Is there a way in Pandas to use previous row value in dataframe apply when previous value is also calculated in the apply

Question

I have the following dataframe    Index Date    A    B    C    D                                   2015-01-31    10   10   Nan  10  2015-02-01     2    3   Nan  22   2015-02-02    10   60   Nan  280  2015-02-03    10   100   Nan  250   Require    Index Date    A    B    C    D                                   2015-01-31    10   10   10   10  2015-02-01     2    3   23   22  2015-02-02    10   60   290  280  2015-02-03    10   100  3000 250   Column C is derived for 2015-01-31 by taking value of D   Then I need to use the value of C for 2015-01-31 and multiply by the value of A on 2015-02-01 and add B   I have attempted an apply and a shift using an if else by this gives a key error

User · Answer

Given a column of numbers   lst      cols     A   for a in range 100  105       lst append  a   df   pd DataFrame lst  columns cols  index range 5   df      A 0   100 1   101 2   102 3   103 4   104   You can reference the previous row with shift   df  Change     df A - df A shift 1  df      A   Change 0   100 NaN 1   101 1 0 2   102 1 0 3   103 1 0 4   104 1 0

User · Answer

It s an old question but the solution below  without a for loop  might be helpful  def new fun df       prev value   df iloc 0   quot C quot       def func2 row             non local variable    gt  will use pre value from the new fun function         nonlocal prev value         new value    prev value   row  A     row  B           prev value   row  C           return new value       This line might throw a SettingWithCopyWarning warning     df iloc 1    quot C quot     df iloc 1   apply func2  axis 1      return df  df   new fun df

User · Answer

Although it has been a while since this question was asked  I will post my answer hoping it helps somebody   Disclaimer  I know this solution is not standard  but I think it works well   import pandas as pd import numpy as np  data   np array   10  2  10  10                     10  3  60  100                     np nan    4                    10  22  280  250    T idx   pd date range  20150131   end  20150203   df   pd DataFrame data data  columns list  ABCD    index idx  df                A    B     C    D                                     2015-01-31    10   10    NaN  10  2015-02-01    2    3     NaN  22   2015-02-02    10   60    NaN  280  2015-02-03    10   100   NaN  250  def calculate mul  add       global value     value   value   mul   add     return value  value   df loc  2015-01-31    D   df loc  2015-01-31    C     value df loc  2015-02-01     C     df loc  2015-02-01    apply lambda row  calculate  row   A    B      axis 1  df                A    B     C     D                                     2015-01-31    10   10    10    10  2015-02-01    2    3     23    22   2015-02-02    10   60    290   280  2015-02-03    10   100   3000  250   So basically we use a apply from pandas and the help of a global variable that keeps track of the previous calculated value     Time comparison with a for loop   data   np random random size  1000  4   idx   pd date range  20150131   end  20171026   df   pd DataFrame data data  columns list  ABCD    index idx  df C   np nan  df loc  2015-01-31    C     df loc  2015-01-31    D      timeit for i in df loc  2015-02-01    index date      df loc i   C     df loc  i - pd DateOffset days 1   date     C     df loc i   A     df loc i   B     3 2 s    114 ms per loop  mean    std  dev  of 7 runs  1 loop each   data   np random random size  1000  4   idx   pd date range  20150131   end  20171026   df   pd DataFrame data data  columns list  ABCD    index idx  df C   np nan  def calculate mul  add       global value     value   value   mul   add     return value  value   df loc  2015-01-31    D   df loc  2015-01-31    C     value    timeit df loc  2015-02-01     C     df loc  2015-02-01    apply lambda row  calculate  row   A    B      axis 1    1 82 s    64 4 ms per loop  mean    std  dev  of 7 runs  1 loop each   So 0 57 times faster on average

User · Answer

numba  For recursive calculations which are not vectorisable  numba  which uses JIT-compilation and works with lower level objects  often yields large performance improvements  You need only define a regular for loop and use the decorator  njit or  for older versions   jit nopython True    For a reasonable size dataframe  this gives a  30x performance improvement versus a regular for loop   from numba import jit   jit nopython True  def calculator nb a  b  d       res   np empty d shape      res 0    d 0      for i in range 1  res shape 0            res i    res i-1    a i    b i      return res  df  C     calculator nb  df list  ABD    values T   n   10  5 df   pd concat  df  n  ignore index True     benchmarking on Python 3 6 0  Pandas 0 19 2  NumPy 1 11 3  Numba 0 30 1   calculator   is same as calculator nb   but without  jit decorator  timeit calculator nb  df list  ABD    values T     14 1 ms per loop  timeit calculator  df list  ABD    values T        444 ms per loop

User · Answer

In general  the key to avoiding an explicit loop would be to join  merge  2 instances of the dataframe on rowindex-1  rowindex  Then you would have a big dataframe containing rows of r and r-1  from where you could do a df apply   function  However the overhead of creating the large dataset may offset the benefits of parallel processing

User · Answer

Applying the recursive function on numpy arrays will be faster than the current answer   df   pd DataFrame np repeat np arange 2  6  3  reshape 4 3   columns   A    B    D    new    df D values 0   for i in range 1  len df index        new append new i-1  df A values i  df B values i   df  C     new   Output        A  B  D    C    0  1  1  1    1    1  2  2  2    4    2  3  3  3   15    3  4  4  4   64    4  5  5  5  325

User · Answer

First  create the derived value   df loc 0   C     df loc 0   D     Then iterate through the remaining rows and fill the calculated values    for i in range 1  len df        df loc i   C     df loc i-1   C     df loc i   A     df loc i   B       Index Date   A   B    C    D 0 2015-01-31  10  10   10   10 1 2015-02-01   2   3   23   22 2 2015-02-02  10  60  290  280

[python] Is there a way in Pandas to use previous row value in dataframe.apply when previous value is also calculated in the apply?

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