Pandas Series

 

What is a Pandas Series?

A Pandas Series is similar to a list or a column in Excel.
It consists of two parts:

1. Values

2. Index (labels)

Example:

import pandas as pd

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

Output:

0    10
1    20
2    30

Here, 0, 1, 2 are the index values.

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How to Create a Pandas Series

1. From a List

s = pd.Series([5, 10, 15])

2. From a Dictionary

s = pd.Series({"a": 10, "b": 20, "c": 30})

3. With Custom Index

s = pd.Series([10, 20, 30], index=["Math", "Science", "English"])

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Operations You Can Perform on a Series

1. Accessing Elements

By Index:

s[0]

By Label (custom index):

s["Math"]

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2. Slicing

s[1:3]

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3. Mathematical Operations

You can apply operations to the whole Series:

s + 10
s * 2
s / 5
s.mean()
s.max()
s.min()
s.sum()

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4. Checking Data Type

s.dtype

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5. Changing Data Type

s.astype(float)

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6. Handling Missing Values

s.isna()
s.fillna(0)
s.dropna()

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7. Filtering

s[s > 10]

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8. Sorting

Sort by values:

s.sort_values()

Sort by index:

s.sort_index()

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9. Applying Functions (Map / Apply)

s.apply(lambda x: x * 2)

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10. Unique Values & Counts

s.unique()
s.value_counts()

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Mini Project: Student Marks Analyzer Using Pandas Series

Objective:

Analyze marks of students using Series operations.

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Step 1: Create a Series of Marks

import pandas as pd

marks = pd.Series(
    [85, 90, 76, 88, 95, 67, 80],
    index=["Amit", "Riya", "Sohan", "Neha", "Karan", "Pooja", "Raj"]
)

print(marks)

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Step 2: Find Highest and Lowest Marks

print("Highest Marks:", marks.max())
print("Lowest Marks:", marks.min())

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Step 3: Find Average Marks

print("Average Marks:", marks.mean())

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Step 4: Filter Students Who Scored Above 85

print(marks[marks > 85])

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Step 5: Add Grace Marks (5 Extra Marks)

new_marks = marks + 5
print(new_marks)

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Step 6: Count How Many Students Scored Each Mark

print(marks.value_counts())

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Output Summary:

• You can see top performers

• Identify weak students

• Add grace marks

• Analyze data in a single line

This small project shows how powerful and simple Pandas Series can be for real-world tasks.

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Conclusion

Pandas Series is a fundamental part of data analysis in Python. It helps you store, manipulate, and analyze one-dimensional data efficiently. Once you master Series, it becomes easier to understand DataFrames and perform advanced data analysis.