Pandas Series in Python

A Series is the first data structure provided by the Pandas library. It is used to store data in a single column along with labels called index.

You can think of a Series as an enhanced Python list where every value has an index label.

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What is a Series?

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Why Do We Use Series?

• To store one-dimensional data.
• To organize data efficiently.
• To perform mathematical operations.
• To analyze data quickly.
• To access data using labels.

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Characteristics of Series

Property	Description
One Dimensional	Stores data in a single column.
Labeled	Each value has an index label.
Mutable	Values can be changed.
Heterogeneous	Can store different data types (though same type is preferred).
Size	Can store any number of elements.

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Structure of a Series

Index      Value

0          45

1          60

2          75

3          90

The left side contains the Index, while the right side contains the actual Values.

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Creating a Series

The syntax for creating a Series is:

import pandas as pd

pd.Series(data)

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Creating Series from a Python List

import pandas as pd

marks = [85, 90, 78, 92]

s = pd.Series(marks)

print(s)

Output

0    85

1    90

2    78

3    92

dtype: int64

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Creating Series from a Tuple

import pandas as pd

marks = (75, 81, 95, 67)

s = pd.Series(marks)

print(s)

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Creating Series from NumPy ndarray

Before creating a Series from an ndarray, import the NumPy library.

import pandas as pd

import numpy as np

arr = np.array([10,20,30,40])

s = pd.Series(arr)

print(s)

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Creating Series from Dictionary

import pandas as pd

student = {

"Amit":85,

"Neha":92,

"Rohan":78

}

s = pd.Series(student)

print(s)

Output

Amit     85

Neha     92

Rohan    78

dtype:int64

The dictionary keys automatically become the index labels.

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Creating Series from a Scalar Value

import pandas as pd

s = pd.Series(100,index=[0,1,2,3])

print(s)

Output

0    100

1    100

2    100

3    100

dtype:int64

When a scalar value is used, the index must be specified.

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Creating Series with Custom Index

import pandas as pd

marks=[85,90,95]

s=pd.Series(marks,index=["A","B","C"])

print(s)

Output

A    85

B    90

C    95

dtype:int64

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Data Types in Series

Python Data	dtype
10	int64
15.5	float64
"Hello"	object
True	bool

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The dtype Attribute

import pandas as pd

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

print(s.dtype)

Output

int64

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The name Attribute

import pandas as pd

s=pd.Series([25,30,35],name="Age")

print(s)

The name attribute assigns a name to the Series.

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Advantages of Series

• Easy to create.
• Supports labels.
• Fast calculations.
• Handles missing values.
• Useful for data analysis.
• Works efficiently with DataFrames.

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Real-Life Applications

Application	Series Stores
School	Marks of Students
Hospital	Patient Temperature
Bank	Daily Transactions
Weather	Daily Temperature
Sports	Player Scores

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Common Errors

Error	Reason
Index length mismatch	Number of indexes is different from values.
NameError	Pandas library not imported.
ModuleNotFoundError	Pandas package not installed.

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Quick Revision

Concept	Remember
Series	One-dimensional data structure
Index	Label of each value
List	Can create Series
Tuple	Can create Series
Dictionary	Keys become Index
Scalar	Requires Index
Alias	pd

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CBSE Exam Tips

• Remember all five methods of creating a Series.
• Dictionary keys become the index automatically.
• Scalar values require an explicit index.
• Know the difference between List and Dictionary-based Series.
• Be able to identify the output of Series programs.

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Summary

A Series is a one-dimensional labeled data structure in Pandas. It can be created from a list, tuple, NumPy ndarray, dictionary, or scalar value. Each element has an associated index, making data access and manipulation simple and efficient. Series serves as the foundation for working with DataFrames and performing data analysis in Python.