Python, Pandas processing df

Question

I am currently learing python and I want to do the following:

I got a pandas dataframe:

Time                  A       B       ...   Z
2018-10-17 16:49:56  NaN     NaN      ...  16.28
2018-10-17 22:40:36  NaN     'String' ...  NaN
2018-10-20 01:37:32  NaN     NaN      ...  25.00
2018-10-20 11:30:39  15.0    NaN      ...  NaN
2018-10-20 12:07:04  NaN     NaN      ...  NaN

and I would like to transform it into something like this:

Time                  A       B       ...   Z
2018-10-17 16:49:56  NaN     NaN      ...  16.28
2018-10-17 22:40:36  NaN     'String' ...  16.28
2018-10-20 01:37:32  NaN     'String' ...  25.00
2018-10-20 11:30:39  15.0    'String' ...  25.00
2018-10-20 12:07:04  15.0    'String' ...  25.00

Where I always carry the value into the next row, except the row has a new item.

If I can do this, the next step would be to replace the string with a floating value which I can do with df = df.replace('string', value) and convert the Time into a numeric which contains the number of seconds t.hour * 3600 + t.minute * 60 + t.second would be my first options with the the module datetime . Currently I am unsure if this is the best way to go, because I know, there is pandas.to_datetime.

Time                  A       B       ...   Z
2018-10-17 16:49:56  NaN     NaN      ...  16.28+0
2018-10-17 22:40:36  NaN     value+0  ...  16.28+1
2018-10-20 01:37:32  NaN     value+1  ...  25.00+0
2018-10-20 11:30:39  15.0    value+2  ...  25.00+1
2018-10-20 12:07:04  15.0    value+3  ...  25.00+2

The Number of colums and rows is variable.

My Idea would be, to build a new dataframe and extract and compare the data with the old dataframe row by row, but I don't know exactly how to do it and maybe there is a more elegant way.

Can you help me?

Sai Kumar · Accepted Answer · 2018-10-21 00:42:44Z

3

You can use ffill docs

df = df.ffill()

          Time           A        B           Z
0   2018-10-17 16:49:56  NaN     NaN        16.28
1   2018-10-17 22:40:36  NaN    'String'    16.28
2   2018-10-20 01:37:32  NaN    'String'    25.00
3   2018-10-20 11:30:39  15.0   'String'    25.00
4   2018-10-20 12:07:04  15.0   'String'    25.00

edited Oct 21, 2018 at 0:42

answered Oct 21, 2018 at 0:37

Sai Kumar

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