Mammoth API quickstart

From nothing to a transformed dataset, three ways. All three do the same thing against the same API.

You need an API token first—see Authentication.

1 · Install

CLI

curl -fsSL https://raw.githubusercontent.com/EdgeMetric/mammothsdk/main/mammoth-cli/installers/mammoth-install.sh | sh
mammoth --version

Python SDK

pip install mammoth-io

curl

# Nothing to install.

2 · Authenticate

export MAMMOTH_API_TOKEN="mm_<your-token>"

CLI

mammoth auth login --input creds.json --storage file --output json --no-input
mammoth doctor --output json --no-input

mammoth-io

The SDK is async: run these steps inside one async def main() and start it with asyncio.run(main()) (in a notebook, await directly).

import asyncio
import os

from mammoth import MammothClient


async def main():
    async with MammothClient(
        api_token=os.environ["MAMMOTH_API_TOKEN"],
    ) as client:
        ...  # steps 3 to 7 go here


asyncio.run(main())

3 · Pick a project

Most operations need a project. The workspace comes from the token.

CLI

mammoth project list --output json --no-input
mammoth context project use <PROJECT_ID>

mammoth-io

await client.projects.list()    # {"projects": [...], "offset": 0, ...}
client.set_project_id(PROJECT_ID)

4 · Load some data

CLI

mammoth dataset create --input '{
  "dataset_spec": {"url": "https://sampledata.mammoth.io/Multi-Store_Retail_Sales.csv"},
  "ds_creation_type": "weburl"
}' --output json --no-input

mammoth-io

await client.datasets.list()
# upload a local file, or create from a URL, see the SDK reference

5 · Find the view

A dataset has one or more views, and transformations act on a view, not on the dataset. Take the id of the first one.

mammoth view list <DATASET_ID> --project <PROJECT_ID> --output json --no-input

6 · Transform it

A View is the object you work on: it wraps a dataview and carries the transformation methods. Transformations apply in place—each call sends a task to the pipeline, waits for it, and refreshes the view.

CLI

mammoth view transform math <VIEW_ID> --project <PROJECT_ID> \
  --input '{"expression": "quantity_sold * unit_price", "new_column": "revenue"}'

mammoth-io

from mammoth import Condition, Operator

view = await client.views.get(VIEW_ID)
await view.filter_rows(Condition("quantity_sold", Operator.GTE, 10))
await view.math("quantity_sold * unit_price", new_column="revenue")

7 · Get the result out

CLI

mammoth view export csv <VIEW_ID> --project <PROJECT_ID> --output json --no-input

The command writes a local file and refuses one that already exists with file_exists (exit 6). Pass --overwrite to replace it.

mammoth-io

await view.export.to_csv("output.csv")

8 · Clean up

Delete anything temporary. Note the confirmation: destructive commands need --yes, and there is no prompt when there is no terminal.

mammoth dataset delete <DATASET_ID> --project <PROJECT_ID> --yes --output json --no-input

Next up

• Command contract: all command families, with blast radius.
• For coding agents: the discovery loop, the envelope, exit codes.