Result Filtering
Sampling
Result Filtering
Sampling
Our API supports sampling on all /results endpoints to provide a uniform sample of the dataset for efficient data analysis and visualization. This feature is particularly useful for use cases like charting (if you only have 4 000 pixels on the chart, then 10 000 datapoints are plenty), where analyzing a full dataset isn’t necessary. Sampling returns a subset (with a uniform distribution) of data, optimizing usage of very large results with reduced latency and costs. You can apply sampling to the following endpoints:
Example Sampling Request
import dotenv, os
from dune_client.types import QueryParameter
from dune_client.client import DuneClient
from dune_client.query import QueryBase
os.chdir("<path_to_your_dotevn_file>")
# load .env file
dotenv.load_dotenv(".env")
# setup Dune Python client
dune = DuneClient.from_env()
query_result = dune.get_latest_result_dataframe(
query=3582296 # https://dune.com/queries/3582296 -> OHLC Price
, sample_count = 5000
)
print(query_result)
Sampling Parameters
sample_count
- Type:
integer - Description: Determines the number of rows to return as a sample from the result set. If the available dataset contains fewer rows than the specified
sample_count, the entire dataset is returned.
- Sampling is designed to provide a randomized subset of data, with each request potentially producing different outcomes.
- When specifying
sample_count(e.g.,sample_count = 10000), the number is approximate. The actual number of rows returned may vary slightly (e.g., 10013, 10017), reflecting the probabilistic nature of the sampling process. sample_countis incompatible withoffset,limit, andfiltersparameters.sample_countcan be used withcolumnsto specify which data fields to include in the sample.- Specifying a low
sample_countrelative to total rows, being probabilistic, may return 0 rows due to the probability based sampling calculation (e.g., 10 in 100,000, means each row has ~0.01% chance to be included)
Sampling Response
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