Operations on Pandas Series

After creating a Series, we can perform various operations such as mathematical calculations, selecting data, indexing, slicing, and viewing records. These operations help analyze and manipulate data efficiently.

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Mathematical Operations on Series

Pandas allows arithmetic operations to be performed directly on all elements of a Series.

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Addition

import pandas as pd

marks = pd.Series([60, 70, 80, 90])

print(marks + 5)

Output

0    65
1    75
2    85
3    95
dtype: int64

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Subtraction

import pandas as pd

marks = pd.Series([60,70,80,90])

print(marks - 10)

Output

0    50
1    60
2    70
3    80
dtype: int64

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Multiplication

import pandas as pd

marks = pd.Series([10,20,30])

print(marks * 2)

Output

0    20
1    40
2    60
dtype:int64

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Division

import pandas as pd

marks = pd.Series([20,40,60])

print(marks / 2)

Output

0    10.0
1    20.0
2    30.0
dtype: float64

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Mathematical Operators Supported

Operator	Operation
+	Addition
-	Subtraction
*	Multiplication
/	Division
//	Floor Division
%	Modulus
**	Power

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head() Function

The head() function displays the first five records of a Series by default.

Syntax

Series.head(n)

• If n is omitted, the first 5 records are displayed.
• If n is specified, the first n records are displayed.

Example

import pandas as pd

s = pd.Series([10,20,30,40,50,60,70])

print(s.head())

Output

0    10
1    20
2    30
3    40
4    50
dtype:int64

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head(3)

print(s.head(3))

Output

0    10
1    20
2    30
dtype:int64

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tail() Function

The tail() function displays the last five records by default.

Syntax

Series.tail(n)

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Example

print(s.tail())

Output

2    30
3    40
4    50
5    60
6    70
dtype:int64

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tail(2)

print(s.tail(2))

Output

5    60
6    70
dtype:int64

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Selecting Elements from Series

Elements can be selected using their index values.

Syntax

Series[index]

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Example

import pandas as pd

marks = pd.Series([85,90,78,95])

print(marks[2])

Output

78

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Accessing Multiple Elements

print(marks[[0,2]])

Output

0    85
2    78
dtype:int64

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Indexing

Every value in a Series has an associated index.

Default Index

0
1
2
3

Custom Index

A
B
C
D

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Example of Custom Index

import pandas as pd

marks = pd.Series([85,90,95],index=["A","B","C"])

print(marks["B"])

Output

90

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Slicing

Slicing extracts a range of values from a Series.

Syntax

Series[start:stop]

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Example

import pandas as pd

marks = pd.Series([60,70,80,90,95])

print(marks[1:4])

Output

1    70
2    80
3    90
dtype:int64

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Slicing with Custom Index

import pandas as pd

marks = pd.Series(
[60,70,80,90],
index=["A","B","C","D"]
)

print(marks["B":"D"])

Output

B    70
C    80
D    90
dtype:int64

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Difference Between Indexing and Slicing

Indexing	Slicing
Returns one element.	Returns multiple elements.
Uses a single index.	Uses a range of indexes.
Example: s[2]	Example: s[1:4]

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Common Errors

Error	Reason
KeyError	Index label does not exist.
IndexError	Index is outside the Series range.
TypeError	Invalid index type used.

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Quick Revision

Function	Purpose
head()	Shows first 5 records.
tail()	Shows last 5 records.
s[2]	Select one element.
s[1:4]	Select multiple elements.
+	Addition
*	Multiplication

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CBSE Exam Tips

• Remember that head() and tail() display 5 records by default.
• Understand the difference between indexing and slicing.
• Practice predicting the output of Series programs.
• Know that arithmetic operations are applied to every element.
• Be comfortable using both default and custom indexes.

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Summary

Pandas Series supports powerful operations such as arithmetic calculations, indexing, slicing, and viewing data with head() and tail(). These features make data analysis simple and efficient and form the foundation for working with DataFrames in Pandas.