CSVtoSheets

Free CSV to Parquet Converter

Convert CSV to Apache Parquet format. Includes command-line instructions for Python, DuckDB, and Spark.

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Browser Parquet conversion

Parquet requires a WASM library that is still maturing for browser environments. The most reliable approach is using Python or DuckDB with the commands shown below.

Drop your CSV file here to see the conversion commands

or click to browse

CSV → Parquet

Python (pandas + pyarrow)

pip install pandas pyarrow
import pandas as pd

df = pd.read_csv("input.csv")
df.to_parquet("output.parquet", index=False)
print(f"Saved {len(df)} rows to output.parquet")

DuckDB CLI

brew install duckdb
-- DuckDB (also works with the CLI)
COPY (
  SELECT * FROM read_csv_auto('input.csv')
) TO 'output.parquet' (FORMAT PARQUET);

Apache Spark

PySpark
-- Apache Spark SQL
spark.read.csv("input.csv", header=True, inferSchema=True) \
     .write.parquet("output.parquet")

Parquet → CSV

Python (pandas + pyarrow)

pip install pandas pyarrow
import pandas as pd

df = pd.read_parquet("input.parquet")
df.to_csv("output.csv", index=False)
print(f"Saved {len(df)} rows to output.csv")

DuckDB CLI

brew install duckdb
-- DuckDB CLI
COPY (SELECT * FROM 'input.parquet') TO 'output.csv' (HEADER, DELIMITER ',');

How to convert

  1. 1

    Review the code snippets below for your preferred tool (Python, DuckDB, or Spark).

  2. 2

    Install the required package (pandas + pyarrow, DuckDB CLI, or PySpark).

  3. 3

    Replace the filename in the command with your actual CSV file path.

  4. 4

    Run the command to generate your Parquet file.

Features

  • Python (pandas + pyarrow) code snippets
  • DuckDB CLI conversion commands
  • Apache Spark conversion examples
  • Copy-ready code for instant use
  • Covers both CSV-to-Parquet and Parquet-to-CSV
  • No signup or account required
  • Detailed step-by-step instructions
  • Free guide for data engineers and analysts

What is Parquet?

Apache Parquet is a columnar storage format optimized for big data processing. It is widely used with data warehouses (BigQuery, Snowflake, Redshift) and analytics frameworks (Apache Spark, DuckDB). Parquet files are significantly smaller than CSV due to efficient columnar compression and preserve data types.

When to convert?

Convert CSV to Parquet when you need to load data into a data warehouse, improve query performance on large datasets, reduce storage costs in cloud object storage like S3 or GCS, or when working with analytics frameworks that perform better with columnar formats.

Frequently asked questions

Why can't I convert CSV to Parquet in the browser?

Parquet is a complex binary columnar format that requires native libraries. Browser-based WASM implementations exist but are not yet reliable enough for production use.

What tools can I use to convert CSV to Parquet?

Python with pandas and pyarrow is the most common approach. DuckDB and Apache Spark also provide simple one-line conversion commands, shown on this page.

What are the benefits of Parquet over CSV?

Parquet files are significantly smaller (often 10x) due to columnar compression, support column pruning for faster queries, and preserve data types — unlike CSV which stores everything as text.

Does Parquet support all CSV data?

Yes. Parquet can store any data that CSV can, plus it preserves data types (integers, floats, dates, booleans) that CSV loses.

Working with data files on Mac? Open CSV & Excel files in Google Sheets with a double-click.

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