Source: https://databento.com/docs/api-historical/helpers/map_symbols_csv


### map_symbols_csv

Use a `symbology.json` file to map a symbols column onto an existing CSV
file. The result is written to `out_file`.

#### API method

```python
map_symbols_csv(
    symbology_file: PathLike[str] | str,
    csv_file: PathLike[str] | str,
    out_file: PathLike[str] | str | None = None,
) -> Path:
```

#### Parameters

| Parameter | Type | Required | Description |
| `symbology_file` | PathLike[str] or str | required | Path to a `symbology.json` file to use as a symbology source. |
| `csv_file` | PathLike[str] or str | required | Path to a CSV file that contains encoded DBN data; must contain a `ts_recv` or `ts_event` and `instrument_id` column. |
| `out_file` | PathLike[str] or str | optional | Path to a file to write results to. If unspecified, `_mapped` will be appended to the `csv_file` name. |

#### Returns

[Path](https://docs.python.org/3/library/pathlib.html?highlight=path#pathlib.Path) to the written file.

#### Example usage

```python
import databento as db

result = db.map_symbols_csv(
    "symbology.json",
    "xnas-itch-20230821-20230825.ohlcv-1d.csv",
)

print(result.read_text())
```
#### Example response

```plaintext
ts_event,rtype,publisher_id,instrument_id,open,high,low,close,volume,symbol
1692576000000000000,35,2,523,133550000000,135200000000,132710000000,134360000000,11015261,AMZN
1692576000000000000,35,2,7290,439090000000,472900000000,437260000000,470990000000,11098972,NVDA
1692576000000000000,35,2,10157,217000000000,233500000000,217000000000,233380000000,21336884,TSLA
1692576000000000000,35,2,7130,430170000000,434700000000,0,433000000000,68661,NOC
1692662400000000000,35,2,7132,431610000000,438480000000,0,437740000000,86950,NOC
1692662400000000000,35,2,10161,236710000000,241550000000,229560000000,232870000000,17069349,TSLA
1692662400000000000,35,2,523,135320000000,135900000000,133740000000,134200000000,8133698,AMZN
1692662400000000000,35,2,7293,475120000000,483440000000,453340000000,457910000000,11700447,NVDA
1692748800000000000,35,2,523,135120000000,137860000000,133220000000,137060000000,11430081,AMZN
1692748800000000000,35,2,7296,463190000000,518870000000,452080000000,502150000000,13361964,NVDA
1692748800000000000,35,2,7135,0,440000000000,0,434030000000,68411,NOC
1692748800000000000,35,2,10163,236590000000,243750000000,226500000000,240810000000,13759234,TSLA
1692835200000000000,35,2,7287,508190000000,512600000000,466720000000,468700000000,21611800,NVDA
1692835200000000000,35,2,10154,242000000000,244140000000,228180000000,229980000000,13724054,TSLA
1692835200000000000,35,2,7126,433650000000,437730000000,0,429880000000,54570,NOC
1692835200000000000,35,2,521,137250000000,137820000000,131410000000,131880000000,11990573,AMZN
```