Unit 5: Exception Handling

Exception Handling in Python

An exception is a runtime error that occurs during program execution and stops the normal flow of the program.

Common Examples

• Division by zero
• File not found
• Invalid input

print(10 / 0)   # ZeroDivisionError

What is Exception Handling?

Exception handling is a mechanism to handle runtime errors gracefully so that the program does not crash suddenly.

Python uses:

• try
• except
• finally

try and except Clause

Syntax

try:
    statement
except:
    statement

Example

try:
    x = int(input("Enter number: "))
    print(10 / x)
except:
    print("Error occurred")

Handling Specific Exceptions

try:
    a = int(input())
    print(10 / a)
except ZeroDivisionError:
    print("Cannot divide by zero")
except ValueError:
    print("Invalid input")

try – finally Clause

The finally block always executes, whether an exception occurs or not.

Syntax

try:
    statement
finally:
    statement

Example

try:
    file = open("data.txt", "r")
    print(file.read())
finally:
    file.close()

try – except – finally (Complete Form)

try:
    print(10 / 0)
except:
    print("Error handled")
finally:
    print("Program ended")

User-Defined Exceptions

User can create custom exceptions using Exception class.

Syntax

class MyError(Exception):
    pass

Example

class AgeError(Exception):
    pass

age = int(input("Enter age: "))
if age < 18:
    raise AgeError("Age must be 18 or above")
else:
    print("Valid age")

Important Exception Keywords 

Keyword	Use
try	Test risky code
except	Handle error
finally	Always execute
raise	Throw exception

Important Exam Points (Exception Handling)

• Exceptions occur at runtime
• except prevents program crash
• finally always executes
• User-defined exceptions improve control
• Multiple except blocks allowed

Basics of Python for Data Analysis

Python is widely used in Data Analysis due to libraries like NumPy, Pandas, Matplotlib.

Why Python for Data Analysis?

• Easy syntax
• Powerful libraries
• Handles large datasets
• Strong community support

Introduction to Pandas Library

Pandas is a Python library used for data manipulation and analysis.

Main Data Structures

• Series
• DataFrame

import pandas as pd

Introduction to Series

A Series is a one-dimensional labeled array.

Example

import pandas as pd
s = pd.Series([10, 20, 30, 40])
print(s)

Series with Custom Index

s = pd.Series([85, 90, 78], index=["Math", "Sci", "Eng"])
print(s)

Series Properties

Property	Meaning
values	Data
index	Labels
dtype	Data type

Introduction to DataFrame

A DataFrame is a two-dimensional table-like structure (rows & columns).

Diagram (Conceptual)

   Name    Age   Marks
0  Amit     20     85
1  Jay      22     90
2  Ravi     21     88

Creating DataFrame

data = {
    "Name": ["Amit", "Jay", "Ravi"],
    "Age": [20, 22, 21],
    "Marks": [85, 90, 88]
}
df = pd.DataFrame(data)
print(df)

Accessing DataFrame Data

print(df["Name"])      # Column
print(df.loc[0])       # Row

Series vs DataFrame

Feature	Series	DataFrame
Dimension	1D	2D
Structure	Single column	Rows & columns
Use	Single data	Tabular data

Important Exam Points (Data Analysis)

• Pandas is core library for data analysis
• Series is 1D, DataFrame is 2D
• DataFrame works like Excel sheet
• Labels make data easy to handle

Frequently Asked Exam Questions

• What is exception handling?
• Explain try-except-finally
• What is user-defined exception?
• Define Series and DataFrame
• Difference between Series and DataFrame