> ## Documentation Index
> Fetch the complete documentation index at: https://docs.minerva.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Output formats

> Every list-shaped result → dicts, DataFrame, CSV, or a pretty terminal table.

```python theme={null}
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])
```

Nested fields are flattened where it makes sense — `personal_information.full_name`
becomes a top-level `full_name` column in the DataFrame, etc.

## Map results to your own schema

Project any result onto **your** pydantic model — missing fields become `None`
(with a one-time warning), so it never breaks:

```python theme={null}
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, warns once

leads = mc.api.enrich(records).map_to(Lead)      # -> list[Lead]
```

Pass `strict=True` to **validate** instead — raises on mismatch:

```python theme={null}
leads = mc.api.enrich(records).map_to(Lead, strict=True)
# raises pydantic.ValidationError if any record can't fit the Lead shape
```

## 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` |
| `map_to(Model)` | —          | Pydantic is already a core dep    |

## Which responses support them

The list-shaped responses (`EnrichResponse`, `ResolveResponse`, `UsageResponse`)
implement the `TabularMixin` — they have `to_dicts` / `to_df` / `to_csv` /
`to_table` / `map_to`.

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:

```python theme={null}
import pandas as pd

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