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Problems with bnlearn as library regarding float numbers

I'm trying this notebook but on float numbers
https://github.com/erdogant/bnlearn/blob/master/notebooks/bnlearn.ipynb
Has anyone used "structure_learning.fit()" from bnlearn with float numbers?
My chart is blank. When I run a simple correlation on my dataframe, I get results so is not a a dataframe problem.
Another hint about my hypotheses : When I transform my float to binary, it works

Bnlearn in python only works with binary and not with cont values. This library is an adaptation of an R library so not everything is done. Currently P(A/B) can be done only for binary problems in this library. Please check the math of P(A/B) to understand

Python Panda.read_csv rounds to get import errors?

I have a 10000 x 250 dataset in a csv file. When I use the command
data = pd.read_csv('pool.csv', delimiter=',',header=None)
while I am in the correct path I actually import the values.
First I get the Dataframe. Since I want to work with the numpy package I need to convert this to its values using
data = data.values
And this is when i gets weird. I have at position [9999,0] in the file a -0.3839 as value. However after importing and calculating with it I noticed, that Python (or numpy) does something strange while importing.
Calling the value of data[9999,0] SHOULD give the expected -0.3839, but gives something like -0.383899892....
I already imported the file in other languages like Matlab and there was no issue of rounding those values. I aswell tried to use the .to_csv command from the pandas package instead of .values. However there is the exact same problem.
The last 10 elements of the first column are
-0.2716
0.3711
0.0487
-1.518
0.5068
0.4456
-1.753
-0.4615
-0.5872
-0.3839
Is there any import routine, which does not have those rounding errors?

Passing float_precision='round_trip' should solve this issue:
data = pd.read_csv('pool.csv',delimiter=',',header=None,float_precision='round_trip')

That's a floating point error. This is because of how computers work. (You can look it up if you really want to know how it works.) Don't be bothered by it, it is very small.
If you really want to use exact precision (because you are testing for exact values) you can look at the decimal module of Python, but your program will be a lot slower (probably like 100 times slower).
You can read more here: https://docs.python.org/3/tutorial/floatingpoint.html
You should know that all languages have this problem, only some are better in hiding it. (Also note that in Python3 this "hiding" of the floating point error has been improved.)
Since this problem cannot be solved by an ideal solution, you are given the task to solve it yourself and choose the most appropriate solution for your situtation
I don't know about 'round_trip' and its limitations, but it probably can help you. Other solutions would be to use float_format from the to_csv method. (https://docs.python.org/3/library/string.html#format-specification-mini-language)

Reading binary big endian files in python

I'd like to use python read a large binary file in ieee big endian 64bit floating point format, but am having trouble getting the correct values. I have a working method in matlab, as below:
fid=fopen(filename,'r','ieee-be');
data=fread(fid,inf,'float64',0,'ieee-be');
fclose(fid)
I've tried the following in python:
data = np.fromfile(filename, dtype='>f', count=-1)
This method doesn't throw any errors, but the values it reads are extremely large and incorrect. Can anyone help with a way to read these files? Thanks in advance.

Using >f will give you a single-precision (32-bit) floating point value. Instead, try
data = np.fromfile(filename, dtype='>f8', count=-1)

Pandas - Decimal format when writing to_csv instead of scientific

I've just started using Pandas and I'm trying to export my dataset using the to_csv function on my dataframe fp_df
One column (entitled fp_df['Amount Due'])has multiple decimal places (the result is 0.000042) - but when using to_csv it's being output in a scientific notation, which the resulting system will be unable to read. It needs to be output as '0.000042'.
What is the easiest way to do this? The other answers I've found seem overly complex and I don't understand how or why they work.
(Apologies if any of my terminology is off, I'm still learning)

Check the documentation for to_csv(), you'll find an attribute called float_format
df.to_csv(..., float_format='%.6f')
you can define the format you want as defined in the Format Specification Mini-Language

Python: Pandas and BigFloat

I have a pandas data frame with columns of data type float64.
I would like to increase the floating point precision to 200. I know you can do this with the BigFloat library.
I'm not sure what's the best way to increase the precision for floating point numbers in pandas.