Python's csv module provides an easy way to read and write CSV files. 1. When reading a CSV file, you can use csv.reader() to read line by line and return each line of data as a string list; if you need to access the data through column names, you can use csv.DictReader() to map each line into a dictionary. 2. When writing to a CSV file, use csv.writer() and call writerow() or writerows() methods to write single or multiple rows of data; if you want to write dictionary data, use csv.DictWriter(), you need to define the column name first and write the header through writeheader(). 3. When dealing with edge cases, the module automatically handles commas and line breaks in quoted fields, but pay attention to using quotes correctly; at the same time, you should pay attention to encoding issues, such as UTF-8-BOM under Windows, and avoiding problems caused by blank lines. It is recommended to always set newline='' when opening the file. 4. The module supports custom delimiters. After registering a custom dialect through csv.register_dialect(), it can easily deal with data files separated by non-standard delimiters (such as tabs).

Working with CSV files in Python is straightforward thanks to the built-in csv module. Whether you're reading from or writing to a CSV file, this module gives you simple tools that handle most common tasks without needing anything else.

Reading CSV Files

If you want to pull data from a CSV file, the csv.reader() function is your go-to option. It reads the file row by row, giving you each line as a list of strings.

Let's say you have a file called data.csv that looks like this:

 Name,Age,Location
Alice,30,New York
Bob, 25, Los Angeles
Charlie, 35, Chicago

Here's how you'd read it:

 import csv

with open('data.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    for row in reader:
        print(row)

This will output:

 ['Name', 'Age', 'Location']
['Alice', '30', 'New York']
['Bob', '25', 'Los Angeles']
['Charlie', '35', 'Chicago']

A few things to note:

• Make sure to use newline='' when opening the file to prevent issues with blank lines on some platforms.
• Each row is returned as a list, so if you need specific values, just index into the list (eg, row[1] for age).

If your CSV has headers and you want to work with named fields, consider using csv.DictReader , which maps each row to a dictionary:

 with open('data.csv', newline='') as csvfile:
    reader = csv.DictReader(csvfile)
    for row in reader:
        print(row['Name'], row['Age'])

Now you're working with key-value pairs instead of indexes — easier to manage if your data has clear column names.

Writing to CSV Files

Writing data to a CSV file is just as easy using csv.writer() . You create a writer object and then pass it rows of data.

Suppose you have a list of lists and want to write them into a new CSV file:

 data = [
    ['Name', 'Age', 'Location'],
    ['Alice', '30', 'New York'],
    ['Bob', '25', 'Los Angeles']
]

with open('output.csv', 'w', newline='') as csvfile:
    writer = csv.writer(csvfile)
    writer.writerows(data)

You'll get a file that looks like this:

 Name,Age,Location
Alice,30,New York
Bob, 25, Los Angeles

Some tips:

• Use 'w' mode to overwrite an existing file or create a new one.
• If you want to append to an existing CSV, use 'a' mode instead.
• The writerow() method writes a single row, while writerows() writes multiple rows at once.

Again, if you want to write dictionaries instead of lists, use csv.DictWriter . Just remember to specify the fieldsnames first:

 fieldnames = ['Name', 'Age', 'Location']

with open('output.csv', 'w', newline='') as csvfile:
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerow({'Name': 'Alice', 'Age': 30, 'Location': 'New York'})

Handling Edge Cases

CSV files sometimes contain commas inside quoted fields, or even line breaks within cells. The csv module handles these cases automatically, but only if you use the module correctly.

For example, if a name is written like "Smith, John" inside a cell, the reader will still treat it as a single value — as long as the quotes are properly used in the file.

Also, be careful with encoding:

• On Windows, especially with Excel-generated CSVs, you might run into UTF-8-BOM issues. In that case, open the file with encoding='utf-8-sig' .
• If you're dealing with non-English characters, make sure to set the correct encoding when reading and writing.

Another thing to watch out for: empty lines. Some CSV readers (like Excel) can misinterpret extra blank lines. To avoid that, always use newline='' when opening files in write mode.

Lastly, don't forget about dialects. The csv module supports custom dialects if your CSV uses non-standard delimiters (like tabs or semicolons). For example:

 csv.register_dialect('mydialect', delimiter='\t', quoting=csv.QUOTE_NONE)

with open('data.tsv', newline='') as f:
    reader = csv.reader(f, dialect='mydialect')

That way, you can adapt the csv module to fit different formats without rewriting your logic.

These basic patterns cover most use cases. Once you're comfortable with them, you can start combining them — like reading from one CSV, processing the data, and writing to another. But even if you stop here, you've got solid tools for handling CSV files in Python.

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