mirofish_swarm_oracle__maxia_cc11afc3 · Datasets on Ouro

mirofish_swarm_oracle__maxia_cc11afc3

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Dataset documentation

Learn how to interact with this dataset using the Ouro SDK or REST API.

API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.

Retrieve dataset

Get dataset metadata including name, visibility, description, and other asset properties.

import os
from ouro import Ouro
 
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
 
dataset_id = "019d9fed-48a5-7402-a5a7-adab05535466"
 
# Retrieve dataset metadata
dataset = ouro.datasets.retrieve(dataset_id)
print(dataset.name, dataset.visibility)
print(dataset.metadata)

Read schema

Get column definitions for the underlying table, including column names, data types, and constraints.

10 columns

Column	Type
average_signal	`real`
confidence	`real`
consensus_id	`text`
consensus_reached	`integer`
depth_iterations	`integer`
direction	`text`
domain_scores	`text`
query	`text`
swarm_size	`integer`
timestamp	`text`

# Get column definitions for the underlying table
columns = ouro.datasets.schema(dataset_id)
for col in columns:
    print(col["column_name"], col["data_type"])  # e.g., age integer, name text

Query data

Fetch the dataset's rows. Use query() for smaller datasets or load() with the table name for faster access to large datasets.

# Option 1: Query data by dataset ID (returns Pandas DataFrame)
df = ouro.datasets.query(dataset_id)
print(df.head())
 
# Option 2: Load data by table name (faster for large datasets)
table_name = dataset.metadata["table_name"]  # e.g., "mirofish_swarm_oracle__maxia_cc11afc3"
df = ouro.datasets.load(table_name)
print(len(df))

Update dataset

Update dataset metadata (visibility, description, etc.) and optionally write new rows to the table. Writing new data will replace the existing data in the table. Requires write or admin permission on the dataset.

import pandas as pd
 
# Update dataset metadata
updated = ouro.datasets.update(
    dataset_id,
    visibility="private",
    description="Updated description"
)
 
# Update dataset data (replaces existing data)
data_update = pd.DataFrame([
    {"name": "Charlie", "age": 33},
    {"name": "Diana", "age": 28},
])
updated = ouro.datasets.update(dataset_id, data=data_update)