**Why Is There No Floating Point Range Implementation In The Standard Library?**

As made clear by all the posts here, there is no floating point version of `range()`

. That said, the omission makes sense if we consider that the `range()`

function is often used as an index (and of course, that means an *accessor*) generator. So, when we call `range(0,40)`

, we’re in effect saying we want 40 values starting at 0, up to 40, but non-inclusive of 40 itself.

When we consider that index generation is as much about the number of indices as it is their values, the use of a float implementation of `range()`

in the standard library makes less sense. For example, if we called the function `frange(0, 10, 0.25)`

, we would expect both 0 and 10 to be included, but that would yield a generator with 41 values, not the 40 one might expect from `10/0.25`

.

Thus, depending on its use, an `frange()`

function will always exhibit counter intuitive behavior; it either has too many values as perceived from the indexing perspective or is not inclusive of a number that reasonably should be returned from the mathematical perspective. In other words, it’s easy to see how such a function would appear to conflate two very different use cases – the naming implies the indexing use case; the behavior implies a mathematical one.

**The Mathematical Use Case**

With that said, as discussed in other posts, `numpy.linspace()`

performs the generation from the mathematical perspective nicely:

```
numpy.linspace(0, 10, 41)
array([ 0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75,
2. , 2.25, 2.5 , 2.75, 3. , 3.25, 3.5 , 3.75,
4. , 4.25, 4.5 , 4.75, 5. , 5.25, 5.5 , 5.75,
6. , 6.25, 6.5 , 6.75, 7. , 7.25, 7.5 , 7.75,
8. , 8.25, 8.5 , 8.75, 9. , 9.25, 9.5 , 9.75, 10.
])
```

**The Indexing Use Case**

And for the indexing perspective, I’ve written a slightly different approach with some tricksy string magic that allows us to specify the number of decimal places.

```
# Float range function - string formatting method
def frange_S (start, stop, skip = 1.0, decimals = 2):
for i in range(int(start / skip), int(stop / skip)):
yield float(("%0." + str(decimals) + "f") % (i * skip))
```

Similarly, we can also use the built-in `round`

function and specify the number of decimals:

```
# Float range function - rounding method
def frange_R (start, stop, skip = 1.0, decimals = 2):
for i in range(int(start / skip), int(stop / skip)):
yield round(i * skip, ndigits = decimals)
```

**A Quick Comparison & Performance**

Of course, given the above discussion, these functions have a fairly limited use case. Nonetheless, here’s a quick comparison:

```
def compare_methods (start, stop, skip):
string_test = frange_S(start, stop, skip)
round_test = frange_R(start, stop, skip)
for s, r in zip(string_test, round_test):
print(s, r)
compare_methods(-2, 10, 1/3)
```

The results are identical for each:

```
-2.0 -2.0
-1.67 -1.67
-1.33 -1.33
-1.0 -1.0
-0.67 -0.67
-0.33 -0.33
0.0 0.0
...
8.0 8.0
8.33 8.33
8.67 8.67
9.0 9.0
9.33 9.33
9.67 9.67
```

And some timings:

```
>>> import timeit
>>> setup = """
... def frange_s (start, stop, skip = 1.0, decimals = 2):
... for i in range(int(start / skip), int(stop / skip)):
... yield float(("%0." + str(decimals) + "f") % (i * skip))
... def frange_r (start, stop, skip = 1.0, decimals = 2):
... for i in range(int(start / skip), int(stop / skip)):
... yield round(i * skip, ndigits = decimals)
... start, stop, skip = -1, 8, 1/3
... """
>>> min(timeit.Timer('string_test = frange_s(start, stop, skip); [x for x in string_test]', setup=setup).repeat(30, 1000))
0.024284090992296115
>>> min(timeit.Timer('round_test = frange_r(start, stop, skip); [x for x in round_test]', setup=setup).repeat(30, 1000))
0.025324633985292166
```

Looks like the string formatting method wins by a hair on my system.

**The Limitations**

And finally, a demonstration of the point from the discussion above and one last limitation:

```
# "Missing" the last value (10.0)
for x in frange_R(0, 10, 0.25):
print(x)
0.25
0.5
0.75
1.0
...
9.0
9.25
9.5
9.75
```

Further, when the `skip`

parameter is not divisible by the `stop`

value, there can be a yawning gap given the latter issue:

```
# Clearly we know that 10 - 9.43 is equal to 0.57
for x in frange_R(0, 10, 3/7):
print(x)
0.0
0.43
0.86
1.29
...
8.14
8.57
9.0
9.43
```

There are ways to address this issue, but at the end of the day, the best approach would probably be to just use Numpy.