Making a list of evenly spaced numbers in a certain range in python

What is a pythonic way of making list of arbitrary length containing evenly spaced numbers (not just whole integers) between given bounds? For instance:

my_func(0,5,10) # ( lower_bound , upper_bound , length )
# [ 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5 ] 

Note the Range() function only deals with integers. And this:

def my_func(low,up,leng):
    list = []
    step = (up - low) / float(leng)
    for i in range(leng):
        list.append(low)
        low = low + step
    return list

seems too complicated. Any ideas?

Given numpy, you could use linspace:

Including the right endpoint (5):

In [46]: import numpy as np
In [47]: np.linspace(0,5,10)
Out[47]: 
array([ 0.        ,  0.55555556,  1.11111111,  1.66666667,  2.22222222,
        2.77777778,  3.33333333,  3.88888889,  4.44444444,  5.        ])

Excluding the right endpoint:

In [48]: np.linspace(0,5,10,endpoint=False)
Out[48]: array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5])

You can use the following approach:

[lower + x*(upper-lower)/length for x in range(length)]

lower and/or upper must be assigned as floats for this approach to work.

Similar to unutbu's answer, you can use numpy's arange function, which is analog to Python's intrinsic function range. Notice that the end point is not included, as in range:

>>> import numpy as np
>>> a = np.arange(0,5, 0.5)
>>> a
array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5])
>>> a = np.arange(0,5, 0.5) # returns a numpy array
>>> a
array([ 0. ,  0.5,  1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ,  4.5])
>>> a.tolist() # if you prefer it as a list
[0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]

f = 0.5
a = 0
b = 9
d = [x * f for x in range(a, b)]

would be a way to do it.

You can use the folowing code:

def float_range(initVal, itemCount, step):
    for x in xrange(itemCount):
        yield initVal
        initVal += step

[x for x in float_range(1, 3, 0.1)]

Numpy's r_ convenience function can also create evenly spaced lists with syntax np.r_[start:stop:steps]. If steps is a real number (ending on j), then the end point is included, equivalent to np.linspace(start, stop, step, endpoint=1), otherwise not.

>>> np.r_[-1:1:6j, [0]*3, 5, 6]
array([-1. , -0.6, -0.2,  0.2,  0.6,  1.])

You can also directly concatente other arrays and also scalars:

>>> np.r_[-1:1:6j, [0]*3, 5, 6]
array([-1. , -0.6, -0.2,  0.2,  0.6,  1. ,  0. ,  0. ,  0. ,  5. ,  6. ])

Similar to Howard's answer but a bit more efficient:

def my_func(low, up, leng):
    step = ((up-low) * 1.0 / leng)
    return [low+i*step for i in xrange(leng)]