replacing some missing values of a row with values of previous rows

Question

I have the following data set.

I have the following data set. As you see some of the values in column A, B, D, and R are missing. I am going to replace the values with exact values from previous rows. for example, in row 4, the value for column B and R are missing, and I need to replace them with 21 and 2 from pervious column.

  A   B        D        R        sentence              ADR1         ADR2     
 135 21  EffexorXR.21    1    lack of good feeling.     good        feeling
                                                          0              0
 136 21  EffexorXR.21    2    Feel disconnected        disconnected   feel    
 136     EffexorXR.21                                     0            0
 142 22  EffexorXR.22    1    Weight gain               gain         
 142                     1                                1

The final out put is like this:

  A  B          D        R        sentence              ADR1         ADR2     
 135 21  EffexorXR.21    1    lack of good feeling.     good        feeling
 135 21  EffexorXR.21    1                               1             0
 136 21  EffexorXR.21    2    Feel disconnected        disconnected   feel    
 136 21  EffexorXR.21    2                                 0            0
 142 22  EffexorXR.22    1    Weight gain               gain         
 142 22  EffexorXR.22    1                                1

I can use copy, but I do not know how to iterate between rows. Any suggestion ?

What is the file format for the data set-- CSV, tab seperated txt, or something else? — Ébe Isaac
– Ébe Isaac, Commented Jun 25, 2017 at 13:10

jezrael · Accepted Answer · 2017-06-25 13:10:44Z

5

Use fillna with method ffill what is same as ffill() if missing values are NaNs:

cols = ['A','B','D','R']
df[cols] = df[cols].ffill()

If missing values are empty strings:

cols = ['A','B','D','R']
df[cols] = df[cols].replace('',np.nan).ffill()

answered Jun 25, 2017 at 13:10

jezrael

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