Source: https://databento.com/docs/examples/basics-historical/encodings # Convert DBN to other encoding formats In this example, we'll walk through how to convert DBN to CSV, JSON, or Parquet encoding formats. ## DBN [DBN](https://databento.com/docs/conventions/databento-binary-encoding.md) is an extremely fast message encoding and storage format for normalized market data. DBN is the default encoding used across our API and client libraries. Historical data can be requested with [timeseries.get_range()](https://databento.com/docs/api-historical/timeseries/timeseries-get-range.md). This method returns a [DBNStore](https://databento.com/docs/api-historical/helpers/dbn-store.md). > **See also** > > Historical data can also be requested in various encoding formats with batch downloads. > See the [programmatic batch downloads example](https://databento.com/docs/examples/basics-historical/programmatic-batch-download.md) for more information. ```python import databento as db client = db.Historical("$YOUR_API_KEY") dbn_store = client.timeseries.get_range( dataset="EQUS.SUMMARY", symbols=["AAPL", "NVDA", "NFLX", "META", "GOOGL"], schema="ohlcv-1d", start="2025-09-30", ) ``` You can save this data to a DBN file with [DBNStore.to_file()](https://databento.com/docs/api-historical/helpers/dbn-store-to-file.md), then read it back into a DBNStore with [DBNStore.from_file()](https://databento.com/docs/api-historical/helpers/dbn-store-from-file.md). ```python dbn_store.to_file("demo_data.dbn") dbn_store = db.DBNStore.from_file("demo_data.dbn") ``` You can inspect the symbology mappings for a [DBNStore](https://databento.com/docs/api-historical/helpers/dbn-store.md) with the `symbology` attribute. ```python dbn_store = db.DBNStore.from_file("demo_data.dbn") print(dbn_store.symbology) ``` ```stdout {"symbols": ["AAPL", "NVDA", "NFLX", "META", "GOOGL"], "stype_in": "raw_symbol", "stype_out": "instrument_id", "start_date": "2025-09-30", "end_date": "2025-10-01", "partial": [], "not_found": [], "mappings": {"GOOGL": [{"start_date": datetime.date(2025, 9, 30), "end_date": datetime.date(2025, 10, 1), "symbol": "7152"}], "NVDA": [{"start_date": datetime.date(2025, 9, 30), "end_date": datetime.date(2025, 10, 1), "symbol": "11667"}], "AAPL": [{"start_date": datetime.date(2025, 9, 30), "end_date": datetime.date(2025, 10, 1), "symbol": "38"}], "NFLX": [{"start_date": datetime.date(2025, 9, 30), "end_date": datetime.date(2025, 10, 1), "symbol": "11275"}], "META": [{"start_date": datetime.date(2025, 9, 30), "end_date": datetime.date(2025, 10, 1), "symbol": "10451"}]}} ``` You can [iterate](https://databento.com/docs/api-historical/helpers/dbn-store-iter.md) over a DBNStore, which will yield DBN records. ```python dbn_store = db.DBNStore.from_file("demo_data.dbn") for record in dbn_store: print(record) ``` ```stdout OhlcvMsg(rtype=, publisher_id=EQUS.SUMMARY.EQUS (90), instrument_id=38, ts_event=1759190400000000000, pretty_ts_event='2025-09-30T00:00:00.000000000Z', open=254855000000, pretty_open=254.855, high=255919000000, pretty_high=255.919, low=253110000000, pretty_low=253.11, close=254630000000, pretty_close=254.63, volume=37704259) OhlcvMsg(rtype=, publisher_id=EQUS.SUMMARY.EQUS (90), instrument_id=7152, ts_event=1759190400000000000, pretty_ts_event='2025-09-30T00:00:00.000000000Z', open=242810000000, pretty_open=242.81, high=243290000000, pretty_high=243.29, low=239245000000, pretty_low=239.245, close=243100000000, pretty_close=243.1, volume=34724346) OhlcvMsg(rtype=, publisher_id=EQUS.SUMMARY.EQUS (90), instrument_id=10451, ts_event=1759190400000000000, pretty_ts_event='2025-09-30T00:00:00.000000000Z', open=742250000000, pretty_open=742.25, high=742970000000, pretty_high=742.97, low=726300000000, pretty_low=726.3, close=734380000000, pretty_close=734.38, volume=16226750) OhlcvMsg(rtype=, publisher_id=EQUS.SUMMARY.EQUS (90), instrument_id=11275, ts_event=1759190400000000000, pretty_ts_event='2025-09-30T00:00:00.000000000Z', open=1206410000000, pretty_open=1206.41, high=1208500000000, pretty_high=1208.5, low=1178000000000, pretty_low=1178, close=1198920000000, pretty_close=1198.92, volume=3830304) OhlcvMsg(rtype=, publisher_id=EQUS.SUMMARY.EQUS (90), instrument_id=11667, ts_event=1759190400000000000, pretty_ts_event='2025-09-30T00:00:00.000000000Z', open=182080000000, pretty_open=182.08, high=187350000000, pretty_high=187.35, low=181480000000, pretty_low=181.48, close=186580000000, pretty_close=186.58, volume=236981032) ``` You can convert a DBNStore into a Pandas DataFrame with [DBNStore.to_df()](https://databento.com/docs/api-historical/helpers/dbn-store-to-df.md). ```python dbn_store = db.DBNStore.from_file("demo_data.dbn") df = dbn_store.to_df() print(df) ``` ```stdout rtype publisher_id instrument_id open high low close volume symbol ts_event 2025-09-30 00:00:00+00:00 35 90 38 254.855 255.919 253.110 254.63 37704259 AAPL 2025-09-30 00:00:00+00:00 35 90 7152 242.810 243.290 239.245 243.10 34724346 GOOGL 2025-09-30 00:00:00+00:00 35 90 10451 742.250 742.970 726.300 734.38 16226750 META 2025-09-30 00:00:00+00:00 35 90 11275 1206.410 1208.500 1178.000 1198.92 3830304 NFLX 2025-09-30 00:00:00+00:00 35 90 11667 182.080 187.350 181.480 186.58 236981032 NVDA ``` ## CSV > **See also** > > Data can also be directly requested in CSV format by setting `encoding="csv"` in [batch.submit_job()](https://databento.com/docs/api-historical/batch/batch-submit-job.md). You can save a DBNStore to a CSV file with [DBNStore.to_csv()](https://databento.com/docs/api-historical/helpers/dbn-store-to-csv.md). ```python dbn_store = db.DBNStore.from_file("demo_data.dbn") dbn_store.to_csv("demo_data.csv") with open("demo_data.csv", "r") as f: print(f.read()) ``` ```stdout ts_event,rtype,publisher_id,instrument_id,open,high,low,close,volume,symbol 2025-09-30T00:00:00.000000000Z,35,90,38,254.855000000,255.919000000,253.110000000,254.630000000,37704259,AAPL 2025-09-30T00:00:00.000000000Z,35,90,7152,242.810000000,243.290000000,239.245000000,243.100000000,34724346,GOOGL 2025-09-30T00:00:00.000000000Z,35,90,10451,742.250000000,742.970000000,726.300000000,734.380000000,16226750,META 2025-09-30T00:00:00.000000000Z,35,90,11275,1206.410000000,1208.500000000,1178.000000000,1198.920000000,3830304,NFLX 2025-09-30T00:00:00.000000000Z,35,90,11667,182.080000000,187.350000000,181.480000000,186.580000000,236981032,NVDA ``` ## JSON > **See also** > > Data can also be directly requested in JSON format by setting `encoding="json"` in [batch.submit_job()](https://databento.com/docs/api-historical/batch/batch-submit-job.md). You can save a DBNStore to a JSONL file with [DBNStore.to_json()](https://databento.com/docs/api-historical/helpers/dbn-store-to-json.md). ```python dbn_store = db.DBNStore.from_file("demo_data.dbn") dbn_store.to_json("demo_data.json") with open("demo_data.json", "r") as f: print(f.read()) ``` ```stdout {"hd":{"ts_event":"2025-09-30T00:00:00.000000000Z","rtype":35,"publisher_id":90,"instrument_id":38},"open":"254.855000000","high":"255.919000000","low":"253.110000000","close":"254.630000000","volume":"37704259","symbol":"AAPL"} {"hd":{"ts_event":"2025-09-30T00:00:00.000000000Z","rtype":35,"publisher_id":90,"instrument_id":7152},"open":"242.810000000","high":"243.290000000","low":"239.245000000","close":"243.100000000","volume":"34724346","symbol":"GOOGL"} {"hd":{"ts_event":"2025-09-30T00:00:00.000000000Z","rtype":35,"publisher_id":90,"instrument_id":10451},"open":"742.250000000","high":"742.970000000","low":"726.300000000","close":"734.380000000","volume":"16226750","symbol":"META"} {"hd":{"ts_event":"2025-09-30T00:00:00.000000000Z","rtype":35,"publisher_id":90,"instrument_id":11275},"open":"1206.410000000","high":"1208.500000000","low":"1178.000000000","close":"1198.920000000","volume":"3830304","symbol":"NFLX"} {"hd":{"ts_event":"2025-09-30T00:00:00.000000000Z","rtype":35,"publisher_id":90,"instrument_id":11667},"open":"182.080000000","high":"187.350000000","low":"181.480000000","close":"186.580000000","volume":"236981032","symbol":"NVDA"} ``` ## Parquet You can save a DBNStore to a parquet file with [DBNStore.to_parquet()](https://databento.com/docs/api-historical/helpers/dbn-store-to-parquet.md). ```python dbn_store = db.DBNStore.from_file("demo_data.dbn") dbn_store.to_parquet("demo_data.parquet") ```