0.30000000000000004 | Floating Point Math Examples | Math library

In run the follow code:

I'm experimenting with the precision of a double value in various programming languages.

I am trying to plot the content of the arrays x and lj in the following code. However, the point in the plots are not the one inside the arrays. Can someone help me see the mistake I did ? I am still new in python.

In our C++ codebase, we have a default formatting method to convert double floating point numbers into strings, that is used notably for JSON serialization and for debug logs. For that default number formatting, I have the following contradictory requirements:

1. Favor human readability. Prefer 1000 to 1e3 or 0.125 to 1.25e-1.
2. Keep the precision. Prefer 3.1415926535897931 to 3.14.
3. Avoid spurious digits for decimal numbers. Prefer 0.1 to 0.10000000000000001.

Up to now, the best tradeoff I found is to use the equivalent of printf("%.15g", value) formatting. It fulfills requirements 1 and 3, but not completely 2. There is a loss of precision of about 4 bits.

Other people use a default formatting based on "%.17g", which fulfills requirements 1 and 2, but not 3. The number 0.2 is for example formatted as 0.20000000000000001.

In between, the format "%.16g" is close to fulfill requirements 2 and 3, but not always for both.

As an illustration, I wish 0.3 to be formatted as 0.3, but 0.1+0.2, which is slightly bigger due to rounding errors, to be formatted as 0.30000000000000004 to see the difference.

I wrote the following function that format floating point numbers the way I wish, as a proof of concept. However it is unacceptable on the performance point of view, since it can make up to 34 conversions between double and strings, for a limited precision gain over the current implementation with "%.15g".

Hello so i have this function that returns two lists, (list_y and list_t), i wanna use these lists separately, because i want to draw a graph using them (list_y represent the y label and list_t represents the x labels )

The following reveals a difference in the way numbers are generated by division vs multiplication:

The following code seems incredibly simple. an integer is passed to the function in Python, which creates a PyList in C then populates it:

hello.c:

So let's start with normal values in the regular R setup:

I'm having trouble figuring ou ta solution to my ploblem, so here it is:

I have this column "Cs/min - bruto" in my dataframe (df_refData):