---
id: dataflows-tabulator
version: "1.54.3"
license: MIT
license_treatment: permissive
maintenance: dormant
---

# dataflows-tabulator — Consistent interface for stream reading and writing tabular data (csv/xls/json/etc)

License: permissive · Maintenance: dormant · Downloads: 417.8K/mo

## What it is and what it does

dataflows-tabulator is a library for reading and writing tabular data across multiple formats and sources. It abstracts away the differences between CSV, XLS, ODS, JSON, Google Sheets, SQL, and other formats, letting you work with them through a single Stream interface. The library keeps only the current row in memory, making it suitable for processing large datasets without consuming significant RAM. It supports local files, HTTP, FTP, and S3 sources, and can decompress ZIP and GZIP files automatically.

The package is a maintained fork of the archived tabulator-py project. It provides both a Python API via the Stream class and a command-line tool for quick data inspection. You can iterate over rows as lists or dictionaries, reset the stream pointer, and read entire files into memory when needed. Encoding detection is automatic, though you can specify it explicitly, and the library raises an error if it detects HTML content to prevent common mistakes.

Use it for:

- Load CSV or Excel files from a URL and iterate over rows without downloading the entire file into memory.
- Convert between tabular formats (e.g. CSV to JSON) by reading from one source and writing to another.
- Process large datasets from S3 or remote HTTP sources row-by-row in a data pipeline.
- Read multi-format data sources in a data workflow where format varies but the interface remains consistent.
- Extract and validate tabular data from compressed archives (ZIP, GZIP) without manual decompression.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Reads and writes tabular data in multiple formats (CSV, XLS, JSON, ODS, and others) from local files, HTTP, FTP, and S3 sources with low memory overhead.

Yes, if you need a lightweight, format-agnostic tabular data reader and can tolerate dormant maintenance. The low install friction, permissive license, and broad format support make it practical for data pipelines and ETL tasks. However, be aware that the last release was 873 days ago and no active development is occurring—security patches and bug fixes are unlikely, so audit the dependencies and test thoroughly in your environment before relying on it in production.

## Install

pip install dataflows-tabulator
uv add dataflows-tabulator
poetry add dataflows-tabulator

## Installing dataflows-tabulator

Before you install: Low install friction with a pure-Python wheel and 12 runtime dependencies covering common data formats and transport. Maintenance is dormant—last release was 873 days ago—but the package is a maintained fork of an archived upstream project, so expect no active bug fixes or feature development.

License in practice: MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license and copyright notice in distributions.

Quickstart:

pip install dataflows-tabulator

from tabulator import Stream

with Stream('data.csv', headers=1) as stream:
    for row in stream:
        print(row)

Verify before relying:

- Whether the fork is actively maintained or if dormancy means security issues will go unpatched.
- Whether all 12 runtime dependencies are required or if some are optional for specific formats.
- Current compatibility with modern Python versions beyond those listed in classifiers (3.7 is the newest declared).

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 417.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

read csv xls json tabular data, stream tabular data processing, multi-format data loader, csv excel json parser, remote data source reader, low memory tabular stream, data format converter, data-loading, tabular-formats, streaming

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