Tablizer

Convert JSON to Pandas DataFrame Online

Paste a JSON 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 JSON is

JSON is a text format for nested data: objects with named keys, arrays, strings, numbers, booleans and null. It has no date type and no comments.

Every web API, most configuration files and anything that talks to JavaScript.

What Pandas DataFrame is

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

Data analysis and notebooks.

What changes when you convert JSON to Pandas DataFrame

Four shapes all read correctly: an array of objects, an array of arrays with a header row, an object of column arrays, and an object keyed by row id. A single wrapper key holding the array, which is how most APIs reply, is unwrapped automatically. Nested objects are flattened one level into dotted keys; anything deeper is kept as JSON text in the cell, because a table cannot hold a tree.

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 JSON to Pandas DataFrame

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

JSON 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.

JSON 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 JSON to Pandas DataFrame?

Paste your JSON 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 zip code lose its leading zero?

Type inference turned the text 07030 into the number 7030. Tablizer leaves inference off by default for exactly this reason, and refuses to convert any value starting with a zero followed by a digit even when it is on.

What happens to nested values?

pandas has no nested cell, so a list or object inside a JSON 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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