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 moduleimport pandas as pd # create dataframe with 4 columnsdata = 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 dataframeprint(data) |
Output:
Perform sum of all columns by grouping particular column
Python3
# import pandas moduleimport pandas as pd # create dataframe with 4 columnsdata = 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 nameprint(data.groupby('name').sum()) # find sum of all columns group by subjectsprint(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 moduleimport pandas as pd # create dataframe with 4 columnsdata = 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 columnprint(data.groupby('name')['internal marks'].sum()) print("---------------") # find sum of columns group by# name with external marks columnprint(data.groupby('name')['external marks'].sum()) print("---------------") # find sum of columns group by# subjects with internal marks columnprint(data.groupby('subjects')['internal marks'].sum()) print("---------------") # find sum of columns group by# subjects with external marks columnprint(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 moduleimport pandas as pd # create dataframe with 4 columnsdata = 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 columnprint(data.groupby('name')[['external marks', 'internal marks']].sum()) print("---------------") # find sum of columns group by subjects# with external marks and internal marks columnprint(data.groupby('subjects')[['external marks', 'internal marks']].sum()) |
Output:
