Apply function to every row in Pandas DataFrame

Michael Zippo 18.07.2021

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:

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Apply function to every row in Pandas DataFrame

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