apply() to use any functions to all elements of the DataFrame or use it on series

plus2net.com offers FREE online classes on Basics of Python for selected few visitors.
Read more on course content , Details about the Program.

Pandas

Apply any function to element or data series of a DataFrame. By using this we can clean data or apply any generic functionality to the elements.

Options

func: Function to apply
axis : Default is 0, we can use 1 for columns.
raw : Default is false, if row or column is passed.
result_type : Default is None , values are 'expand', 'reduce', 'broadcast', None
args : tuple, Positional arguments to passed

Examples using options

Sum of MATH and ENGLISH using axis=1

import pandas as pd 
import numpy as np
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,82,30],
         'ENGLISH':[81,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data['MATH_NEW']=my_data[['MATH','ENGLISH']].apply(np.sum, axis=1)
print(my_data)

Output

   NAME  ID  MATH  ENGLISH  MATH_NEW
0  Ravi   1    80       81       161
1  Raju   2    40       70       110
2  Alex   3    70       40       110
3   Ron   4    70       50       120
4  King   5    82       60       142
5  Jack   6    30       30        60

Using lambda

We will add 5 mark to MATH column.

my_data['MATH_NEW']=my_data['MATH'].apply(lambda x:x+5)

Output

   NAME  ID  MATH  ENGLISH  MATH_NEW
0  Ravi   1    80       81        85
1  Raju   2    40       70        45
2  Alex   3    70       40        75
3   Ron   4    70       50        75
4  King   5    82       60        87
5  Jack   6    30       30        35

Using function

One new function my_check() is used to add 5 marks to MATH column.

import pandas as pd 
def my_check(a):
   sum=a+5
   return sum
   
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,82,30],
         'ENGLISH':[81,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data['MATH_NEW']=my_data['MATH'].apply(lambda x:my_check(x))
print(my_data)

Use one fucntion to add MATH and ENGLISH

def my_fun(a,b):
   sum=a+b
   return sum
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,82,30],
         'ENGLISH':[81,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data['total']=my_data.apply(lambda x:my_fun(x['MATH'],x['ENGLISH']),axis=1)
print(my_data)

Output

   NAME  ID  MATH  ENGLISH  total
0  Ravi   1    80       81    161
1  Raju   2    40       70    110
2  Alex   3    70       40    110
3   Ron   4    70       50    120
4  King   5    82       60    142
5  Jack   6    30       30     60

Using Lambda ( same function we can use by lambda )

my_data['total']=my_data.apply(lambda x:(x['MATH']+x['ENGLISH']),axis=1)

If sum of MATH and ENGLISH is equal or more than 120 then Pass or Fail.

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,82,30],
         'ENGLISH':[81,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
my_data['total']=my_data.apply(lambda x:(x['MATH']+x['ENGLISH']),axis=1)
my_data['status']=my_data['total'].apply(lambda x: x>=120)
#my_data['status']=my_data['total'].apply(lambda x: x>=120 and 'Pass' or 'Fail' )
print(my_data)

Output

   NAME  ID  MATH  ENGLISH  total  status
0  Ravi   1    80       81    161    True
1  Raju   2    40       70    110   False
2  Alex   3    70       40    110   False
3   Ron   4    70       50    120    True
4  King   5    82       60    142    True
5  Jack   6    30       30     60   False

to display Pass or Fail in place of True or False use this line

my_data['status']=my_data['total'].apply(lambda x: x>=120 and 'Pass' or 'Fail' )

Pandas Pandas DataFrame sort_values groupby cut

Numpy arrys Python & MySQL Python- Tutorials