How to create range of numbers in Python like in MATLAB
Question:
Is there any way to create a range of numbers in Python like MATLAB using a simple syntax, i.e, not using loops. For example:
MATLAB:
a = 1:0.5:10
give
a = [1 1.5 2 2.5 3 3.5 .... 9.5 10]
Answers:
import numpy as np
a = np.arange(1, 10, 0.5)
print (a)
Python’s built in xrange function can generate integers like so:
xrange(start, stop, step)
But xrange cannot do floats.
Numpy has arange and r_ which look something like this:
import numpy as np
print(np.arange(1, 3, .5))
# [ 1. 1.5 2. 2.5]
print(np.r_[1:3:.5])
# [ 1. 1.5 2. 2.5]
Notice that it is a little different than matlab, first the order of the stop and step are reversed in numpy compared to matlab, and second the stop is not included the the result. You might also consider using linspace it’s often preferred over arange when you’re working with floating point numbers because num can be defined more precisely than step:
print(np.linspace(1, 3, num=5))
# [ 1. 1.5 2. 2.5 3. ]
or
print(np.linspace(1, 3, num=4, endpoint=False))
# [ 1. 1.5 2. 2.5]
As others have pointed out, np.arange gets you closest to what you are used to from matlab. However, np.arange excludes the end point. The solution that you proposed in your own answer can lead to wrong results (see my comment).
This however, will always work:
start = 0
stop = 3
step = 0.5
a = np.arange(start, stop+step, step)
For further reading: Especially if you are an experienced matlab-user, this guide/cheat sheet might be interesting: Link
Is there any way to create a range of numbers in Python like MATLAB using a simple syntax, i.e, not using loops. For example:
MATLAB:
a = 1:0.5:10
give
a = [1 1.5 2 2.5 3 3.5 .... 9.5 10]
import numpy as np
a = np.arange(1, 10, 0.5)
print (a)
Python’s built in xrange function can generate integers like so:
xrange(start, stop, step)
But xrange cannot do floats.
Numpy has arange and r_ which look something like this:
import numpy as np
print(np.arange(1, 3, .5))
# [ 1. 1.5 2. 2.5]
print(np.r_[1:3:.5])
# [ 1. 1.5 2. 2.5]
Notice that it is a little different than matlab, first the order of the stop and step are reversed in numpy compared to matlab, and second the stop is not included the the result. You might also consider using linspace it’s often preferred over arange when you’re working with floating point numbers because num can be defined more precisely than step:
print(np.linspace(1, 3, num=5))
# [ 1. 1.5 2. 2.5 3. ]
or
print(np.linspace(1, 3, num=4, endpoint=False))
# [ 1. 1.5 2. 2.5]
As others have pointed out, np.arange gets you closest to what you are used to from matlab. However, np.arange excludes the end point. The solution that you proposed in your own answer can lead to wrong results (see my comment).
This however, will always work:
start = 0
stop = 3
step = 0.5
a = np.arange(start, stop+step, step)
For further reading: Especially if you are an experienced matlab-user, this guide/cheat sheet might be interesting: Link