dataflows-tabulator
Consistent interface for stream reading and writing tabular data (csv/xls/json/etc)
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
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 · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license and copyright notice in distributions.
last release 2024-03-24 (873 days) · last repo commit 2024-03-24
0 known vulnerabilities (OSV.dev, 2026-08-14) · 417,773 downloads/mo, #6,808 on PyPI
Alternatives
Verify before relying
pip install dataflows-tabulator
from tabulator import Stream
with Stream('data.csv', headers=1) as stream:
for row in stream:
print(row)- 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).
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 on this page. Verify before relying on it.
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
dataflows-tabulator on PyPI
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 | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 12 packagessixclickrequestschardetboto3unicodecsvijsonjsonlinessqlalchemylinear-tsvxlrdopenpyxl |
| Maintenance | Dormant 873 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 417,773 / month, #6,808 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: Web EnvironmentIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Topic :: Internet :: WWW/HTTP :: Dynamic ContentTopic :: Software Development :: Libraries :: Python Modules |
Evidence: dataflows_tabulator-1.54.3-py2.py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “read csv xls json tabular data”
- dataflows-tabulatorReads and writes tabular data in multiple formats (CSV, XLS, JSON,…
- tabulatorReads and writes tabular data in multiple formats (CSV, XLS, JSON,…
- pyexcelProvides a single API to read, write, and manipulate data across…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also tabulator · csvw · tablib · linear-tsv · frictionless · table-logger · pytablewriter · DrissionRecord · pyexcel-io · datapackage