getting the index of a row in a pandas apply function

Question

I am trying to access the index of a row in a function applied across an entire DataFrame in Pandas  I have something like this   df   pandas DataFrame   1 2 3   4 5 6    columns   a   b   c     gt  gt  gt  df    a  b  c 0  1  2  3 1  4  5  6   and I ll define a function that access elements with a given row  def rowFunc row       return row  a     row  b     row  c     I can apply it like so   df  d     df apply rowFunc  axis 1   gt  gt  gt  df    a  b  c   d 0  1  2  3   7 1  4  5  6  34   Awesome  Now what if I want to incorporate the index into my function  The index of any given row in this DataFrame before adding d would be Index  u a   u b   u c   u d    dtype  object    but I want the 0 and 1  So I can t just access row index   I know I could create a temporary column in the table where I store the index  but I m wondering if it is stored in the row object somewhere

User · Answer

To answer the original question  yes  you can access the index value of a row in apply    It is available under the key name and requires that you specify axis 1  because the lambda processes the columns of a row and not the rows of a column    Working example  pandas 0 23 4     gt  gt  gt  import pandas as pd  gt  gt  gt  df   pd DataFrame   1 2 3   4 5 6    columns   a   b   c     gt  gt  gt  df set index  a   inplace True   gt  gt  gt  df    b  c a       1  2  3 4  5  6  gt  gt  gt  df  index x10     df apply lambda row  10 row name  axis 1   gt  gt  gt  df    b  c  index x10 a                  1  2  3         10 4  5  6         40

User · Answer

To access the index in this case you access the name attribute   In  182    df   pd DataFrame   1 2 3   4 5 6    columns   a   b   c    def rowFunc row       return row  a     row  b     row  c    def rowIndex row       return row name df  d     df apply rowFunc  axis 1  df  rowIndex     df apply rowIndex  axis 1  df Out 182      a  b  c   d  rowIndex 0  1  2  3   7         0 1  4  5  6  34         1   Note that if this is really what you are trying to do that the following works and is much faster   In  198    df  d     df  a     df  b     df  c   df Out 198      a  b  c   d 0  1  2  3   7 1  4  5  6  34  In  199     timeit df  a     df  b     df  c    timeit df apply rowIndex  axis 1  10000 loops  best of 3  163   s per loop 1000 loops  best of 3  286   s per loop   EDIT  Looking at this question 3  years later  you could just do   In 15   df  d   df  rowIndex     df  a     df  b     df  c    df index df  Out 15       a  b  c   d  rowIndex 0  1  2  3   7         0 1  4  5  6  34         1   but assuming it isn t as trivial as this  whatever your rowFunc is really doing  you should look to use the vectorised functions  and then use them against the df index   In 16   df  newCol     df  a     df  b     df  c     df index df  Out 16       a  b  c   d  rowIndex  newCol 0  1  2  3   7         0       6 1  4  5  6  34         1      16

User · Answer

Either   1  with row name inside the apply      axis 1  call   df   pandas DataFrame   1 2 3   4 5 6    columns   a   b   c    index   x   y        a  b  c x  1  2  3 y  4  5  6  df apply lambda row  row name  axis 1   x    x y    y   2  with iterrows    slower   DataFrame iterrows   allows you to iterate over rows  and access their index   for idx  row in df iterrows

[python] getting the index of a row in a pandas apply function

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