iterrows pandas get next rows value

Question

I have a df in pandas  import pandas as pd df   pd DataFrame   AA    BB    CC    columns     value      I want to iterate over rows in df  For each row i want rows value and next rows value Something like it does not work    for i  row in df iterrows         print row  value        i1  row1   next df iterrows         print row1  value     As a result I want    AA   BB   BB   CC   CC   Wrong index error here     At this point i have mess way to solve this  for i in range 0  df shape 0      print df irow i   value      print df irow i 1   value     Is there more efficient way to solve this issue

User · Answer

There is a pairwise   function example in the itertools document   from itertools import tee  izip def pairwise iterable        s - gt   s0 s1    s1 s2    s2  s3            a  b   tee iterable      next b  None      return izip a  b   import pandas as pd df   pd DataFrame   AA    BB    CC    columns     value     for  i1  row1    i2  row2  in pairwise df iterrows         print i1  i2  row1  value    row2  value     Here is the output   0 1 AA BB 1 2 BB CC   But  I think iter rows in a DataFrame is slow  if you can explain what s the problem you want to solve  maybe I can suggest some better method

User · Answer

a combination of answers gave me a very fast running time   using the shift method to create new column of next row values  then using the row iterator  function as  alisdt did  but here i changed it from iterrows to itertuples which is 100 times faster    my script is for iterating dataframe of duplications in different length and add one second for each duplication so they all be unique     create new column with shifted values from the departure time column df  next column value     df  column value   shift 1    create row iterator that can  save  the next row without running for loop row iterator   df itertuples     jump to the next row using the row iterator last   next row iterator    because pandas does not support items alteration i need to save it as an object t   last your column num    run and update the time duplications with one more second each for row in row iterator      if row column value    row next column value           t   t   add sec          df result at row Index   column name     t     else             here i resetting the  last  and  t  values          last   row          t   last your column num    Hope it will help

User · Answer

This can be solved also by izipping the dataframe  iterator  with an offset version of itself    Of course the indexing error cannot be reproduced this way   Check this out  import pandas as pd from itertools import izip  df   pd DataFrame   AA    BB    CC    columns     value        for id1  id2 in izip df iterrows   df ix 1   iterrows         print id1 1   value       print id2 1   value     which gives  AA BB BB CC

User · Answer

Firstly  your  messy way  is ok  there s nothing wrong with using indices into the dataframe  and this will not be too slow  iterrows   itself isn t terribly fast   A version of your first idea that would work would be   row iterator   df iterrows      last   row iterator next      take first item from row iterator for i  row in row iterator      print row  value        print last  value        last   row   The second method could do something similar  to save one index into the dataframe   last   df irow 0  for i in range 1  df shape 0        print last      print df irow i       last   df irow i    When speed is critical you can always try both and time the code

User · Answer

I would use shift   function as follows    df  value 1     df value shift -1   print x  for x in df T unstack   dropna how    any   values     which produces  AA BB BB CC CC   This is how the code above works   Step 1  Use shift function   df  value 1     df value shift -1  print df    produces  value value 1 0    AA      BB 1    BB      CC 2    CC     NaN   step 2  Transpose   df   df T print df    produces              0   1    2 value    AA  BB   CC value 1  BB  CC  NaN   Step 3  Unstack   df   df unstack   print df    produces   0  value       AA    value 1     BB 1  value       BB    value 1     CC 2  value       CC    value 1    NaN dtype  object   Step 4  Drop NaN values  df   df dropna how    any   print df    produces   0  value      AA    value 1    BB 1  value      BB    value 1    CC 2  value      CC dtype  object   Step 5  Return a Numpy representation of the DataFrame  and print value by value   df   df values  print x  for x in df     produces    AA BB BB CC CC

[python] iterrows pandas get next rows value

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