JSON to pandas DataFrame code API

Code examples

These examples send the sample as multipart/form-data and save the returned bytes unchanged. Set TABLECONVERT_API_KEY in your environment.

curl --fail-with-body --max-time 40 \
  'https://api.tableconvert.com/v2/convert/json-to-pandasdataframe' \
  --header "Authorization: Bearer $TABLECONVERT_API_KEY" \
  --form-string 'data=[
  {
    "date": "2026-10-30",
    "sales": "1250",
    "region": "North"
  },
  {
    "date": "2026-10-31",
    "sales": "1480",
    "region": "South"
  }
]' \
  --output 'json-to-pandasdataframe.py'

import os
from pathlib import Path
import requests

endpoint = "https://api.tableconvert.com/v2/convert/json-to-pandasdataframe"
headers = {"Authorization": "Bearer " + os.environ["TABLECONVERT_API_KEY"]}
fields = {
    "data": (None, "[\n  {\n    \"date\": \"2026-10-30\",\n    \"sales\": \"1250\",\n    \"region\": \"North\"\n  },\n  {\n    \"date\": \"2026-10-31\",\n    \"sales\": \"1480\",\n    \"region\": \"South\"\n  }\n]"),
}

response = requests.post(endpoint, headers=headers, files=fields, timeout=40)
response.raise_for_status()
Path("json-to-pandasdataframe.py").write_bytes(response.content)

import { writeFile } from "node:fs/promises";

const form = new FormData();
form.set("data", "[\n  {\n    \"date\": \"2026-10-30\",\n    \"sales\": \"1250\",\n    \"region\": \"North\"\n  },\n  {\n    \"date\": \"2026-10-31\",\n    \"sales\": \"1480\",\n    \"region\": \"South\"\n  }\n]");

const response = await fetch("https://api.tableconvert.com/v2/convert/json-to-pandasdataframe", {
  method: "POST",
  headers: { Authorization: "Bearer " + process.env.TABLECONVERT_API_KEY },
  body: form,
  signal: AbortSignal.timeout(40_000),
});
if (!response.ok) {
  throw new Error("HTTP " + response.status + ": " + await response.text());
}
await writeFile("json-to-pandasdataframe.py", Buffer.from(await response.arrayBuffer()));

Request parameters

A nonempty array of flat objects is the clearest record input. Object keys form a union of columns; nested structures are not automatically flattened.

Send one source: data, file, or url. The examples below use the source mode appropriate for this format.

Output options

Generate Python source containing a column dictionary and pd.DataFrame construction. The API returns code text, not a live DataFrame or a pandas execution result.

This output format does not expose additional options.

Conversion rules

How the input is read

• For object arrays, columns follow keys in first-seen order; missing keys yield empty cells.
• null becomes the literal text null, while numbers and booleans become text during table normalization.
• A nested object becomes [object Object]; an array inside a field becomes comma-joined text rather than additional rows.
• Prepare explicit flat fields before conversion; the endpoint has no generic flatten parameter.

How the output is written

• Header names become dictionary keys and each column becomes a list of quoted values.
• Numbers and dates are not restored as pandas dtypes automatically; cast them in your analysis code.
• Review Python string escaping before running code containing quotes, slashes or newlines.

When to use this API

Responses and errors

Check the response before retrying. A new HTTP request is a new conversion and may use credits again.

Limits, billing and versions

Local previews accept up to 200 KB. The current API implementation allows one uploaded file up to 50 MiB, limits each multipart text field to 1 MiB, and gives v2 conversion tasks a 30-second deadline. Deployment limits may be stricter. Read the returned error before retrying; some parser or renderer failures return HTTP 500.

Live calls follow the API credit policy. Failed conversions are refunded; a completed conversion is not undone if the download connection later fails.

References and format specifications

Format references explain the underlying syntax. The conversion rules and samples above describe TableConvert’s implementation.

• www.ecma-international.org
• pandas.pydata.org