CSV Column Extractor
Extract specific columns from CSV data.
About this tool
A CSV Column Extractor is a straightforward utility for pulling specific columns from CSV (Comma-Separated Values) data. Whether you're working with a spreadsheet export containing dozens of fields but need only a few, or you're processing raw data files with mixed content, this tool lets you select exactly which columns to keep and discards the rest. It's especially valuable when dealing with large datasets where manual selection would be tedious or error-prone.
To use the tool, paste or upload your CSV data, then specify which columns you want to extract—either by column names (if your CSV has headers) or by column numbers. Click the extract button, and the tool instantly generates a new CSV containing only your selected columns in their original order. The result can be downloaded or copied directly, making it easy to prepare data for import into another system, database, or analysis tool.
This tool is invaluable for data analysts, developers, and anyone managing large datasets who need to clean, subset, or reorganize CSV files quickly. It handles various CSV formats and preserves the integrity of your data—column order, values, and quoted fields remain intact. If you frequently work with exports from databases, CRM systems, or analytics platforms, keeping this tool nearby saves significant time and reduces the risk of manual selection errors.
Frequently Asked Questions
Code Implementation
import csv
import io
def extract_columns(csv_text: str, columns: list[str], delimiter: str = ",") -> str:
"""Extract specific columns from CSV text."""
reader = csv.DictReader(io.StringIO(csv_text), delimiter=delimiter)
output = io.StringIO()
writer = csv.DictWriter(output, fieldnames=columns, delimiter=delimiter,
extrasaction="ignore")
writer.writeheader()
for row in reader:
writer.writerow({col: row.get(col, "") for col in columns})
return output.getvalue()
# Example
csv_data = """name,email,age,city
Alice,alice@example.com,30,Seoul
Bob,bob@example.com,25,Tokyo"""
result = extract_columns(csv_data, ["name", "email"])
print(result)
# name,email
# Alice,alice@example.com
# Bob,bob@example.comComments & Feedback
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