CSV Files — The Most Common Data Format — Free Data Analytics Tutorial

TL;DR: Learn CSV Files — The Most Common Data Format in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on…

CSV Files — The Most Common Data Format in Data Analytics

CSV (Comma-Separated Values) is the most universal data format — virtually every data tool (Excel, Python, R, SQL databases, Power BI) can read and write CSV. If you know how to work with CSVs, you can handle most real-world datasets.

A CSV file looks like a spreadsheet saved as plain text. The first row is usually the header (column names). Each subsequent row is one record (data point). Values are separated by commas (or sometimes semicolons or tabs).

In Python, the csv module (built-in) handles CSV files. Pandas' read_csv() is even more powerful. Always open CSV files with encoding='utf-8' or 'utf-8-sig' for Indian language support.

Real CSV datasets you can download for practice: Indian census data, IPL cricket stats, NIFTY stock data, COVID-19 India data (all freely available on data.gov.in and Kaggle).

# Reading and writing CSV files in Python
import csv

# Reading:
with open('students.csv', 'r', encoding='utf-8') as f:
    reader = csv.DictReader(f)
    for row in reader:
        print(row['name'], row['marks'])

# Writing:
with open('output.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.DictWriter(f, fieldnames=['name', 'marks'])
    writer.writeheader()
    writer.writerow({'name': 'Arjun', 'marks': 85})

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