Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts

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1. What is a Dataset?

• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.

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2. Types of Datasets

• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).

• Unstructured Data: Images, text, audio, video.

• Semi-structured Data: JSON, XML files containing hierarchical data.

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3. Common Dataset Formats

• CSV (Comma-Separated Values)

• Excel (.xls, .xlsx)

• JSON (JavaScript Object Notation)

• XML (eXtensible Markup Language)

• Images (JPEG, PNG, TIFF)

• Audio (WAV, MP3)

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4. Loading Datasets in Python

• Use libraries like pandas for structured data:

import pandas as pd
df = pd.read_csv('data.csv')

• Use libraries like json for JSON files:

import json
with open('data.json') as f:
    data = json.load(f)

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5. Basic Dataset Exploration

• Check shape and size:

print(df.shape)

• Preview data:

print(df.head())

• Check for missing values:

print(df.isnull().sum())

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6. Summary

• Understanding dataset types is crucial before processing.

• Loading and exploring datasets helps identify cleaning and preprocessing needs.

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Exercise

• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.

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