Get cell value and fill rows of new column in python pandas

Jailbone :

I have data looking like this:

Col1
time: 4
1
2
3
time: 7
4
5
6
time: 11
7 
8
...

I want to add a new column an make it to look like this:

Col1      Col2
time: 4   4
1         4
2         4
3         4
time: 7   7
4         7
5         7
6         7
time: 11  11
7         11
8         11
...       ...

So I want to grab the specific value from the rows "time: x" and put them in the rows of the new column until the next row with "time: x" appears. Any suggestions? I am not even quite sure if the value in the row is an integer or string. I appreciate your help!

anky_91 :

You can try something like:

df['Col2']=(df.groupby(df['Col1'].str.contains('time:').cumsum())['Col1'].transform('first')
        .str.split(':').str[-1])
print(df)

---

        Col1 Col2
0    time: 4    4
1          1    4
2          2    4
3          3    4
4    time: 7    7
5          4    7
6          5    7
7          6    7
8   time: 11   11
9          7   11
10         8   11
....
....

Explanation:

First we create a helper series which returns True for all the rows having the word time and then cumulative sum them:

print(df['Col1'].str.contains('time:').cumsum())

0     1
1     1
2     1
3     1
4     2
5     2
6     2
7     2
8     3
9     3
10    3

Now we can treat this as individual groups so we groupby on this helper series and return the first value:

print(df.groupby(df['Col1'].str.contains('time:').cumsum())['Col1'].transform('first'))

0      time: 4
1      time: 4
2      time: 4
3      time: 4
4      time: 7
5      time: 7
6      time: 7
7      time: 7
8     time: 11
9     time: 11
10    time: 11

Once we have this result , we can chain str.split which splits the series on : and return the last element of the split by using .str[-1].

Hope that helps.