# 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' ],

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 ]}

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 ( )