Spreadsheet formats & CSV

How To Convert CSV To Parquet Using Python?

Read CSV into a DataFrame with explicit types and write Parquet using a supported engine such as pyarrow.

By Uddhav Gopani, FounderPublished 27 September 2026Updated 29 September 2026

How To Convert CSV To Parquet Using Python?

Read CSV into a DataFrame with explicit types and write Parquet using a supported engine such as pyarrow. Parquet retains column types and columnar storage; it is not CSV with a new filename extension.

Set up the conversion

Install pandas and pyarrow. Declare an identifier column as a string and a quantity column as a nullable integer. Keep invalid numeric values as errors until the data owner decides how to handle them. The example stores the table without a DataFrame index and reads it back to verify the conversion.

A reproducible conversion example

import pandas as pd

df = pd.read_csv("orders.csv",
                 dtype={"id": "string", "quantity": "Int64"},
                 encoding="utf-8", keep_default_na=False, na_values=[""])
df.to_parquet("orders.parquet", engine="pyarrow", index=False,
              compression="snappy")
check = pd.read_parquet("orders.parquet", engine="pyarrow")
pd.testing.assert_frame_equal(df, check)

The code is an illustrative conversion core. Run it in an isolated working folder with the named test files, install only the dependencies used by the example, and handle its reported errors before applying it to a larger dataset. It does not contact a remote conversion service.

Check the expected result

Test an id of 00123, a positive quantity, a blank quantity and a non-ASCII name. Verify both values and dtypes after reading the Parquet file. Count rows before and after. For dates, parse a named column using the exact intended format and specify how time zones are represented; guessing a locale-sensitive date such as 03/04/2026 can produce a plausible but wrong value.

Format-specific limits

CSV has no embedded schema, so choosing types is part of the conversion. Compression can vary by reader support; confirm the chosen codec with the receiving system. Very large files should be read in chunks and written through an explicit Parquet writer or dataset plan. Repeatedly overwriting the same filename inside a chunk loop retains only the last chunk.

Verify the file contract before using the result

Write down the delimiter, encoding, header names, field order and treatment of empty values. Keep the original file unchanged and write the converted output to a different path. Conversion is successful when a parser for the destination format recovers the intended values; a familiar filename extension or a successful save message is not enough.

Start with a small fixture that contains the cases your real data can contain. Include a leading-zero identifier, a quoted comma, an embedded quotation mark, a blank field and a non-ASCII character. Add a multiline field only if both formats and the receiving system support it. Verify the fixture first, then run a bounded sample from the real input. Record parsed row counts and check the first and last records so truncation or an accidental duplicate header is visible.

Keep identifiers separate from quantities. An account-like or product identifier may contain digits while still being text; converting it to a floating-point number can remove leading zeros or precision. Dates also need an agreed format. When a conversion produces an unexpected blank, investigate whether it came from an empty source cell, a missing field, an invalid value or an automatic missing-value rule. Those conditions can require different downstream handling.

Do not resolve malformed records by silently skipping them unless that behavior is an explicit requirement and the skipped records are reported. Use a failure message that identifies the record or field, retain enough context to correct the input, and avoid exposing sensitive contents in public logs. For production automation, write to a temporary destination and promote it only after validation so a failed run cannot replace a previously good export.

Where the browser tool fits

Convert Text to CSV helps with already extracted plain text. It does not replace a workbook reader, a JSON parser, an XML schema validator or a Parquet engine. Use it only when its input structure and delimiter choices match your actual data. For structured source files, perform the format-aware conversion above and check the result with an independent reader.

Primary reference

Official format and API documentation