Charity Commission register data

See the data before you download

These filters apply to the table, free sample and list builder. Change the filters.

Four charity lists you can build in a click

Each example covers the Charity Commission register for England and Wales and starts with registered main charities. Change or combine the filters in the list builder.

• Registered main charities. Exclude removed charities and linked subsidiary records.
• Latest income of £1 million or more. The minimum is inclusive. Check the financial year end alongside income.
• Activities mentioning food bank. Searches the charity’s own activity text; it is not a complete cause classification.
• Registered contact postcodes starting LS. A Leeds postcode prefix describes the registered contact address, not the area served.

Before downloading, review the charity register CSV columns, sample and latest import date. You can also compare the other available datasets.

Read the fields correctly

Registered charity number identifies the registration; linked charity number distinguishes linked records. The “Main charities only” filter excludes those linked records. Latest income and latest expenditure are filed figures in GBP, not forecasts. Financial year end is the reporting period; the DataScoop import date is separate.

Postcode matching uses a prefix: LS matches LS postcodes, while M also matches ME and MK. Use a district such as M1 when that distinction matters. Neither a registered address nor an activity-text match proves where a charity delivers services.

Open the CSV in Excel

1. Choose an example and check the rows and import date.
2. Download the free sample, which includes up to 25 matching rows and all public columns.
3. In Excel, choose Data → From Text/CSV, select UTF-8 and comma-separated fields, and keep charity numbers and postcodes as text.
4. Compare the income figure with its financial year end before combining charities in an analysis.

Prefer the original files? The Charity Commission full register download is free. DataScoop prepares a filterable table from that source. Scotland and Northern Ireland are separate registers and are not included.

Or pull it straight into your code or your agent

# Registered main charities with income of GBP 1m or more and LS contact postcodes
curl "https://datascoop.io/v1/datasets/charity-commission/query?\
  status=Registered&mainCharitiesOnly=true&minIncome=1000000&postcodeArea=LS&format=csv" \
  -H "Authorization: Bearer ds_your_key"

// # Registered main charities with income of GBP 1m or more and LS contact postcodes
const res = await fetch("https://datascoop.io/v1/datasets/charity-commission/query?status=Registered&mainCharitiesOnly=true&minIncome=1000000&postcodeArea=LS", {
  headers: { Authorization: "Bearer ds_your_key" }
});
const { rows, total, attribution } = await res.json();

# # Registered main charities with income of GBP 1m or more and LS contact postcodes
import requests
r = requests.get("https://datascoop.io/v1/datasets/charity-commission/query",
                 params={"status":"Registered","mainCharitiesOnly":"true","minIncome":"1000000","postcodeArea":"LS"},
                 headers={"Authorization": "Bearer ds_your_key"})
rows = r.json()["rows"]

# Claude Code (one line; other clients on /agents)
claude mcp add --transport http datascoop https://datascoop.io/mcp \
  --header "Authorization: Bearer ds_your_key"

# Your agent then calls
query_dataset({ dataset: "charity-commission", filters: {"status":"Registered","mainCharitiesOnly":"true","minIncome":"1000000","postcodeArea":"LS"} })

Everything you need on every row

Organisational fields only. No personal contact lists.