Skip to content
Related Articles
Open in App
Not now

Related Articles

numpy.linspace() in Python

Improve Article
Save Article
  • Difficulty Level : Easy
  • Last Updated : 19 May, 2022
Improve Article
Save Article

The numpy.linspace() function returns number spaces evenly w.r.t interval. Similar to numpy.arange() function but instead of step it uses sample number. 
Syntax : 
 

numpy.linspace(start,
               stop,
               num = 50,
               endpoint = True,
               retstep = False,
               dtype = None)

Parameters : 

-> start  : [optional] start of interval range. By default start = 0
-> stop   : end of interval range
-> restep : If True, return (samples, step). By default restep = False
-> num    : [int, optional] No. of samples to generate
-> dtype  : type of output array

Return : 
 

-> ndarray
-> step : [float, optional], if restep = True

Code 1 : Explaining linspace function 
 

Python




# Python Programming illustrating
# numpy.linspace method
 
import numpy as geek
 
# restep set to True
print("B\n", geek.linspace(2.0, 3.0, num=5, retstep=True), "\n")
 
# To evaluate sin() in long range
x = geek.linspace(0, 2, 10)
print("A\n", geek.sin(x))

Output : 

B
 (array([ 2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)

A
 [ 0.          0.22039774  0.42995636  0.6183698   0.77637192  0.8961922
  0.9719379   0.99988386  0.9786557   0.90929743]

Code 2 : Graphical Representation of numpy.linspace() using matplotlib module – pylab 

Python




# Graphical Representation of numpy.linspace()
import numpy as geek
import pylab as p
 
# Start = 0
# End = 2
# Samples to generate = 10
x1 = geek.linspace(0, 2, 10, endpoint = False)
y1 = geek.ones(10)
 
p.plot(x1, y1, '*')
p.xlim(-0.2, 1.8)

Output : 
 

Code 3 : Graphical Representation of numpy.linspace() using pylab 
 

Python




# Graphical Representation of numpy.linspace()
import numpy as geek
import pylab as p
 
# Start = 0
# End = 2
# Samples to generate = 15
x1 = geek.linspace(0, 2, 15, endpoint = True)
y1 = geek.zeros(15)
 
p.plot(x1, y1, 'o')
p.xlim(-0.2, 2.1)

Output : 
 

Note : 
These NumPy-Python programs won’t run on online IDE’s, so run them on your systems to explore them 
.
This article is contributed by Mohit Gupta_OMG 😀. If you like GeeksforGeeks and would like to contribute, you can also write an article using write.geeksforgeeks.org or mail your article to review-team@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.
 


My Personal Notes arrow_drop_up
Related Articles

Start Your Coding Journey Now!