Apply function to every row in Pandas DataFrame

You can use the apply () function to apply the function to each line in a given data frame. Let`s see how we can accomplish this task.

Example # 1:






  # Import pandas package 
  import   pandas as pd 
  
  # Function to add  
  def   add (a, b, c): 
   return   a   +   b   +   c 
  
  def   main (): 
   
   # create with c ++  
   # three fields each  
   data   =   { 

   `A`  : [  1  ,   2  ,   3  ], 
   `B`  : [  4  ,   5  ,   6  ], 
   `C`   : [  7  ,   8  ,   9  ]} 
   
    # Convert dictionary to DataFrame  
   df   =   pd .DataFrame (data) 
   print   (  "Original DataFrame:"  , df) 
  
    df [ ` add`  ]   =   df.   apply   (  lambda   row: add (row [  `A`  ], 

   row [  `B`  ], row [ ` C`  ]), axis   =   1  )  

  
   print   (  `After Applying Function:`  )   
   # print new data frame  
    print   (df) 
  
  if   __ name__   =   =   `__mai n__`  : 
   main ()

Output:

Example # 2:

You can also use numpy function as parameters for data frame.

import pandas as pd

import numpy as np

def main ():

# create a dictionary with

# five fields each

data = {

` A ` : [ 1 , 2 , 3 ],

`B` : [ 4 , 5 , 6 ],

`C` : [ 7 , 8 , 9 ]}


  
   # Convert dictionary to DataFrame  
   df   =   pd.DataFrame (data) 
   print   (  "Original DataFrame:"  , df)  

  
   # applying the function to each row in the dataframe  
   # and saving the result in a new column  

   df [  `add`  ]   =   df.   apply   (np.   sum  , axis   =   1  ) 
  
   print   (  `After Applying Function:`  ) 
   # print new data frame  
   print   (df) 
  
  if   __ name__   =   =   `__main__`  : 
    main ( )

Output:

Example # 3: data normalization






  # Pandas package import  
  import   pandas as pd 
  
  def   normalize (x, y): 
    x_new   =   ((x   -   np.mean ([ x, y]))   /  
   (  max   (x, y)   -   min   (x, y))) 
  
   # print (x_new)  
   return   x_new 
  
  def   main (): 
  
   # create a dictionary with three fields each  
   data   =   {
   `X`  : [  1  ,   2  ,   3  ], 

   `Y`  : [  45  ,   65  ,   89  ]} 
  
   # Convert dictionary to DataFrame  
   df   =   pd.DataFrame (data) 
   print   (  "Original DataFrame:"  , df) 
  
   df [  `X`  ]   =   df.   apply   (  lambda   row: normalize (row [  `X`  ], 
   row [  `Y`  ]), axis   =   1  ) 
  
   print   ( ` Normalized: ` ) 
   print   (df) 
   
  if   __ name__   =   =   `__main__`  : 
   main () 




  
  Output:  
  
  Example # 4:  Create Range 





  import   pandas as pd  
  import   numpy as np 
   
  pd.options.mode.chained_assignment   =   None  
   
  # Function to generate a range   
  def   gene rate_range (n): 
  
   # print a range, for example:  
   # in 67 out 60 -70  
   n   =   int   (n) 
  
   lower_limit   =   n   /   /   10   *   10  
   upper_limit   =   lower_limit   +   10   
  
   return   str   (  str   (lower_limit )   +   `-`   +   str   (upper_limit)) 

  
  def   replace (row): 
   for   i, item   in   enumerate   (row): 
  
   # update string value  
   r ow [i]   =   generate_range (item) 
   return   row 
   
  
  def   main (): 
   # create a dictionary with  
   # three fields each  
    data   =   {

   `A`  : [  0  ,   2  ,   3  ], 
   ` B`  : [  4  ,   15  ,   6  ], 
   `C`  : [  47  ,   8  ,   19  ]}  

  
   # Convert dictionary to DataFrame  

   df   =   pd.DataFrame (data) 
  
    print   (  `Before applying function:`  ) 
   print   (df) 
   

  # apply function to each line in  
   # dataframe and saving the result in a new column  
   df   =   df.   apply   (  lambda   row: replace (row)) 
  
  
   print   (  `After Applying Function:`  ) 
    # print new data frame  
   print   (df) 
  
  if   __ name__   =   =   `__main__`  : 
    main ( )

Output:

Apply function to every row in Pandas DataFrame

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