7 examples of 'float upto 2 decimal in python' in Python

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def fmtFloat(n):
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    return '{:6.3f}'.format(n)

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def to_float(num):
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    """Convert anything to float."""
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    return float(to_num(num))

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def test_decimal_int(self):
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    a, b = mathfilters.handle_float_decimal_combinations(Decimal('2.0'), 1, '+')
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    self.assertTrue(isinstance(a, Decimal), 'Type is {0}'.format(type(a)))
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    self.assertTrue(isinstance(b, int), 'Type is {0}'.format(type(b)))

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def truncate_float(float_num, n):
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    str_repr = str(float_num)
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    idx = str_repr.find('.')
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    if idx > 0:
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        return float(str_repr[0: 1 + idx + n])
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    else:
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        return float_num

10
def convert_to_decimal(number, frac=False):
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    bad_type = not isinstance(number, (int, long, str, unicode, Decimal, Fraction))
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    if bad_type:
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        message = "{}, of type {} is not suitable as a Decimal"
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        logger.warning(message.format(number, type(number)))
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    return Fraction(number) if frac else Decimal(number)

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def BN_convert_float(module):
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    '''
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    Designed to work with network_to_half.
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    BatchNorm layers need parameters in single precision.
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    Find all layers and convert them back to float. This can't
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    be done with built in .apply as that function will apply
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    fn to all modules, parameters, and buffers. Thus we wouldn't
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    be able to guard the float conversion based on the module type.
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    '''
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    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
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        module.float()
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    for child in module.children():
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        BN_convert_float(child)
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    return module

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def makeFloat(sign, ds, tail):
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        return float((sign or '') + ds + tail)