Pandas Tutorial Pandas Resources
Python Java C++ C C# PHP R SQL DS Algo InterviewQ

Pandas Series - sum() function



The Pandas Series - sum() function returns the sum of the values over the specified axis. The syntax for using this function is mentioned below:

Syntax

Series.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0)

Parameters

axis Optional. Specify {0 or 'index'}. Specify axis for the function to be applied on.
skipna Optional. Specify True to exclude NA/null values when computing the result. Default is True.
level Optional. Specify level (int or str). If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. A str specifies the level name.
numeric_only Optional. Specify True to include only float, int or boolean data. Default: False
min_count Optional. Specify required number of valid values to perform the operation. If the count of non-NA values is less than the min_count, the result will be NA.

Return Value

Returns scalar or Series if a level is specified.

Example: Using sum() on a Series

In the example below, the sum() function is used to get the sum of values of a given series.

import pandas as pd
import numpy as np

idx = pd.MultiIndex.from_arrays([
    ['even', 'even', 'even', 'odd', 'odd', 'odd']],
    names=['DataType'])

x = pd.Series([10, 20, 30, 5, 7, 9], name='Numbers', index=idx)

print("The Series contains:")
print(x)

#Sum of all values in the series
print("\nx.sum() returns:")
print(x.sum())

#Sum of all values within given level
print("\nx.sum(level='DataType') returns:")
print(x.sum(level='DataType'))
print("\nx.sum(level=0) returns:")
print(x.sum(level=0))

The output of the above code will be:

The Series contains:
DataType
even    10
even    20
even    30
odd      5
odd      7
odd      9
Name: Numbers, dtype: int64

x.sum() returns:
81

x.sum(level='DataType') returns:
DataType
even    60
odd     21
Name: Numbers, dtype: int64

x.sum(level=0) returns:
DataType
even    60
odd     21
Name: Numbers, dtype: int64

Example: Using sum() on selected series in a DataFrame

Similarly, the sum() function can be applied on selected series/column of a given DataFrame. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Last Salary": [58, 60, 63, 57],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#sum of values of 'Salary' series
print("\ndf['Salary'].sum() returns:")
print(df["Salary"].sum())

The output of the above code will be:

The DataFrame is:
       Bonus  Last Salary  Salary
John       5           58      60
Marry      3           60      62
Sam        2           63      65
Jo         4           57      59

df['Salary'].sum() returns:
246

❮ Pandas Series - Functions

5