Convert float to string in positional format (without scientific notation and false precision)

Unfortunately it seems that not even the new-style formatting with float.__format__ supports this. The default formatting of floats is the same as with repr; and with f flag there are 6 fractional digits by default:

>>> format(0.0000000005, 'f')
'0.000000'

---

However there is a hack to get the desired result - not the fastest one, but relatively simple:

• first the float is converted to a string using str() or repr()
• then a new Decimal instance is created from that string.
• Decimal.__format__ supports f flag which gives the desired result, and, unlike floats it prints the actual precision instead of default precision.

Thus we can make a simple utility function float_to_str:

import decimal

# create a new context for this task
ctx = decimal.Context()

# 20 digits should be enough for everyone :D
ctx.prec = 20

def float_to_str(f):
    """
    Convert the given float to a string,
    without resorting to scientific notation
    """
    d1 = ctx.create_decimal(repr(f))
    return format(d1, 'f')

Care must be taken to not use the global decimal context, so a new context is constructed for this function. This is the fastest way; another way would be to use decimal.local_context but it would be slower, creating a new thread-local context and a context manager for each conversion.

This function now returns the string with all possible digits from mantissa, rounded to the shortest equivalent representation:

>>> float_to_str(0.1)
'0.1'
>>> float_to_str(0.00000005)
'0.00000005'
>>> float_to_str(420000000000000000.0)
'420000000000000000'
>>> float_to_str(0.000000000123123123123123123123)
'0.00000000012312312312312313'

The last result is rounded at the last digit

As @Karin noted, float_to_str(420000000000000000.0) does not strictly match the format expected; it returns 420000000000000000 without trailing .0.

If you are satisfied with the precision in scientific notation, then could we just take a simple string manipulation approach? Maybe it's not terribly clever, but it seems to work (passes all of the use cases you've presented), and I think it's fairly understandable:

def float_to_str(f):
    float_string = repr(f)
    if 'e' in float_string:  # detect scientific notation
        digits, exp = float_string.split('e')
        digits = digits.replace('.', '').replace('-', '')
        exp = int(exp)
        zero_padding = '0' * (abs(int(exp)) - 1)  # minus 1 for decimal point in the sci notation
        sign = '-' if f < 0 else ''
        if exp > 0:
            float_string = '{}{}{}.0'.format(sign, digits, zero_padding)
        else:
            float_string = '{}0.{}{}'.format(sign, zero_padding, digits)
    return float_string

n = 0.000000054321654321
assert(float_to_str(n) == '0.000000054321654321')

n = 0.00000005
assert(float_to_str(n) == '0.00000005')

n = 420000000000000000.0
assert(float_to_str(n) == '420000000000000000.0')

n = 4.5678e-5
assert(float_to_str(n) == '0.000045678')

n = 1.1
assert(float_to_str(n) == '1.1')

n = -4.5678e-5
assert(float_to_str(n) == '-0.000045678')

Performance:

I was worried this approach may be too slow, so I ran timeit and compared with the OP's solution of decimal contexts. It appears the string manipulation is actually quite a bit faster. Edit: It appears to only be much faster in Python 2. In Python 3, the results were similar, but with the decimal approach slightly faster.

Result:

• Python 2: using ctx.create_decimal(): 2.43655490875

• Python 2: using string manipulation: 0.305557966232

• Python 3: using ctx.create_decimal(): 0.19519368198234588

• Python 3: using string manipulation: 0.2661344590014778

Here is the timing code:

from timeit import timeit

CODE_TO_TIME = '''
float_to_str(0.000000054321654321)
float_to_str(0.00000005)
float_to_str(420000000000000000.0)
float_to_str(4.5678e-5)
float_to_str(1.1)
float_to_str(-0.000045678)
'''
SETUP_1 = '''
import decimal

# create a new context for this task
ctx = decimal.Context()

# 20 digits should be enough for everyone :D
ctx.prec = 20

def float_to_str(f):
    """
    Convert the given float to a string,
    without resorting to scientific notation
    """
    d1 = ctx.create_decimal(repr(f))
    return format(d1, 'f')
'''
SETUP_2 = '''
def float_to_str(f):
    float_string = repr(f)
    if 'e' in float_string:  # detect scientific notation
        digits, exp = float_string.split('e')
        digits = digits.replace('.', '').replace('-', '')
        exp = int(exp)
        zero_padding = '0' * (abs(int(exp)) - 1)  # minus 1 for decimal point in the sci notation
        sign = '-' if f < 0 else ''
        if exp > 0:
            float_string = '{}{}{}.0'.format(sign, digits, zero_padding)
        else:
            float_string = '{}0.{}{}'.format(sign, zero_padding, digits)
    return float_string
'''

print(timeit(CODE_TO_TIME, setup=SETUP_1, number=10000))
print(timeit(CODE_TO_TIME, setup=SETUP_2, number=10000))

As of NumPy 1.14.0, you can just use numpy.format_float_positional. For example, running against the inputs from your question:

>>> numpy.format_float_positional(0.000000054321654321)
'0.000000054321654321'
>>> numpy.format_float_positional(0.00000005)
'0.00000005'
>>> numpy.format_float_positional(0.1)
'0.1'
>>> numpy.format_float_positional(4.5678e-20)
'0.000000000000000000045678'

numpy.format_float_positional uses the Dragon4 algorithm to produce the shortest decimal representation in positional format that round-trips back to the original float input. There's also numpy.format_float_scientific for scientific notation, and both functions offer optional arguments to customize things like rounding and trimming of zeros.