90. Working with CSV Files

The csv module in Python allows you to easily read and write CSV files. Below are 10 Python code snippets demonstrating common tasks when working with CSV files.

1. Reading a CSV File

import csv

# Open the CSV file for reading
with open('data.csv', mode='r') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

This snippet opens a CSV file and prints each row. The csv.reader is used to read the contents of the file.

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2. Reading CSV File with Headers

import csv

# Open the CSV file for reading with headers
with open('data.csv', mode='r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row)

DictReader reads the CSV into dictionaries using the first row as the keys for the fields, making it easier to access by column name.

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3. Writing Data to a CSV File

import csv

# Data to write to the CSV file
data = [["name", "age", "city"], ["Alice", 25, "New York"], ["Bob", 30, "Los Angeles"]]

# Open the CSV file for writing
with open('output.csv', mode='w', newline='') as file:
    writer = csv.writer(file)
    writer.writerows(data)

This snippet writes a list of lists to a CSV file. writerows writes multiple rows at once.

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4. Writing Data with Headers

import csv

# Data with headers to write to the CSV file
data = [{"name": "Alice", "age": 25, "city": "New York"}, 
        {"name": "Bob", "age": 30, "city": "Los Angeles"}]

# Open the CSV file for writing with headers
with open('output.csv', mode='w', newline='') as file:
    fieldnames = ['name', 'age', 'city']
    writer = csv.DictWriter(file, fieldnames=fieldnames)
    writer.writeheader()  # Write the header
    writer.writerows(data)  # Write the rows

DictWriter is used for writing dictionaries into the CSV file, and writeheader() writes the column names.

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5. Reading CSV File Line by Line

import csv

# Open the CSV file for reading
with open('data.csv', mode='r') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)  # Process each row here

This reads and processes each row of the CSV file one at a time, making it memory-efficient for large files.

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6. Skipping Header Row in CSV

import csv

# Open the CSV file for reading
with open('data.csv', mode='r') as file:
    reader = csv.reader(file)
    next(reader)  # Skip the header row
    for row in reader:
        print(row)

The next(reader) skips the first row, which is usually the header row.

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7. Handling Comma-Separated Values with Different Delimiters

import csv

# Open the CSV file for reading with a custom delimiter
with open('data.csv', mode='r') as file:
    reader = csv.reader(file, delimiter=';')  # Custom delimiter
    for row in reader:
        print(row)

If the CSV uses a delimiter other than a comma (like semicolon ;), you can specify it using the delimiter parameter.

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8. Writing CSV Data with Custom Delimiter

import csv

# Data to write to the CSV file
data = [["name", "age", "city"], ["Alice", 25, "New York"], ["Bob", 30, "Los Angeles"]]

# Open the CSV file for writing with custom delimiter
with open('output.csv', mode='w', newline='') as file:
    writer = csv.writer(file, delimiter=';')  # Custom delimiter
    writer.writerows(data)

You can use a custom delimiter, such as a semicolon ;, when writing to a CSV file.

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9. Reading CSV with Encoding

import csv

# Open the CSV file for reading with specified encoding
with open('data.csv', mode='r', encoding='utf-8') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

When reading files with non-ASCII characters, you may need to specify an encoding like utf-8 to avoid encoding errors.

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10. Handling Empty Rows in CSV Files

import csv

# Open the CSV file for reading
with open('data.csv', mode='r') as file:
    reader = csv.reader(file)
    for row in reader:
        if row:  # Skip empty rows
            print(row)

This ensures that empty rows are skipped by checking if the row is not empty before processing it.

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These 10 code snippets show how to effectively work with CSV files in Python using the csv module for both reading and writing structured data. Whether dealing with headers, custom delimiters, or large datasets, these techniques make it easy to handle CSV files.