Convert list of numpy.float64 to float in Python quickly

Question

What is the fastest way of converting a list of elements of type numpy.float64 to type float? I am currently using the straightforward for loop iteration in conjunction with float().

I came across this post: Converting numpy dtypes to native python types, however my question isn't one of how to convert types in python but rather more specifically how to best convert an entire list of one type to another in the quickest manner possible in python (i.e. in this specific case numpy.float64 to float). I was hoping for some secret python machinery that I hadn't come across that could do it all at once :)

Answer 1

The tolist() method should do what you want. If you have a numpy array, just call tolist():

In [17]: a
Out[17]: 
array([ 0.        ,  0.14285714,  0.28571429,  0.42857143,  0.57142857,
        0.71428571,  0.85714286,  1.        ,  1.14285714,  1.28571429,
        1.42857143,  1.57142857,  1.71428571,  1.85714286,  2.        ])

In [18]: a.dtype
Out[18]: dtype('float64')

In [19]: b = a.tolist()

In [20]: b
Out[20]: 
[0.0,
 0.14285714285714285,
 0.2857142857142857,
 0.42857142857142855,
 0.5714285714285714,
 0.7142857142857142,
 0.8571428571428571,
 1.0,
 1.1428571428571428,
 1.2857142857142856,
 1.4285714285714284,
 1.5714285714285714,
 1.7142857142857142,
 1.857142857142857,
 2.0]

In [21]: type(b)
Out[21]: list

In [22]: type(b[0])
Out[22]: float

If, in fact, you really have python list of numpy.float64 objects, then @Alexander's answer is great, or you could convert the list to an array and then use the tolist() method. E.g.

In [46]: c
Out[46]: 
[0.0,
 0.33333333333333331,
 0.66666666666666663,
 1.0,
 1.3333333333333333,
 1.6666666666666665,
 2.0]

In [47]: type(c)
Out[47]: list

In [48]: type(c[0])
Out[48]: numpy.float64

@Alexander's suggestion, a list comprehension:

In [49]: [float(v) for v in c]
Out[49]: 
[0.0,
 0.3333333333333333,
 0.6666666666666666,
 1.0,
 1.3333333333333333,
 1.6666666666666665,
 2.0]

Or, convert to an array and then use the tolist() method.

In [50]: np.array(c).tolist()
Out[50]: 
[0.0,
 0.3333333333333333,
 0.6666666666666666,
 1.0,
 1.3333333333333333,
 1.6666666666666665,
 2.0]

---

If you are concerned with the speed, here's a comparison. The input, x, is a python list of numpy.float64 objects:

In [8]: type(x)
Out[8]: list

In [9]: len(x)
Out[9]: 1000

In [10]: type(x[0])
Out[10]: numpy.float64

Timing for the list comprehension:

In [11]: %timeit list1 = [float(v) for v in x]
10000 loops, best of 3: 109 µs per loop

Timing for conversion to numpy array and then tolist():

In [12]: %timeit list2 = np.array(x).tolist()
10000 loops, best of 3: 70.5 µs per loop

So it is faster to convert the list to an array and then call tolist().

Answer 2

You could use a list comprehension:

floats = [float(np_float) for np_float in np_float_list]

Answer 3

So out of the possible solutions I've come across (big thanks to Warren Weckesser and Alexander for pointing out all of the best possible approaches) I ran my current method and that presented by Alexander to give a simple comparison for runtimes (the two choices come as a result of the fact that I have a true list of elements of numpy.float64 and wish to convert them to float speedily):

2 approaches covered: list comprehension and basic for loop iteration

First here's the code:

import datetime
import numpy

list1 = []
for i in range(0,1000):
    list1.append(numpy.float64(i))
list2 = []
t_init = time.time()
for num in list1:
    list2.append(float(num))
t_1 = time.time()
list2 = [float(np_float) for np_float in list1]
t_2 = time.time()

print("t1 run time: {}".format(t_1-t_init))
print("t2 run time: {}".format(t_2-t_1))

I ran four times to give a quick set of results:

>>> run 1
t1 run time: 0.000179290771484375
t2 run time: 0.0001533031463623047
Python 3.4.0

>>> run 2
t1 run time: 0.00018739700317382812
t2 run time: 0.0001518726348876953
Python 3.4.0

>>> run 3
t1 run time: 0.00017976760864257812
t2 run time: 0.0001513957977294922
Python 3.4.0

>>> run 4
t1 run time: 0.0002455711364746094
t2 run time: 0.00015997886657714844
Python 3.4.0

Clearly to convert a true list of numpy.float64 to float, the optimal approach is to use python's list comprehension.