Sometimes, it may be required to get the sum of a specific column. This is where the ‘sum’ function can be used.
The column whose sum needs to be computed can be passed as a value to the sum function. The index of the column can also be passed to find the sum.
Let us see a demonstration of the same −
Example
import pandas as pd
my_data = {'Name':pd.Series(['Tom','Jane','Vin','Eve','Will']),'Age':pd.Series([45, 67, 89, 12, 23]),'value':pd.Series([8.79,23.24,31.98,78.56,90.20])
}
print("The dataframe is :")
my_df = pd.DataFrame(my_data)
print(my_df)
print("The sum of 'age' column is :")
print(my_df.sum(1))Output
The dataframe is : Name Age value 0 Tom 45 8.79 1 Jane 67 23.24 2 Vin 89 31.98 3 Eve 12 78.56 4 Will 23 90.20 The sum of 'age' column is : 0 53.79 1 90.24 2 120.98 3 90.56 4 113.20 dtype: float64
Explanation
The required libraries are imported, and given alias names for ease of use.
Dictionary of series consisting of key and value is created, wherein a value is actually a series data structure.
This dictionary is later passed as a parameter to the ‘Dataframe’ function present in the ‘pandas’ library
The dataframe is printed on the console.
We are looking at computing the sum of the ‘Age’ column.
The name of the column whose sum needs to be computed is passed as a parameter to the ‘sum’ function.
The sum is printed on the console.