Tablizer

Convert YAML to Pandas DataFrame Online

Paste a YAML table below, edit it if you need to, and get Pandas DataFrame back. The conversion runs in your browser, so nothing is uploaded, there is no size limit and there is no signup.

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The output will appear here once there is a table to convert.

What YAML is

YAML uses indentation instead of brackets. It is a superset of JSON, supports comments, and is meant to be edited by hand.

Kubernetes manifests, CI pipelines, Ansible playbooks and most modern config files.

What Pandas DataFrame is

Python that builds a pandas DataFrame from a dictionary of columns.

Data analysis and notebooks.

What changes when you convert YAML to Pandas DataFrame

A YAML list of maps reads as rows and columns directly. Anchors and aliases are resolved before the table is built, so a repeated block arrives expanded rather than as a reference.

The import line is included so the snippet runs as is. Turn type inference on and numeric columns arrive as numbers rather than strings, which is usually what you want in a DataFrame.

What carries over from YAML to Pandas DataFrame

One property of the YAML has no home in pandas, and it is worth knowing which before you convert.

YAML can hold a value that is itself a list or an object. pandas has only flat cells, so nested values are flattened into one cell rather than being spread across columns.

pandas marks header cells differently from data cells, so the keys are lifted out and written once as a marked header row rather than repeated on every row.

Both formats record types, so numbers, booleans and nulls survive as themselves rather than as text.

The identifier 007 comes out of the Pandas DataFrame as 007. Reading it as a number would have made it 7, and an id that changes value is worse than one that stays text.

The role "Analyst, data" survives with its comma, in one cell rather than split across two. That is the first thing to check in any converted table, and the usual place a Pandas DataFrame file goes wrong.

The quotes around Jonah "Jo" Pryce are escaped with a backslash in the Pandas DataFrame.

A worked example

Three rows of staff data, with an identifier that has a leading zero, a value containing a comma, a value containing quotes and one blank cell. Those are the four places formats disagree, so they are the four places to look.

YAML in
- id: "007"
  name: Halima Yusuf
  role: Analyst, data
  started: "2024-03-15"
  hours: "38.5"
- id: "012"
  name: Jonah "Jo" Pryce
  role: Engineer
  started: "2025-11-02"
  hours: ""
- id: "104"
  name: Wei Chen
  role: Manager
  started: "2023-06-30"
  hours: "40"
Pandas DataFrame out
import pandas as pd

staff = pd.DataFrame({
    "id": ["007", "012", "104"],
    "name": ["Halima Yusuf", "Jonah \"Jo\" Pryce", "Wei Chen"],
    "role": ["Analyst, data", "Engineer", "Manager"],
    "started": ["2024-03-15", "2025-11-02", "2023-06-30"],
    "hours": ["38.5", "", "40"],
})

Questions

How do I convert YAML to Pandas DataFrame?

Paste your YAML into the box above or drop the file onto it. Check the table in the grid, then copy or download the Pandas DataFrame from the output panel. It takes one step and the data never leaves your browser.

Why did my YAML value change meaning?

YAML reads several bare words as booleans. In the 1.1 spec that includes yes, no, on and off, which is why a country column containing NO for Norway can arrive as false. Quote values you want kept as text.

What happens to nested values?

pandas has no nested cell, so a list or object inside a YAML value is flattened into a single cell rather than expanded into extra columns. Split it before converting if the parts need to be separate.

Is there a limit on file size?

No. The work happens on your own machine, so the limit is your machine's memory rather than an upload cap. A file with tens of thousands of rows converts in a second or two.

Is my data uploaded anywhere?

No. The parsing and generating are done by JavaScript running on this page. You can watch the network tab while you convert and see that nothing is sent.

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