Types of Data: Structured, Semi-Structured & Unstructured

Understand the role of data types in the data-science workflow

A fraud-detection system may combine structured transaction amounts, semi-structured device events and unstructured support notes. The modeling pipeline should keep the sources separate long enough to validate each one before joining features.

• Structured data fits a defined schema such as rows and columns, while unstructured data includes text, images, audio and other formats without a fixed tabular shape.
• Semi-structured formats such as JSON and event logs carry keys or tags but can vary from record to record.
• Measurement scale matters: nominal, ordinal, interval and ratio variables support different summaries and mathematical operations.
• Before modeling, identify the unit of observation and data type because encoding a category as a number does not make it truly numeric.

Connect the idea to a concrete decision

For Types of Data, predict one visible result before each snippet runs—a row count, shape, statistic, ranking or error pattern—then compare it with the printed checkpoint.

# Types of Data · example 1
record = {
  'structured': {'id': 101, 'revenue': 2499.0},
  'semi_structured': {'event':'purchase','items':[3,8]},
  'unstructured': 'customer review text'
}
for kind, value in record.items():
    print(kind, type(value).__name__)

Types of Data checkpoint 1: Run the cell and inspect the printed values before changing anything.

# Types of Data · example 2
import pandas as pd

df = pd.read_csv('practice.csv')
print(df.shape)
print(df.dtypes)
print(df.head(3).to_string(index=False))

Types of Data checkpoint 2: (30, 5)
Column types are printed, followed by the first three rows.

# Types of Data · example 3
missing = df.isna().sum().sort_values(ascending=False)
print(missing[missing.gt(0)])

Types of Data checkpoint 3: Only columns with at least one missing value are listed.

# Types of Data · example 4
summary = df.select_dtypes('number').agg(['count','mean','median','min','max']).T
print(summary.round(2))

Types of Data checkpoint 4: A compact numeric summary appears with one row per numeric column.

# Types of Data · example 5
test = df.copy()
if len(test):
    test.iloc[0, 0] = test.iloc[0, 0]
print('rows:', len(test), 'duplicates:', test.duplicated().sum())

Types of Data checkpoint 5: The row count and duplicate count make the check visible instead of implicit.

Use a small reproducible example

Self-check with answers

Check your understanding with a changed input