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kennethreitz
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New essay on physical modeling synthesis in pure Python — Karplus-Strong strings, tabla strokes, djembe cross-choking, Hammond organ drawbars. Software pages: - Responder: mention this site runs on it, link to colophon - All pages: change pip install to uv add Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
90 lines
3.2 KiB
Markdown
90 lines
3.2 KiB
Markdown
# Tablib: Tabular Datasets for Humans
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Tablib is a format-agnostic tabular dataset library for Python. Create a dataset once, export it to CSV, JSON, Excel, YAML, or a pandas DataFrame. The data stays the same. The format is up to you.
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$ uv add tablib
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## What It Looks Like
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```python
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import tablib
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# Create a dataset.
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data = tablib.Dataset(headers=["Name", "Age", "City"])
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data.append(["Alice", 28, "Portland"])
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data.append(["Bob", 34, "Seattle"])
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data.append(["Charlie", 22, "Austin"])
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# Export to CSV.
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print(data.export("csv"))
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# Name,Age,City
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# Alice,28,Portland
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# Bob,34,Seattle
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# Charlie,22,Austin
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# Export to JSON.
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print(data.export("json"))
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# [{"Name": "Alice", "Age": 28, "City": "Portland"}, ...]
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# Export to Excel.
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with open("people.xlsx", "wb") as f:
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f.write(data.export("xlsx"))
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# Import from CSV.
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data = tablib.Dataset().load(open("people.csv").read())
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# Filter columns dynamically.
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print(data["Name"])
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# ['Alice', 'Bob', 'Charlie']
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# Stack datasets together.
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more_people = tablib.Dataset(headers=["Name", "Age", "City"])
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more_people.append(["Diana", 29, "Denver"])
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combined = data.stack(more_people)
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# Databooks: multiple sheets in one export.
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book = tablib.Databook(sets=[data, more_people])
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with open("report.xlsx", "wb") as f:
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f.write(book.export("xlsx"))
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```
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One dataset, any format. Import from one, export to another. The data doesn't care about file formats, and neither should you.
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## The Story
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Tablib was one of my first open source projects, built in 2010 before the data science ecosystem in Python really took off. It came from a simple frustration: I kept writing the same import/export boilerplate for every project that dealt with tabular data.
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The idea was format agnosticism. Your data is your data. Whether it ends up as a spreadsheet, a JSON file, or a DataFrame shouldn't change how you work with it. That principle — that software should adapt to your workflow rather than the other way around — became central to everything I built after.
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Tablib powers the export functionality in [Records](/software/records), and its approach to clean API design directly influenced [Requests](/software/requests). Looking back, it was the first place I figured out what "for humans" actually meant in practice: a single object that does the obvious thing, with sane defaults and no ceremony.
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Now maintained by the [Jazzband](https://jazzband.co/) community, which is a model for sustainable open source stewardship.
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## Install
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```bash
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$ uv add tablib
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```
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For specific format support:
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```bash
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$ uv add tablib[xlsx] # Excel support
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$ uv add tablib[yaml] # YAML support
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$ uv add tablib[pandas] # DataFrame support
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$ uv add tablib[all] # Everything
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```
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## Resources
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- [Documentation](https://tablib.readthedocs.io/)
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- [Source Code on GitHub](https://github.com/jazzband/tablib)
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- [Python Package Index](https://pypi.org/project/tablib/)
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## Related
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- [**Records**](/software/records) — SQL for Humans, powered by Tablib's export engine.
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- [**The Lego Bricks Era**](/essays/2026-03-18-values_i_outgrew_and_the_ones_that_stayed) — The era when these libraries came together.
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- [**From HTTP to Consciousness**](/essays/2025-08-27-from_http_to_consciousness) — How designing for humans became a worldview.
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