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Pandas DataFrame - add() function



The Pandas add() function returns addition of dataframe and other, element-wise. It is equivalent to dataframe + other, but with support to substitute a fill_value for missing data as one of the parameters.

Syntax

DataFrame.add(other, axis='columns', 
              level=None, fill_value=None)

Parameters

other Required. Specify any single or multiple element data structure, or list-like object.
axis Optional. Specify whether to compare by the index (0 or 'index') or columns (1 or 'columns'). For Series input, axis to match Series index on. Default is 'columns'.
level Optional. Specify int or label to broadcast across a level, matching Index values on the passed MultiIndex level. Default is None.
fill_value Optional. Specify value to fill existing missing (NaN) values, and any new element needed for successful DataFrame alignment. If data in both corresponding DataFrame locations is missing the result will be missing. Default is None.

Return Value

Returns the result of the arithmetic operation.

Example: using add() on whole DataFrame

In the example below, a DataFrame df is created. The add() function is used to add a scalar value to the whole DataFrame.

import pandas as pd
import numpy as np

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

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

#adding 3 to all entries of the DataFrame
print("\ndf.add(3) returns:")
print(df.add(3))

The output of the above code will be:

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

df.add(3) returns:
       Bonus  Salary
John       8      63
Marry      6      65
Sam        5      68
Jo         7      62

Example: Adding different value to different column

Different scalar value can be added to different column by providing other argument as a list. Consider the following example:

import pandas as pd
import numpy as np

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

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

#adding 5 to all entries of Bonus column
#adding 10 to all entries of Salary column
print("\ndf.add([5,10]) returns:")
print(df.add([5,10]))

The output of the above code will be:

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

df.add([5,10]) returns:
       Bonus  Salary
John      10      70
Marry      8      72
Sam        7      75
Jo         9      69

Example: using add() on selected columns

Instead of whole DataFrame, the add() function can be applied on selected columns. 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)

#adding 3 to all entries of Salary column
print("\ndf['Salary'].add(3) returns:")
print(df["Salary"].add(3))

#adding 3 to all entries of Salary column
#adding 2 to all entries of Bonus column
print("\ndf[['Salary','Bonus']].add([3,2]) returns:")
print(df[["Salary", "Bonus"]].add([3,2]))

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'].add(3) returns:
John     63
Marry    65
Sam      68
Jo       62
Name: Salary, dtype: int64

df[['Salary','Bonus']].add([3,2]) returns:
       Salary  Bonus
John       63      7
Marry      65      5
Sam        68      4
Jo         62      6

Example: Adding columns in a DataDrame

The add() function can be applied in a DataFrame to get the addition of two series/column element-wise. Consider the following example.

import pandas as pd
import numpy as np

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

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

#adding 'Bonus' and 'Salary' column
df['Total Salary'] = df['Salary'].add(df['Bonus'])

print("\nThe DataFrame is:")
print(df)

The output of the above code will be:

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

The DataFrame is:
       Bonus  Salary  Total Salary
John       5      60            65
Marry      3      62            65
Sam        2      65            67
Jo         4      59            63

❮ Pandas DataFrame - Functions

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