CODEWITHSUNDEEP
pythonpython-3.xnumpy
alife
alife

Reputation: 629

Is there a numpy version of arange() that returns a generator object and not an entire array?

For reasons that I cannot understand, Python3 still doesn't supply a built-in range() like generator object for floats.

So enter numpy.arange(). However that function returns an array. Arrays of floats simply for iteration make no fundamental sense when they are very large, which for my uses are common.

Is there a function within numpy that supplies a generator version of arange(), or am I left to code it by hand?

Upvotes: 0

Views: 1018

Answers (1)

hpaulj
hpaulj

Reputation: 231605

The lack of float support in range doesn't seem like a big fault. The suggested link shows all kinds of ways of generating the numbers. They also point out the tricky issues. numeric_range also discusses these issues.

arange can handle float steps, but with a warning.

In [79]: np.arange(0,10,1.25)                                                   
Out[79]: array([0.  , 1.25, 2.5 , 3.75, 5.  , 6.25, 7.5 , 8.75])

linspace is recommended instead, with better end point control. For what it's worth MATLAB also has a linspace function.

In [80]: np.linspace(0,10,9)                                                    
Out[80]: array([ 0.  ,  1.25,  2.5 ,  3.75,  5.  ,  6.25,  7.5 ,  8.75, 10.  ])

But my gut feeling is that scaling range output would be the cleanest generator:

In [81]: g = (i*1.25 for i in range(9))   # generator expression                                 
In [82]: list(g)                                                                
Out[82]: [0.0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75, 10.0]

One of the link answers suggests itertools.takewhile:

In [83]: import itertools                                                       
In [86]: g = itertools.takewhile(lambda x: x<10, itertools.count(0,1.25))       
In [87]: list(g)                                                                
Out[87]: [0, 1.25, 2.5, 3.75, 5.0, 6.25, 7.5, 8.75]

Both of these answers feed one generator to another, consistent with the philosophy I cited in my comment. Building complex actions by stringing together smaller building blocks is typical Python, and even more so numpy.

Upvotes: 1

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