Python CSV: Read and Write CSV files

1. What is a CSV File

A CSV (Comma-Separated Values) file stores tabular data in plain text, where each line represents a row and values are separated by commas.

Example:

name,age,city
Alice,25,New York
Bob,30,London

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2. Reading a CSV File Using csv.reader

import csv

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

Reads each row as a list of values.

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3. Skipping Header Row

import csv

with open("data.csv", "r") as file:
    reader = csv.reader(file)
    next(reader)  # Skip header
    for row in reader:
        print(row)

Useful when the first row contains column names.

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4. Reading CSV as Dictionary (csv.DictReader)

import csv

with open("data.csv", "r") as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row["name"], row["age"])

Each row is read as an ordered dictionary mapped to column headers.

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5. Writing to a CSV File Using csv.writer

import csv

with open("output.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["name", "age", "city"])
    writer.writerow(["Alice", 25, "New York"])
    writer.writerow(["Bob", 30, "London"])

Creates and writes rows to a CSV file.

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6. Writing Multiple Rows at Once

import csv

data = [
    ["John", 28, "Toronto"],
    ["Emma", 35, "Berlin"]
]

with open("people.csv", "w", newline="") as file:
    writer = csv.writer(file)
    writer.writerows(data)

Efficient for bulk data writing.

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7. Writing Dictionaries Using csv.DictWriter

import csv

fieldnames = ["name", "age", "city"]

with open("users.csv", "w", newline="") as file:
    writer = csv.DictWriter(file, fieldnames=fieldnames)
    writer.writeheader()
    
    writer.writerow({"name": "Alice", "age": 25, "city": "Paris"})
    writer.writerow({"name": "Bob", "age": 30, "city": "Rome"})

Ensures structured column mapping.

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8. Custom Delimiters and Quoting

import csv

with open("custom.csv", "w", newline="") as file:
    writer = csv.writer(file, delimiter="|")
    writer.writerow(["Name", "Age", "Country"])
    writer.writerow(["Alice", 25, "India"])

CSV supports configurable delimiters for complex data formats.

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9. Appending Data to an Existing CSV

import csv

with open("users.csv", "a", newline="") as file:
    writer = csv.writer(file)
    writer.writerow(["Charlie", 40, "USA"])

"a" mode appends data without overwriting existing rows.

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10. Reading CSV Using pandas (Advanced)

import pandas as pd

df = pd.read_csv("data.csv")
print(df.head())

Pandas provides high-level, efficient CSV data manipulation for analytics and ML workflows.

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