Pandas: Arithmetic and Data Alignment

Arithmetic and Data Alignment

• It is possible to perform arithmetic operations using arithmetic operators. Following Python

code illustrates it ‒

Python program

import pandas as pd

s1 = pd.Series([10,20,30,40],index=['a', 'b','d', 'e'])

s2 = pd.Series([1,2,3,4],index=['b','c', 'd','f'])

print(s1)

print(s2)

print(s1+s2)

Output

Code explanation: In above code,

1) We have created two series with some indexing.

2) Then using arithmetic operator + we try to add these two series.

3) Note that only common index data gets added.

4) If some index is missing in any of the series, then addition is not possible. It simply displays NaN.

We can pass a fill_value argument with value 0 in the add function so that it will remove NaN values for instance ‒

Python program

import pandas as pd

s1 = pd.Series([10,20,30,40],index=['a', 'b', 'd', 'e'])

s2 = pd.Series([1,2,3,4],index=['b','c','d','f'])

print(s1)

print(s2)

print(s1+s2)

print(s1.add(s2,fill_value=0))

Output

Similarly there are methods for other arithmetic operations such as sub, div, mul and pow

Data alignment

When we perform mathematical operations between Panda objects with different indexes, Pandas will perform the data alignment into the resulting Panda object. This operation is known as data alignment.

Following Python code is an illustration of data alignment

Python program

import numpy as np

import pandas as pd

df1 = pd.DataFrame(np.arange(9).reshape(3,3), columns=['a','b','c'], index=['Red', 'Blue', 'Green'])

print(df1)

Output