In this short, you’ll master one of Python’s most elegant shortcuts — the lambda function (also known as an anonymous function).
Instead of defining a function with def and multiple lines, you can create it in one line using lambda.
This shortcut is heavily used in data science, analytics, and machine learning — especially with pandas, map(), filter(), and apply() functions.
It’s perfect for quick calculations, inline logic, and one-time use cases where defining a full function isn’t necessary.
🧩 5 Interview Questions & Answers
Q1: What is a lambda function in Python?
A1: It’s an anonymous, one-line function created using the lambda keyword for quick operations.
Q2: Can lambda functions have multiple arguments?
A2: Yes, for example lambda x, y: x + y takes two arguments.
Q3: Why are lambda functions called “anonymous”?
A3: Because they are defined without a name, unlike functions created using def.
Q4: Where are lambda functions used in data science?
A4: Commonly used in pandas for data transformations, e.g., df['col'].apply(lambda x: x*2).
Q5: What are the limitations of lambda functions?
A5: They can only contain one expression and cannot include statements or multiple lines.
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Codes:
🧩 Long Way:
Defining a simple function (Traditional Way)
def add(a, b):
return a + b
print(add(5, 3))
⚡ One-Liner Shortcut:
add = lambda a, b: a + b
print(add(5, 3))
💡 Lambda functions let you define quick, one-time functions in a single line — clean and fast!
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