Things to check before integrating
Before you integrate
Simplify wiki templates, attributes and merged cells so the intended values are explicit.
Before you integrate
Generated columns contain text; review escaping and cast dtypes explicitly instead of assuming a typed DataFrame.
Code examples
These examples send the sample as multipart/form-data and save the returned bytes unchanged. Set TABLECONVERT_API_KEY in your environment.
MediaWiki table to pandas DataFrame code API · cURL
curl --fail-with-body --max-time 40 \
'https://api.tableconvert.com/v2/convert/mediawiki-to-pandasdataframe' \
--header "Authorization: Bearer $TABLECONVERT_API_KEY" \
--form-string 'data={| class="wikitable"
! date !! sales !! region
|-
| 2026-10-30 || 1250 || North
|-
| 2026-10-31 || 1480 || South
|}' \
--output 'mediawiki-to-pandasdataframe.py'Requires cURL. Download the sample input first when this example uploads a file.
MediaWiki table to pandas DataFrame code API · Python
import os
from pathlib import Path
import requests
endpoint = "https://api.tableconvert.com/v2/convert/mediawiki-to-pandasdataframe"
headers = {"Authorization": "Bearer " + os.environ["TABLECONVERT_API_KEY"]}
fields = {
"data": (None, "{| class=\"wikitable\"\n! date !! sales !! region\n|-\n| 2026-10-30 || 1250 || North\n|-\n| 2026-10-31 || 1480 || South\n|}"),
}
response = requests.post(endpoint, headers=headers, files=fields, timeout=40)
response.raise_for_status()
Path("mediawiki-to-pandasdataframe.py").write_bytes(response.content)Requires Python 3 and requests. Install it with pip install requests.
MediaWiki table to pandas DataFrame code API · Node.js
import { writeFile } from "node:fs/promises";
const form = new FormData();
form.set("data", "{| class=\"wikitable\"\n! date !! sales !! region\n|-\n| 2026-10-30 || 1250 || North\n|-\n| 2026-10-31 || 1480 || South\n|}");
const response = await fetch("https://api.tableconvert.com/v2/convert/mediawiki-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("mediawiki-to-pandasdataframe.py", Buffer.from(await response.arrayBuffer()));Requires Node.js 20 or later with built-in fetch, FormData and Blob.
Authentication
Send Authorization: Bearer YOUR_API_KEY. Let the HTTP client set the multipart boundary; do not set Content-Type manually.
Keep production API keys in server-side environment variables.
Request parameters
MediaWiki table cell syntax is reduced to plain rows for conversion. Wiki page expansion and template execution are outside this importer.
| Parameter | Type | Default | Description |
|---|---|---|---|
data | string | — | Inline MediaWiki table content. Choose one of data, file or url. |
file | binary | — | Upload a wiki file using multipart/form-data. |
url | string | — | A supported publicly accessible HTTP or HTTPS file URL. |
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
| Case | Behavior |
|---|---|
| Mapping rule 1 | MediaWiki cell separators yield table rows while presentation attributes are discarded where recognized. |
| Mapping rule 2 | Header names become dictionary keys and data rows are transposed into column lists. |
| Mapping rule 3 | Replace wiki links, templates and display units with plain values before creating analytical columns. |
How the input is read
- Supply a {| … |} table with clear cell and row boundaries.
- Simplify table attributes and merged cells before conversion; wiki layout metadata is not a portable data model.
- Wiki links, templates and inline markup are not expanded into rendered page content.
- Confirm the extracted header and cell text before creating named records or code.
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
This conversion in practice
Use a wiki-maintained data table as the starting dataset for a Python notebook or analysis-test fixture. Simplify wiki templates, attributes and merged cells so the intended values are explicit.
pandas DataFrame code
Prepare a small reproducible input fixture for a Python analysis notebook.
Responses and errors
| HTTP status | Description |
|---|---|
200 | text/plain; charset=utf-8 |
400 | Invalid input or conversion parameters. |
402 | Insufficient credits for this request. |
403 | Missing or invalid API key. |
413 | The input exceeds the configured request limit. |
415 | Unsupported input media type. |
429 | Rate limit exceeded. |
500 | The conversion could not be completed. |
503 | The renderer is unavailable or the request timed out. |
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.
View API pricing and creditsUsing API v1?
This page documents v2, which returns the output directly. API v1 wraps the result in a JSON envelope. Check the overview before changing an existing integration.
API overviewReferences and format specifications
Format references explain the underlying syntax. The conversion rules and samples above describe TableConvert’s implementation.