How to Perform a SUMIF Function in Pandas?

Last Updated : 23 Jul, 2025

sumif() function is used to perform sum operation by a group of items in the dataframe, It can be applied on single and multiple columns and we can also use this function with groupby function.

Method 1: SUMIF on all columns with groupby()

This function is used to display sum of all columns with respect to grouped column

Syntax: dataframe.groupby('group_column').sum()

where

  • dataframe is the input dataframe
  • group_column is the column in dataframe to be grouped
  • sum() function is to perform the sum operation

Create the student dataframe with 4 columns

Python3 
# import pandas module
import pandas as pd

# create dataframe with 4 columns
data = pd.DataFrame({

    "name": ['sravan', 'jyothika', 'harsha', 
             'ramya', 'sravan', 'jyothika', 
             'harsha', 'ramya', 'sravan', 'jyothika',
             'harsha', 'ramya'],
    "subjects": ['java', 'java', 'java', 'python',
                 'python', 'python', 'html/php', 
                 'html/php', 'html/php', 'php/js',
                 'php/js', 'php/js'],
    "internal marks": [98, 79, 89, 97, 82, 98, 90,
                       87, 78, 89, 93, 94],
    "external marks": [88, 71, 89, 97, 82, 98, 80,
                       87, 71, 89, 92, 64],
})

# display dataframe
print(data)

Output:

Perform sum of all columns by grouping particular column

Python3 
# import pandas module
import pandas as pd

# create dataframe with 4 columns
data = pd.DataFrame({

    "name": ['sravan', 'jyothika', 'harsha', 'ramya',
             'sravan', 'jyothika', 'harsha', 'ramya',
             'sravan', 'jyothika', 'harsha', 'ramya'],
    "subjects": ['java', 'java', 'java', 'python',
                 'python', 'python', 'html/php',
                 'html/php', 'html/php', 'php/js',
                 'php/js', 'php/js'],
    "internal marks": [98, 79, 89, 97, 82, 98, 90,
                       87, 78, 89, 93, 94],
    "external marks": [88, 71, 89, 97, 82, 98, 80,
                       87, 71, 89, 92, 64],
})

# find sum of all columns group by name
print(data.groupby('name').sum())


# find sum of all columns group by subjects
print(data.groupby('subjects').sum())

Output:

Method 2: SUMIF Function on One Column

Here we are performing sumif operation on one particular column by grouping it with one column

Syntax: dataframe.groupby('group_column')['column_name].sum()

where

  • dataframe is the input dataframe
  • group_column is the column in dataframe to be grouped
  • column_name is to get sum of this column with respect to grouped column
  • sum() function is to perform the sum operation
Python3 
# import pandas module
import pandas as pd

# create dataframe with 4 columns
data = pd.DataFrame({

    "name": ['sravan', 'jyothika', 'harsha', 'ramya',
             'sravan', 'jyothika', 'harsha', 'ramya', 
             'sravan', 'jyothika', 'harsha', 'ramya'],
    "subjects": ['java', 'java', 'java', 'python',
                 'python', 'python', 'html/php', 
                 'html/php', 'html/php', 'php/js',
                 'php/js', 'php/js'],
    "internal marks": [98, 79, 89, 97, 82, 98, 90,
                       87, 78, 89, 93, 94],
    "external marks": [88, 71, 89, 97, 82, 98, 80,
                       87, 71, 89, 92, 64],
})

# find sum of  columns group by
# name with internal marks column
print(data.groupby('name')['internal marks'].sum())

print("---------------")

# find sum of  columns group by
# name with external marks column
print(data.groupby('name')['external marks'].sum())

print("---------------")

# find sum of  columns group by
# subjects with internal marks column
print(data.groupby('subjects')['internal marks'].sum())

print("---------------")

# find sum of  columns group by
# subjects with external marks column
print(data.groupby('subjects')['external marks'].sum())

Output:

Method 3: SUMIF Operation on multiple columns 

Here we will use sumif operation on multiple columns.

Syntax: dataframe.groupby('group_column')[['column_names']].sum()

where,

  • dataframe is the input dataframe
  • group_column is the column in dataframe to be grouped
  • column_names are to get sum of these columns with respect to grouped column
  • sum() function is to perform the sum operation
Python3 
# import pandas module
import pandas as pd

# create dataframe with 4 columns
data = pd.DataFrame({

    "name": ['sravan', 'jyothika', 'harsha', 'ramya',
             'sravan', 'jyothika', 'harsha', 'ramya', 
             'sravan', 'jyothika', 'harsha', 'ramya'],
    "subjects": ['java', 'java', 'java', 'python',
                 'python', 'python', 'html/php', 
                 'html/php', 'html/php', 'php/js', 
                 'php/js', 'php/js'],
    "internal marks": [98, 79, 89, 97, 82, 98, 90,
                       87, 78, 89, 93, 94],
    "external marks": [88, 71, 89, 97, 82, 98, 80,
                       87, 71, 89, 92, 64],
})

# find sum of  columns group by name with
# external marks and internal marks column
print(data.groupby('name')[['external marks',
                            'internal marks']].sum())

print("---------------")

# find sum of  columns group by subjects
# with external marks and internal marks column
print(data.groupby('subjects')[['external marks',
                                'internal marks']].sum())

Output:

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