Pandas: Get sum of column values in a Dataframe

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How to get the sum of column values in a dataframe in Python ?

In this article, we will discuss about how to get the sum To find the sum of values in a dataframe. So, let’s start exploring the topic.

Select the column by name and get the sum of all values in that column :

To find the sum of values of a single column we have to use the sum( ) or the loc[ ] function.

Using sum() :

Here by using sum( ) only, we selected a column from a dataframe by the column name and from that we can get the sum of values in that column.

Syntax- dataFrame_Object[‘column_name’].sum( )
#Program :

import numpy as np
import pandas as pd
# Example data
students = [('Jill',    16,     'Tokyo',  150),
('Rachel',    38,     'Texas',   177),
('Kirti',    39,     'New York',  97),
('Veena',   40,     'Texas',   np.NaN),
('Lucifer',   np.NaN, 'Texas',   130),
('Pablo', 30,     'New York',  155),
('Lionel',   45,     'Colombia', 121) ]
dfObj = pd.DataFrame(students, columns=['Name','Age','City','Score'])
#Sum of all values in the 'Score' column of the dataframe
totalSum = dfObj['Score'].sum()
print(totalSum)
Output :
830.0

Using loc[ ] :

Here by using loc[] and  sum( ) only, we selected a column from a dataframe by the column name and from that we can get the sum of values in that column.

Syntax- dataFrame_Object_name.loc[:, ‘column_name’].sum( )

So, let’s see the implementation of it by taking an example.

#Program :

import numpy as np
import pandas as pd
# Example data
students = [('Jill',    16,     'Tokyo',  150),
('Rachel',    38,     'Texas',   177),
('Kirti',    39,     'New York',  97),
('Veena',   40,     'Texas',   np.NaN),
('Lucifer',   np.NaN, 'Texas',   130),
('Pablo', 30,     'New York',  155),
('Lionel',   45,     'Colombia', 121) ]
dfObj = pd.DataFrame(students, columns=['Name','Age','City','Score'])
#Sum of all values in the 'Score' column of the dataframe using loc[ ]
totalSum = dfObj.loc[:, 'Score'].sum()
print(totalSum)
Output :
830.0

Select the column by position and get the sum of all values in that column :

In case we don’t know about the column name but we know its position, we can find the sum of all value in that column using both iloc[ ] and sum( ). The iloc[ ] returns a series of values which is then passed into the sum( ) function.

So, let’s see the implementation of it by taking an example.

#Program :

import numpy as np
import pandas as pd

# Example data
students = [('Jill',    16,     'Tokyo',  150),
('Rachel',    38,     'Texas',   177),
('Kirti',    39,     'New York',  97),
('Veena',   40,     'Texas',   np.NaN),
('Lucifer',   np.NaN, 'Texas',   130),
('Pablo', 30,     'New York',  155),
('Lionel',   45,     'Colombia', 121) ]
dfObj = pd.DataFrame(students, columns=['Name','Age','City','Score'])
column_number = 4
# Total sum of values in 4th column i.e. ‘Score’
totalSum = dfObj.iloc[:, column_number-1:column_number].sum()
print(totalSum)
Output :
Score    830.0
dtype: float64

Find the sum of columns values for selected rows only in Dataframe :

If we need the sum of values from a column’s specific entries we can-

So, let’s see the implementation of it by taking an example.

#Program :

import numpy as np
import pandas as pd

# Example data
students = [('Jill',    16,     'Tokyo',  150),
('Rachel',    38,     'Texas',   177),
('Kirti',    39,     'New York',  97),
('Veena',   40,     'Texas',   np.NaN),
('Lucifer',   np.NaN, 'Texas',   130),
('Pablo', 30,     'New York',  155),
('Lionel',   45,     'Colombia', 121) ]
dfObj = pd.DataFrame(students, columns=['Name','Age','City','Score'])
column_number = 4
entries = 3
#Sum of the first three values from the 4th column
totalSum = dfObj.iloc[0:entries, column_number-1:column_number].sum()
print(totalSum)
Output :
Score    424.0
dtype: float64

Find the sum of column values in a dataframe based on condition :

In case we want the sum of all values that follows our conditions, for example scores of a particular city like New York can be found out by –

So, let’s see the implementation of it by taking an example.

#Program :

import numpy as np
import pandas as pd

# Example data
students = [('Jill',    16,     'Tokyo',  150),
('Rachel',    38,     'Texas',   177),
('Kirti',    39,     'New York',  97),
('Veena',   40,     'Texas',   np.NaN),
('Lucifer',   np.NaN, 'Texas',   130),
('Pablo', 30,     'New York',  155),
('Lionel',   45,     'Colombia', 121) ]
dfObj = pd.DataFrame(students, columns=['Name','Age','City','Score'])
#Sum of all the scores from New York city
totalSum = dfObj.loc[dfObj['City'] == 'New York', 'Score'].sum()
print(totalSum)
Output :
252.0