

Last Updated : 08 Mar, 2024

Comments

Improve

Suggest changes

Like Article

Like

Report

numpy.around(arr, decimals = 0, out = None) : This mathematical function helps user to evenly round array elements to the given number of decimals.

Parameters :

array : [array_like] Input array.
decimal : [int, optional] Decimal places we want to round off.

Default = 0. In case of -ve decimal, it specifies the n0. of positions to the left of the decimal point.

out : [optional] Output resulted array

Return :

  An array with all array elements being rounded off,  having same type as input

Code #1 : Working

   # Python program explaining 
# around() function 
  
import numpy as np 
  
in_array = [.5, 1.5, 2.5, 3.5, 4.5, 10.1] 
print ("Input array : \n", in_array) 
  
round_off_values = np.around(in_array) 
print ("\nRounded values : \n", round_off_values) 
  
  
in_array = [.53, 1.54, .71] 
print ("\nInput array : \n", in_array) 
  
round_off_values = np.around(in_array) 
print ("\nRounded values : \n", round_off_values) 
  
in_array = [.5538, 1.33354, .71445] 
print ("\nInput array : \n", in_array) 
  
round_off_values = np.around(in_array, decimals = 3) 
print ("\nRounded values : \n", round_off_values) 
 
  

Output :

  Input array :    [0.5, 1.5, 2.5, 3.5, 4.5, 10.1]    Rounded values :    [  0.   2.   2.   4.   4.  10.]    Input array :    [0.53, 1.54, 0.71]    Rounded values :    [ 1.  2.  1.]    Input array :    [0.5538, 1.33354, 0.71445]    Rounded values :    [ 0.554  1.334  0.714]

Code 2 : Working

   # Python program explaining 
# around() function 
  
import numpy as np 
  
in_array = [1 ,4, 7, 9, 12] 
print ("Input array : \n", in_array) 
  
round_off_values = np.around(in_array) 
print ("\nRounded values : \n", round_off_values) 
  
  
in_array = [133 ,344, 437, 449, 12] 
print ("\nInput array : \n", in_array) 
  
round_off_values = np.around(in_array, decimals = -2) 
print ("\nRounded values upto 2: \n", round_off_values) 
  
in_array = [133 ,344, 437, 449, 12] 
print ("\nInput array : \n", in_array) 
  
round_off_values = np.around(in_array, decimals = -3) 
print ("\nRounded values upto 3: \n", round_off_values) 
 
  

Output :

  Input array :    [1, 4, 7, 9, 12]    Rounded values :    [ 1  4  7  9 12]    Input array :    [133, 344, 437, 449, 12]    Rounded values upto 2:    [100 300 400 400   0]    Input array :    [133, 344, 437, 449, 12]    Rounded values upto 3:    [0 0 0 0 0]

numpy.angle() in Python

mohit gupta_omg :)

Improve

Article Tags :

Python
Python numpy-Mathematical Function
Python-numpy

Practice Tags :

python

Similar Reads

numpy.arange() in Python

numpy.arange() function creates an array of evenly spaced values within a given interval. It is similar to Python's built-in range() function but returns a NumPy array instead of a list. Let's understand with a simple example: [GFGTABS] Python import numpy as np #create an array arr= np.arange(5 , 1

NumPy Array in Python

NumPy (Numerical Python) is a powerful library for numerical computations in Python. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of the NumPy library and is optimized for numerical and scientific computation in Python. Table of C

numpy.angle() in Python

numpy.angle() function is used when we want to compute the angle of the complex argument. A complex number is represented by â€œ x + yi " where x and y are real number and i= (-1)^1/2. The angle is calculated by the formula tan-1(x/y). Syntax : numpy.angle(z, deg=0) Parameters : z : [array_like] A com

numpy.fabs() in Python

numpy.fabs() function is used to compute the absolute values element-wise. This function returns the absolute values (positive magnitude) of the data in arr. It always return absolute values in floats. Syntax : numpy.fabs(arr, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, u

numpy.greater() in Python

The numpy.greater() checks whether x1 is greater than x2 or not. Syntax : numpy.greater(x1, x2[, out]) Parameters : x1, x2 : [array_like]Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape out : [ndarray, boolean]Array of bools, or a single bool if x1 and x2 are scala

numpy.floor() in Python

The numpy.floor() function returns the largest integer less than or equal to each element in the input array. It effectively rounds numbers down to the nearest whole number. Let's understand with an example: [GFGTABS] Python import numpy as np a = [0.5, 1.5, 2.5, 3, 4.5, 10.1] res = np.floor(a) prin

numpy.inner() in python

numpy.inner(arr1, arr2): Computes the inner product of two arrays. Parameters : arr1, arr2 : array to be evaluated. Return: Inner product of the two arrays. Code #1 : # Python Program illustrating # numpy.inner() method import numpy as geek # Scalars product = geek.inner(5, 4) print("inner Prod

numpy.fix() in Python

The numpy.fix() is a mathematical function that rounds elements of the array to the nearest integer towards zero. The rounded values are returned as floats. Syntax : numpy.fix(a, b = None) Parameters : a : [array_like] Input array to be floated. b : [ndarray, optional] Output array. Return : The arr

numpy.argwhere() in Python

numpy.argwhere() function is used to find the indices of array elements that are non-zero, grouped by element. Syntax : numpy.argwhere(arr) Parameters : arr : [array_like] Input array. Return : [ndarray] Indices of elements that are non-zero. Indices are grouped by element. Code #1 : # Python progra

numpy.allclose() in Python

numpy.allclose() function is used to find if two arrays are element-wise equal within a tolerance. The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(arr2)) and the absolute difference atol are added together to compare against the absolute differenc

Corporate & Communications Address:

A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)

Registered Address:

K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305

Advertise with us

Company
About Us
Legal
Privacy Policy
In Media
Contact Us
Advertise with us
GFG Corporate Solution
Placement Training Program

Languages
Python
Java
C++
PHP
GoLang
SQL
R Language
Android Tutorial
Tutorials Archive

DSA
Data Structures
Algorithms
DSA for Beginners
Basic DSA Problems
DSA Roadmap
Top 100 DSA Interview Problems
DSA Roadmap by Sandeep Jain
All Cheat Sheets

Data Science & ML
Data Science With Python
Data Science For Beginner
Machine Learning
ML Maths
Data Visualisation
Pandas
NumPy
NLP
Deep Learning

Web Technologies
HTML
CSS
JavaScript
TypeScript
ReactJS
NextJS
Bootstrap
Web Design

Python Tutorial
Python Programming Examples
Python Projects
Python Tkinter
Python Web Scraping
OpenCV Tutorial
Python Interview Question
Django

Computer Science
Operating Systems
Computer Network
Database Management System
Software Engineering
Digital Logic Design
Engineering Maths
Software Development
Software Testing

DevOps
Git
Linux
AWS
Docker
Kubernetes
Azure
GCP
DevOps Roadmap

System Design
High Level Design
Low Level Design
UML Diagrams
Interview Guide
Design Patterns
OOAD
System Design Bootcamp
Interview Questions

Inteview Preparation
Competitive Programming
Top DS or Algo for CP
Company-Wise Recruitment Process
Company-Wise Preparation
Aptitude Preparation
Puzzles

School Subjects
Mathematics
Physics
Chemistry
Biology
Social Science
English Grammar
Commerce
World GK

GeeksforGeeks Videos
DSA
Python
Java
C++
Web Development
Data Science
CS Subjects

@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved

We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy

Improvement

Suggest Changes

Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.

geeksforgeeks-suggest-icon

Create Improvement

Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.

geeksforgeeks-improvement-icon

Suggest Changes

min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences

Admission Experiences

Career Journeys

Work Experiences

Campus Experiences

Competitive Exam Experiences