How to fill values in pandas column (which was zero before) to a proper integer/float values from the next coming rows?

Question

I have been working on large dataset with Lat & Longitude Data. I am doing interpolation for the whole df, so before that i just want my latitude and longitude column to have elements to filled with values without zeros.

My Dataframe (explanation purpose) :

Time  lat  long
0  0  0
1  0  0
2  0  0
3  0  0
4  0  0
5  0  0
6  0  0
7  5  2
8  5  2
9  0  0
10  0  0
11  0  0
12  0  0
13  6  1
14  6  1

My Requirement :

    Time  lat  long
    0  5  2
    1  5  2
    2  5  2
    3  5  2
    4  5       2
    5  5  2
    6  5  2
    7  5  2
    8  5  2
    9  6  1
    10  6  1
    11  6  1
    12  6  1
    13  6  1
    14  6  1

What I needed

I hope you understand from the actual df and my requirement.

I just want to take the next last element which has proper values other than zero & fill that element in previous rows of that column.

Request

I believe that there will be some nice methods to complete my work done..

I am sure this is a dupe, df[['lat', 'long']].replace(0, np.nan).bfill().astype(int) — Vaishali, Feb 04 '19 at 15:06

score 2 · Accepted Answer · answered Feb 04 '19 at 15:06

You could replace all 0s with np.nan and use bfill to fill them with the next valid value:

df.loc[:,'Time':].replace(0,np.nan).bfill()

Time  lat  long
0    1.0  5.0   2.0
1    1.0  5.0   2.0
2    2.0  5.0   2.0
3    3.0  5.0   2.0
4    4.0  5.0   2.0
5    5.0  5.0   2.0
6    6.0  5.0   2.0
7    7.0  5.0   2.0
8    8.0  5.0   2.0
9    9.0  6.0   1.0
10  10.0  6.0   1.0
11  11.0  6.0   1.0
12  12.0  6.0   1.0
13  13.0  6.0   1.0
14  14.0  6.0   1.0

Thank u so much !!! It works @yatu – Mari Feb 05 '19 at 08:35 — Mari, Feb 05 '19 at 08:35