Set the Digits After Decimal Point

How to display two digits after decimal point in SQL Server

select cast(your_float_column as decimal(10,2))
from your_table

decimal(10,2) means you can have a decimal number with a maximal total precision of 10 digits. 2 of them after the decimal point and 8 before.

The biggest possible number would be 99999999.99

Fixed digits after decimal with f-strings

Include the type specifier in your format expression:

>>> a = 10.1234
>>> f'{a:.2f}'
'10.12'

Number of digits after decimal point in pandas

You can use pd.set_option to set the decimal number display precision to e.g. 5 in this case:

pd.set_option("display.precision", 5)

or use:

pd.options.display.float_format = '{:.5f}'.format

Result:

print(df)    # with original value of 14.54345774

     Number
0   1.10000
1   2.20000
2   4.10000
3   5.40000
4   9.17600
5  14.54346
6  16.25664

How to get numbers after decimal point?

An easy approach for you:

number_dec = str(number-int(number))[1:]

Printing the correct number of decimal points with cout

With <iomanip>, you can use std::fixed and std::setprecision

Here is an example

#include <iostream>
#include <iomanip>

int main()
{
    double d = 122.345;

    std::cout << std::fixed;
    std::cout << std::setprecision(2);
    std::cout << d;
}

And you will get output

122.34

Limit the digits after decimal point (rounding) without breaking the gradient in TensorFlow

Converting to lower bit depth, like casting float32 to float16, is effectively rounding in base 2, replacing lower bits with zeros. This isn't the same as rounding in base 10; it won't necessarily replace lower base-10 decimal digits with zeros.

Assuming "base-2 rounding" is enough, TensorFlow's "fake_quant" ops are useful for this purpose, for instance tf.quantization.fake_quant_with_min_max_args. They simulate the effect of converting to lower bit-depth, yet are differentiable. The Post-training quantization guide might also be helpful.

Another thought: if you need to hack the gradient of something, check out the utilities tf.custom_gradient and tf.stop_gradient.

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