Python dataframe tranpose a single value from every nth row into a new column

Question

I have these alternating rows of times, and I want the times side by side, the Name and Value column are always redundant so the only value I am interested in preserving is the Time column by transposing those values a new column. But I can't quite figure out how to do this gracefully

Before:

	Name	Time	Value	Value2
0	Q	09:15	1
1	Q	09:16	0
2	Q	09:18	1
3	Q	09:19	0
4	P	22:30	1
5	P	23:20	0

After:

	Name	Time	Value	Value2
0	Q	09:15	1	09:16
1	Q	09:18	1	09:19
2	P	23:20	1	23:20

Answer 1

here is a solution that works with your example. In df1 you have the output

import pandas as pd


df=pd.DataFrame({
    "Name":["Q"  ,"Q" ,"Q" ,"Q" ,"P" ,"P"],
    "Time":["09:15", "09:16", "09:18", "09:19", "22:30", "23:20"],
    "Value":[1 ,0 ,1 ,0 ,1 ,0],
    "Value2":[None,None,None,None,None,None]})

df1=df.loc[df["Value"]!=0]
df2=df.loc[df["Value"]==0]
df2.index=df1.index
df1=df1.drop(columns=["Value2"])

df2=df2.drop(columns=["Name","Value","Value2"])
df2=df2.rename(columns={"Time":"Value2"})

df1=df1.join(df2)