{"id":81,"date":"2026-03-01T17:17:48","date_gmt":"2026-03-01T12:17:48","guid":{"rendered":"https:\/\/gigz.pk\/python\/?post_type=lesson&p=81"},"modified":"2026-03-14T07:49:43","modified_gmt":"2026-03-14T02:49:43","slug":"working-with-csv-files","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/python\/lesson\/working-with-csv-files\/","title":{"rendered":"Working with CSV Files"},"content":{"rendered":"\n

A CSV (Comma-Separated Values)<\/strong> file is used to store tabular data such as spreadsheets and databases.
Each line represents a row, and values are separated by commas.<\/p>\n\n\n\n

Python provides a built-in csv<\/code> module to work with CSV files easily.<\/p>\n\n\n\n

IMPORTING CSV MODULE<\/strong><\/h1>\n\n\n\n
import csv<\/pre>\n\n\n\n
READING A CSV FILE<\/strong><\/h1>\n\n\n\n
Example CSV File (data.csv)<\/h3>\n\n\n\n
Name,Age,City
Hira,25,Karachi
Ali,30,Lahore
Sara,22,Islamabad<\/pre>\n\n\n\n
Reading CSV File<\/h3>\n\n\n\n
import csvwith open(\"data.csv\", \"r\") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)<\/pre>\n\n\n\n
Each row is returned as a list.<\/p>\n\n\n\n
SKIPPING HEADER ROW<\/strong><\/h1>\n\n\n\n
import csvwith open(\"data.csv\", \"r\") as file:
    reader = csv.reader(file)
    next(reader)   # Skip header
    for row in reader:
        print(row)<\/pre>\n\n\n\n
USING DICTREADER<\/strong><\/h1>\n\n\n\n
DictReader<\/code> reads CSV data as dictionaries.<\/p>\n\n\n\n
import csvwith open(\"data.csv\", \"r\") as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row[\"Name\"], row[\"Age\"])<\/pre>\n\n\n\n
This makes it easier to access columns by name.<\/p>\n\n\n\n
WRITING TO A CSV FILE<\/strong><\/h1>\n\n\n\n
import csvwith open(\"new_data.csv\", \"w\", newline=\"\") as file:
    writer = csv.writer(file)
    writer.writerow([\"Name\", \"Age\", \"City\"])
    writer.writerow([\"Hira\", 25, \"Karachi\"])
    writer.writerow([\"Ali\", 30, \"Lahore\"])<\/pre>\n\n\n\n
newline=\"\"<\/code> prevents extra blank lines on some systems.<\/p>\n\n\n\n
WRITING USING DICTWRITER<\/strong><\/h1>\n\n\n\n
import csvdata = [
    {\"Name\": \"Hira\", \"Age\": 25, \"City\": \"Karachi\"},
    {\"Name\": \"Ali\", \"Age\": 30, \"City\": \"Lahore\"}
]with open(\"dict_data.csv\", \"w\", newline=\"\") as file:
    fieldnames = [\"Name\", \"Age\", \"City\"]
    writer = csv.DictWriter(file, fieldnames=fieldnames)    writer.writeheader()
    writer.writerows(data)<\/pre>\n\n\n\n
APPENDING TO A CSV FILE<\/strong><\/h1>\n\n\n\n
import csvwith open(\"data.csv\", \"a\", newline=\"\") as file:
    writer = csv.writer(file)
    writer.writerow([\"Sara\", 22, \"Islamabad\"])<\/pre>\n\n\n\n
IMPORTANT POINTS<\/strong><\/h1>\n\n\n\n
\u2022 Always use with<\/code> to handle files safely
\u2022 Use newline=\"\"<\/code> when writing CSV files
\u2022 Use DictReader<\/code> and DictWriter<\/code> for better readability
\u2022 CSV files store data in plain text format<\/p>\n\n\n\n
WHY CSV FILES ARE IMPORTANT<\/strong><\/h1>\n\n\n\n
\u2022 Common format for data exchange
\u2022 Used in Excel and databases
\u2022 Essential for data analysis
\u2022 Widely used in business and automation<\/p>\n\n\n\n
Understanding how to work with CSV files<\/strong> is essential for handling structured data in Python applications.<\/p>\n\n\n
Home<\/a><\/span> \u00bb PYTHON INTERMEDIATE (PYI) > File Handling > Working with CSV Files<\/span><\/span><\/div>\n\n\n
<\/strong> 
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