DBNStore.to_df

Converts data to a pandas DataFrame.

Info

The DataFrame index will be set to ts_recv if it exists in the schema, otherwise it will be set to ts_event.

See also

While not optimized for use with live data due to their column-oriented format, pandas DataFrames can still be used with live data by first streaming DBN data to a file, then converting to a DataFrame with DBNStore.from_file().to_df(). See this example for more information.

Parameters

Parameter
Type
Required
Description
price_type
PriceType or str, default "float"
optional
The price type to use for price fields. If "fixed", prices will have a type of int in fixed decimal format; each unit representing 1e-9 or 0.000000001. If "float", prices will have a type of float. If "decimal", prices will be instances of decimal.Decimal.
pretty_ts
bool, default True
optional
Whether timestamp columns are converted to tz-aware pandas.Timestamp. The timezone can be specified using the tz parameter.
map_symbols
bool, default True
optional
If symbology mappings from the metadata should be used to create a 'symbol' column, mapping the instrument ID to its raw symbol for every record.
schema
Schema or str
optional
The data record schema for the output DataFrame. Must be one of the values from list_schemas. This is only required when reading a DBNStore with mixed record types.
tz
datetime.tzinfo or str, default UTC
optional
If pretty_ts is True, all timestamps will be converted to the specified timezone.
count
int
optional
If set, instead of returning a single DataFrame a DataFrameIterator instance will be returned. When iterated, this object will yield a DataFrame with at most count elements until the entire contents of the DBNStore are exhausted.

Returns

A pandas DataFrame object.

API method 
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DBNStore.to_df(
    price_type: PriceType | str = "float",
    pretty_ts: bool = True,
    map_symbols: bool = True,
    schema: Schema | str | None = None,
    tz: datetime.tzinfo | str = zoneinfo.ZoneInfo("UTC"),
    count: int | None = None,
) -> pd.DataFrame | DataFrameIterator
Example usage 
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import databento as db

client = db.Historical("$YOUR_API_KEY")

data = client.timeseries.get_range(
    dataset="GLBX.MDP3",
    schema="trades",
    symbols=["ESM2"],
    start="2022-03-06",
)

df = data.to_df()
print(df.head())
Example response 
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                                                               ts_event  rtype  publisher_id  instrument_id action  ... size  flags  ts_in_delta  sequence  symbol
ts_recv                                                                                                             ...
2022-03-06 23:00:00.039463300+00:00 2022-03-06 23:00:00.036436177+00:00      0             1           3403      T  ...    1      0        18828      5178    ESM2
2022-03-06 23:00:01.098111252+00:00 2022-03-06 23:00:01.097477845+00:00      0             1           3403      T  ...    1      0        19122      6816    ESM2
2022-03-06 23:00:04.612334175+00:00 2022-03-06 23:00:04.611714663+00:00      0             1           3403      T  ...    1      0        18687     10038    ESM2
2022-03-06 23:00:04.613776789+00:00 2022-03-06 23:00:04.613240435+00:00      0             1           3403      T  ...    1      0        18452     10045    ESM2
2022-03-06 23:00:06.881864467+00:00 2022-03-06 23:00:06.880575603+00:00      0             1           3403      T  ...    1      0        18478     11343    ESM2

[5 rows x 13 columns]