Arithmetical operation on Series using Series functions.

Mathematical Operations on Series We have learnt in Class XI that if we perform basic mathematical operations like addition, subtraction, multiplication, division, etc., on two NumPy arrays, the operation is done on each corresponding pair of elements. Similarly, we can perform mathematical operations on two series in Pandas. While performing mathematical operations on series, index matching is implemented and all missing values are filled in with NaN by default. Consider the following series: seriesA and seriesB for understanding mathematical operations on series in Pandas. 

import pandas as pd

seriesA = pd.Series([1,2,3,4,5], index = ['a', 'b', 'c', 'd', 'e'])

seriesB = pd.Series([10,20,-10,-50,100], index = ['z', 'y', 'a', 'c', 'e'])

print(seriesA + seriesB)

How to Series is added. This table is explained in detail.You can perfomr arithmetic like addition, suctraction, division etc. Wih two seris objects and it will calculate result on two corresponding items of the two objects given in expression BUT it has a caveat-- The operation is performed only on the matching indexes. Example. If first object has indexes a,b,c,d,e and then it will perform arithmetic only with objects having a,b,c,d,e  for all other indexes it will produce NaN.

Second Method : The second method is applied when we do not want to have NaN values in the output. We can use the series method add() and a parameter fill_value to replace missing value with a specified value. That is, calling seriesA.add(seriesB) is equivalent to calling seriesA+seriesB, but add() allows explicit specification of the fill value for any element in seriesA or seriesB

import pandas as pd

seriesA = pd.Series([1,2,3,4,5], index = ['a', 'b', 'c', 'd', 'e'])

seriesB = pd.Series([10,20,-10,-50,100], index = ['z', 'y', 'a', 'c', 'e'])

seriesA.add(seriesB, fill_value=0)

 

Subtraction of two Series Again, it can be done in two different ways, as shown in the following examples: 

import pandas as pd

seriesA = pd.Series([1,2,3,4,5], index = ['a', 'b', 'c', 'd', 'e'])

seriesB = pd.Series([10,20,-10,-50,100], index = ['z', 'y', 'a', 'c', 'e'])

print(seriesA - seriesB)

 

the changes in the series elements and corresponding output without replacing the missing values, and after replacing the missing values with 1000.  

Similarly You can apply mul() function for multiplication and div() function for division. You can apply these methods, also you can multiply and division without these function.

Explicit call to a mathematical operation is preferred when series may have missing values and we want to replace it by a specific value to have a concrete output in place of NaN.

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