The ChartGPT API has evolved significantly since its inception, transforming from a niche tool into a cornerstone of modern data visualization workflows. By 2026, it has become the go-to solution for developers, analysts, and businesses seeking to automate and enhance their data presentation processes. The API now supports a wide range of chart types, integrates seamlessly with popular data platforms, and offers advanced customization options that cater to both beginners and experts.
In this guide, we'll explore the practical aspects of using the ChartGPT API, including step-by-step implementation, real-world examples, and answers to frequently asked questions. Whether you're looking to generate static charts for reports or dynamic visualizations for web applications, this article will equip you with the knowledge to leverage the ChartGPT API effectively.
Before diving into the ChartGPT API, ensure you have the following:
Authentication is handled via an API key passed in the request headers. Here’s how to set it up in Python:
import requests
API_KEY = "your_api_key_here"
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
# Example request to fetch available chart types
response = requests.get("https://api.chartgpt.com/v1/charts/types", headers=headers)
print(response.json())
For JavaScript (Node.js):
const fetch = require('node-fetch');
const API_KEY = "your_api_key_here";
const headers = {
"Authorization": `Bearer ${API_KEY}`,
"Content-Type": "application/json"
};
// Example request to fetch available chart types
fetch("https://api.chartgpt.com/v1/charts/types", { headers })
.then(response => response.json())
.then(data => console.log(data))
.catch(error => console.error(error));
The ChartGPT API supports over 50 chart types, including:
The API allows deep customization of charts to match your brand or report style:
The ChartGPT API can pull data from multiple sources:
For large-scale visualizations, the API supports:
Start by selecting the chart type that best represents your data. Use the /v1/charts/types endpoint to retrieve a list of available charts:
curl -X GET "https://api.chartgpt.com/v1/charts/types" \
-H "Authorization: Bearer your_api_key_here"
Example response:
{
"chart_types": [
"bar",
"line",
"pie",
"scatter",
"heatmap",
"choropleth",
"candlestick"
]
}
Format your data according to the API's requirements. The ChartGPT API expects data in a structured format, typically as a JSON object with labels and datasets. Here’s an example for a bar chart:
{
"labels": ["January", "February", "March", "April"],
"datasets": [
{
"label": "Sales",
"data": [65, 78, 92, 85],
"backgroundColor": "rgba(54, 162, 235, 0.5)",
"borderColor": "rgba(54, 162, 235, 1)"
}
]
}
Customize the chart by specifying options such as title, axis labels, and color scheme. Here’s a JSON payload for a bar chart configuration:
{
"chart_type": "bar",
"data": {
"labels": ["January", "February", "March", "April"],
"datasets": [
{
"label": "Sales",
"data": [65, 78, 92, 85],
"backgroundColor": "rgba(54, 162, 235, 0.5)",
"borderColor": "rgba(54, 162, 235, 1)"
}
]
},
"options": {
"title": {
"display": true,
"text": "Monthly Sales Report"
},
"scales": {
"yAxes": [{
"ticks": {
"beginAtZero": true
}
}]
}
}
}
Send a POST request to the /v1/charts endpoint with your configuration:
import requests
API_KEY = "your_api_key_here"
url = "https://api.chartgpt.com/v1/charts"
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
payload = {
"chart_type": "bar",
"data": {
"labels": ["January", "February", "March", "April"],
"datasets": [
{
"label": "Sales",
"data": [65, 78, 92, 85],
"backgroundColor": "rgba(54, 162, 235, 0.5)",
"borderColor": "rgba(54, 162, 235, 1)"
}
]
},
"options": {
"title": {
"display": true,
"text": "Monthly Sales Report"
}
}
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())
The API returns a JSON response with a chart_url and chart_id:
{
"chart_id": "ch_5f3a8b2c9d4e5f6a7b8c9d0e",
"chart_url": "https://chartgpt.com/embed/ch_5f3a8b2c9d4e5f6a7b8c9d0e",
"status": "generated",
"thumbnail_url": "https://chartgpt.com/thumbnails/ch_5f3a8b2c9d4e5f6a7b8c9d0e.png"
}
You can embed the chart in your application using the chart_url or download the image from the thumbnail_url.
