Structured Data

What is Structured Data? #

Structured Data is data that is organized in a fixed format, usually in rows and columns, making it easy to store, search, and analyze.

Importance of Structured Data #

• Easy to store and manage
• Fast query and retrieval
• Suitable for analysis and reporting
• Used in most business applications

Examples of Structured Data #

• Excel spreadsheets
• SQL databases (tables)
• CSV files

Example:

Characteristics of Structured Data #

• Organized in rows and columns
• Fixed schema (predefined structure)
• Easily searchable using queries (SQL)
• Stored in relational databases

Common Tools #

• SQL (MySQL, PostgreSQL)
• Excel / Google Sheets
• Python (Pandas)

Basic Python Example #

Step 1: Create Structured Data #

import pandas as pd

data = {
    "Name": ["Alex", "John", "Sara"],
    "Age": [25, 30, 28],
    "Country": ["USA", "UK", "Canada"]
}

df = pd.DataFrame(data)
print(df)

Step 2: Access Data

# Select a column
print(df["Name"])

# Filter data
print(df[df["Age"] > 26])

Step 3: Save Data

df.to_csv("data.csv", index=False)