How to replace NaNs by preceding values in pandas DataFrame

Question

Suppose I have a DataFrame with some NaNs    gt  gt  gt  import pandas as pd  gt  gt  gt  df   pd DataFrame   1  2  3    4  None  None    None  None  9     gt  gt  gt  df     0   1   2 0   1   2   3 1   4 NaN NaN 2 NaN NaN   9   What I need to do is replace every NaN with the first non-NaN value in the same column above it  It is assumed that the first row will never contain a NaN  So for the previous example the result would be     0  1  2 0  1  2  3 1  4  2  3 2  4  2  9   I can just loop through the whole DataFrame column-by-column  element-by-element and set the values directly  but is there an easy  optimally a loop-free  way of achieving this

User · Answer

ffill now has it s own method pd DataFrame ffill  df ffill         0    1    2 0  1 0  2 0  3 0 1  4 0  2 0  3 0 2  4 0  2 0  9 0

User · Answer

In my case  we have time series from different devices but some devices could not send any value during some period  So we should create NA values for every device and time period and after that do fillna   df   pd DataFrame    device1   1   first val of device1      device2   2   first val of device2      device3   3   first val of device3     df pivot index 1  columns 0  values 2  fillna method  ffill   unstack   reset index name  value     Result           0   1   value 0   device1     1   first val of device1 1   device1     2   first val of device1 2   device1     3   first val of device1 3   device2     1   None 4   device2     2   first val of device2 5   device2     3   first val of device2 6   device3     1   None 7   device3     2   None 8   device3     3   first val of device3

User · Answer

One thing that I noticed when trying this solution is that if you have N A at the start or the end of the array  ffill and bfill don t quite work   You need both   In  224   df   pd DataFrame  None  1  2  3  None  4  5  6  None    In  225   df ffill   Out 225        0 0  NaN 1  1 0     7  6 0 8  6 0  In  226   df bfill   Out 226        0 0  1 0 1  1 0     7  6 0 8  NaN  In  227   df bfill   ffill   Out 227        0 0  1 0 1  1 0     7  6 0 8  6 0

User · Answer

You could use the fillna method on the DataFrame and specify the method as ffill  forward fill     gt  gt  gt  df   pd DataFrame   1  2  3    4  None  None    None  None  9     gt  gt  gt  df fillna method  ffill      0  1  2 0  1  2  3 1  4  2  3 2  4  2  9   This method        propagate s  last valid observation forward to next valid   To go the opposite way  there s also a bfill method   This method doesn t modify the DataFrame inplace - you ll need to rebind the returned DataFrame to a variable or else specify inplace True   df fillna method  ffill   inplace True

User · Answer

Only one column version   Fill NAN with last valid value     df column name  fillna method  ffill   inplace True       Fill NAN with next valid value     df column name  fillna method  backfill   inplace True

User · Answer

Just agreeing with ffill method  but one extra info is that you can limit the forward fill with keyword argument limit     gt  gt  gt  import pandas as pd      gt  gt  gt  df   pd DataFrame   1  2  3    None  None  6    None  None  9      gt  gt  gt  df      0    1   2 0  1 0  2 0   3 1  NaN  NaN   6 2  NaN  NaN   9   gt  gt  gt  df 1  fillna method  ffill   inplace True   gt  gt  gt  df      0    1    2 0  1 0  2 0    3 1  NaN  2 0    6 2  NaN  2 0    9   Now with limit keyword argument   gt  gt  gt  df 0  fillna method  ffill   limit 1  inplace True    gt  gt  gt  df      0    1  2 0  1 0  2 0  3 1  1 0  2 0  6 2  NaN  2 0  9

User · Answer

The accepted answer is perfect  I had a related but slightly different situation where I had to fill in forward but only within groups  In case someone has the same need  know that fillna works on a DataFrameGroupBy object    gt  gt  gt  example   pd DataFrame   number   0 1 2 nan 4 nan 6 7 8 9   name  list  aaabbbcccc      gt  gt  gt  example   name  number 0    a     0 0 1    a     1 0 2    a     2 0 3    b     NaN 4    b     4 0 5    b     NaN 6    c     6 0 7    c     7 0 8    c     8 0 9    c     9 0  gt  gt  gt  example groupby  name    number   fillna method  ffill     fill in row 5 but not row 3 0    0 0 1    1 0 2    2 0 3    NaN 4    4 0 5    4 0 6    6 0 7    7 0 8    8 0 9    9 0 Name  number  dtype  float64

User · Answer

You can use pandas DataFrame fillna with the method  ffill  option   ffill  stands for  forward fill  and will propagate last valid observation forward  The alternative is  bfill  which works the same way  but backwards   import pandas as pd  df   pd DataFrame   1  2  3    4  None  None    None  None  9    df   df fillna method  ffill    print df      0  1  2  0  1  2  3  1  4  2  3  2  4  2  9   There is also a direct synonym function for this  pandas DataFrame ffill  to make things simpler

User · Answer

You can use fillna to remove or replace NaN values   NaN Remove  import pandas as pd  df   pd DataFrame   1  2  3    4  None  None    None  None  9     df fillna method  ffill        0    1    2 0  1 0  2 0  3 0 1  4 0  2 0  3 0 2  4 0  2 0  9 0   NaN Replace  df fillna 0    0 means What Value you want to replace       0    1    2 0  1 0  2 0  3 0 1  4 0  0 0  0 0 2  0 0  0 0  9 0   Reference pandas DataFrame fillna

[python] How to replace NaNs by preceding values in pandas DataFrame?

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