For more complex visualizations, use the advanced_options parameter to fine-tune your chart:
{
"chart_type": "heatmap",
"data": {
"labels": ["Product A", "Product B", "Product C"],
"datasets": [
{
"label": "Sales Heatmap",
"data": [[12, 19, 3], [5, 2, 8], [9, 15, 5]],
"backgroundColor": "rgba(255, 99, 132, 0.5)"
}
]
},
"advanced_options": {
"color_scheme": "viridis",
"interpolation": "bilinear",
"show_contours": true
}
}
Generate a dashboard with multiple charts using batch processing:
batch_payload = {
"requests": [
{
"chart_type": "line",
"data": {
"labels": ["Q1", "Q2", "Q3", "Q4"],
"datasets": [
{
"label": "Revenue",
"data": [50000, 60000, 75000, 90000],
"borderColor": "rgba(75, 192, 192, 1)"
}
]
}
},
{
"chart_type": "bar",
"data": {
"labels": ["North", "South", "East", "West"],
"datasets": [
{
"label": "Sales by Region",
"data": [20000, 15000, 30000, 25000],
"backgroundColor": "rgba(153, 102, 255, 0.5)"
}
]
}
}
]
}
response = requests.post(
"https://api.chartgpt.com/v1/charts/batch",
headers=headers,
json=batch_payload
)
print(response.json())
Create a choropleth map using geojson data:
geojson_data = {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"properties": {"name": "California", "value": 50},
"geometry": {
"type": "Polygon",
"coordinates": [[[-122, 37], [-121, 37], [-121, 38], [-122, 38], [-122, 37]]]
}
},
{
"type": "Feature",
"properties": {"name": "Texas", "value": 30},
"geometry": {
"type": "Polygon",
"coordinates": [[[-99, 30], [-98, 30], [-98, 31], [-99, 31], [-99, 30]]]
}
}
]
}
payload = {
"chart_type": "choropleth",
"data": {
"geojson": geojson_data,
"metric": "value"
},
"options": {
"title": "Regional Sales Distribution",
"color_scheme": "plasma"
}
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())
Generate a candlestick chart for stock price data:
stock_data = {
"labels": ["2026-01-01", "2026-01-02", "2026-01-03", "2026-01-04"],
"datasets": [
{
"label": "Stock Price",
"data": [
{"open": 100, "high": 110, "low": 95, "close": 105},
{"open": 105, "high": 115, "low": 102, "close": 112},
{"open": 112, "high": 120, "low": 108, "close": 118},
{"open": 118, "high": 125, "low": 115, "close": 122}
]
}
]
}
payload = {
"chart_type": "candlestick",
"data": stock_data,
"options": {
"title": "Stock Price Trends",
"y_axis": "price",
"x_axis": "date"
}
}
response = requests.post(url, headers=headers, json=payload)
print(response.json())
As of 2026, the ChartGPT API enforces the following rate limits:
You can check your current usage and limits in the developer portal.
Yes! The ChartGPT API is designed to complement existing data workflows. You can use Python libraries to preprocess your data before sending it to the API. For example:
import pandas as pd
# Load data from a CSV file
df = pd.read_csv("sales_data.csv")
# Convert DataFrame to JSON
data = {
"labels": df["month"].tolist(),
"datasets": [{
"label": "Sales",
"data": df["revenue"].tolist()
}]
}
# Send to ChartGPT API
response = requests.post(url, headers=headers, json={
"chart_type": "line",
"data": data
})
For datasets exceeding 10MB, use the API's file upload endpoint:
files = {
"file": open("large_dataset.csv", "rb")
}
response = requests.post(
"https://api.chartgpt.com/v1/charts/upload",
headers={"Authorization": f"Bearer {API_KEY}"},
files=files
)
The API will process the file asynchronously and notify you via webhook when the chart is ready.
Yes! Use the /v1/charts/jobs endpoint to create scheduled jobs:
payload = {
"chart_type": "bar",
"data_source": {
"type": "api",
"url": "https://api.yourdata.com/sales",
"polling_interval": "daily"
},
"options": {
"title": "Daily Sales Report"
}
}
response = requests.post(
"https://api.chartgpt.com/v1/charts/jobs",
headers=headers,
json=payload
)
Absolutely. The ChartGPT API provides embeddable iframes or direct image URLs:
<iframe
src="https://chartgpt.com/embed/ch_5f3a8b2c9d4e5f6a7b8c9d0e"
width="600"
height="400"
frameborder="0">
</iframe>
Or use the image URL directly:
<img
src="https://chartgpt.com/thumbnails/ch_5f3a8b2c9d4e5f6a7b8c9d0e.png"
alt="Sales Chart">
Common errors include:
request_id from the response.Use the debug parameter to get detailed error messages:
response = requests.post(
url,
headers=headers,
json=payload,
params={"debug": "true"}
)
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