Output formats - Minerva Documentation

resp = mc.api.enrich(records)

resp.to_dicts()        # list[dict]
resp.to_df()           # pandas DataFrame   (needs [pandas])
resp.to_csv("out.csv") # write a CSV
resp.to_table()        # pretty terminal table   (needs [table])

Enrich results are already flat, so person fields such as full_name map straight to columns. Nested objects are flattened where it makes sense — the keys of premium_fields become top-level columns in the DataFrame, etc.

Map results to your own schema

Build your own pydantic model from to_dicts() — results are flat, so field names line up directly:

from pydantic import BaseModel

class Lead(BaseModel):
    record_id: str
    full_name: str | None = None
    estimated_income_range: str | None = None
    crm_id: str | None = None          # not a Minerva field → stays None

leads = [Lead(**row) for row in mc.api.enrich(records).to_dicts()]

Which extras you need

Method	Extra	Why
`to_dicts()`	—	Pure stdlib
`to_df()`	`[pandas]`	DataFrame construction
`to_csv(path)`	—	Pure stdlib (uses `csv` module)
`to_table()`	`[table]`	Pretty box-drawing via `tabulate`

Which responses support them

The list-shaped responses (EnrichResponse, ResolveResponse, UsageResponse) implement the TabularMixin — they have to_dicts / to_df / to_csv / to_table. The dict-returning methods (get_li_contact_info, validate_emails, infer_record_country, mc.api.call) return raw dict — convert to your preferred format yourself if needed:

import pandas as pd

result = mc.api.validate_emails(["test@example.com"])
df = pd.DataFrame(result["results"])