Trouble copying a column to a file using Pandas

Question

The goal of the code is simply to do the OHE in two columns and write the rest of the columns as they are in the original file. But the column Dur, as shown in the image, is somehow "bugging" when it's written in the second file and passing more content than it should. I didn't want to limit the field because the original file's too large and probably has lines with longer and shorter fields and it could complicate the analysis later.

Image of the problem

import pandas as pd
from sklearn.preprocessing import LabelEncoder,OneHotEncoder

def opendataset():
    file = pd.read_csv('originalfiletest.binetflow')
    return file

def writefile():
    df.to_csv('newfiletest.binetflow', columns=['Dur','Proto','State','TotBytes','average_packet_size','average_bits_psecond'], index=False)

def writebackupproto():
    df.to_csv('fieldprotobackup.binetflow', columns=['Proto2','Proto'], index=False)

def writebackupstate():
    df.to_csv('fieldstatebackup.binetflow', columns=['State2','State'], index=False)

df = opendataset()

df['State2'] = df['State']
df['Proto2'] = df['Proto']

le = LabelEncoder()
dfle = df
dfle.State = le.fit_transform(dfle.State)
X = dfle[['State']].values
Y = dfle[['Proto']].values
ohe = OneHotEncoder()
OnehotX = ohe.fit_transform(X).toarray()
OnehotY = ohe.fit_transform(Y).toarray()

dx = pd.DataFrame(data=OnehotX)
dy = pd.DataFrame(data=OnehotY)

dfle['State'] = (dx[dx.columns[0:]].apply(lambda x:''.join(x.dropna().astype(int).astype(str)), axis=1))
dfle['Proto'] = (dy[dy.columns[0:]].apply(lambda y:''.join(y.dropna().astype(int).astype(str)), axis=1))

writefile()
writebackupproto()
writebackupstate()

Answer 1

Looks like the only bug is that your values are not truncated. You can just use the pandas.Series.apply method with a "truncating lambda" in order to get the expected result.

df.Dur = df.Dur.apply(lambda n: '%.6f' % n) 

A working example might be truncating pi

from math import pi
non_truncated = pd.Series(10*[pi])
non_truncated.apply(lambda n: '%.2f' % n)

You get a truncated series

0    3.14
1    3.14
2    3.14
3    3.14
4    3.14
5    3.14
6    3.14
7    3.14
8    3.14
9    3.14
dtype: object