Chapter 082 / 132 · Level 5 · Databases, APIs & integration

Data validation and transformation

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Chapter 82 of 132 · Level 5 · Databases, APIs & integration · Python demonstrations with recorded outputs. Learn through reading, predicting and testing real Python code.

Code + real outputPractice + explanationsOffline Python exercises

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01 · Why this concept matters

External data cannot be trusted merely because it looks like a dictionary. Validation checks shape, type, range and business rules before transformation or storage.

Learning goal: Data validation and transformation. Start by reading what the program does, then inspect its exact output. Where an example illustrates only a prerequisite, study the named advanced feature separately before using it in production.

02 · The important subtopics

Study these connected ideas as you work through the demonstration:

Schema validation with Pydantic
Missing values and malformed records
Normalization and business rules
Error reports and rejected rows

03 · Pinpoint: read the code line by line

Line 1def valid_product(record):Define or import. Notice what values enter this statement and which names it affects.
Line 2return isinstance(record.get('name'), str) and isinstance(record.get('price'), (int, float)) and record['price'] >= 0Control the next step. Notice what values enter this statement and which names it affects.
Line 3print(valid_product({'name': 'Pen', 'price': 1.5}))Display a result. Notice what values enter this statement and which names it affects.
Line 4print(valid_product({'name': 'Pen', 'price': -1}))Display a result. Notice what values enter this statement and which names it affects.

Input → operation → output

Find the literal input values and arguments, follow each operation in execution order, and verify the result below. This habit turns a code listing into a testable explanation.

04 · Runnable Python example and verified output

Data validation and transformation: working demonstration

Python · runnable

PYTHON CODE

def valid_product(record):
    return isinstance(record.get('name'), str) and isinstance(record.get('price'), (int, float)) and record['price'] >= 0
print(valid_product({'name': 'Pen', 'price': 1.5}))
print(valid_product({'name': 'Pen', 'price': -1}))

Verified execution ▼

True
False

Why it works: External data cannot be trusted merely because it looks like a dictionary. Validation checks shape, type, range and business rules before transformation or storage.

Worked example 2 · Data Validation And Transformation in practice

Python · executed and checked

PYTHON CODE

def validate_person(data):
    if not isinstance(data.get('age'), int): raise TypeError('age must be an integer')
    return {'name': data['name'].strip(), 'age': data['age']}
print(validate_person({'name': ' Ana ', 'age': 25}))

Verified execution output

{'name': 'Ana', 'age': 25}

Pinpoint learning: This practical data validation and transformation demonstration uses a small input you can inspect. Run it exactly as shown, compare the displayed result, then change one value and predict how the result will change.

Understand the code line by line

1: def validate_person(data): — Define a reusable function. Its body executes when called.
2: if not isinstance(data.get('age'), int): raise TypeError('age must be an integer') — Choose which indented branch executes based on a condition.
3: return {'name': data['name'].strip(), 'age': data['age']} — Send the computed result back to the caller.
4: print(validate_person({'name': ' Ana ', 'age': 25})) — Display the value so you can compare it with the verified output.

05 · Independent practice and answer guidance

Exercise A · Adapt the code

Add validation that rejects blank product names and quantities that are not whole numbers.

Exercise B · Predict

Before pressing Run, predict the first displayed line. Next, change one literal value and record the new observed output. Explain the difference in one clear sentence.

Exercise C · Debug

Make a temporary invalid change, read the exception or failed assertion if one occurs, restore the working code, and explain the repair.

Worked explanation for the original example

External data cannot be trusted merely because it looks like a dictionary. Validation checks shape, type, range and business rules before transformation or storage.

True
False

06 · Build, verify and advance

Real-world task: Implement an import pipeline for messy customer records.

Mastery checklist

Run the original code; explain the important lines; change one input and verify the new outcome; use a test or recorded result to justify your conclusion.