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summary

Get Summary Statistics.

The summary that offers a snapshot of its central tendencies, variability, and distribution. This command calculates essential statistics, including mean, standard deviation, variance, and specific percentiles, to provide a detailed profile of your target column. B y examining these metrics, you gain insights into the data's overall behavior, helping to identify patterns, outliers, or anomalies. The summary table is an invaluable tool for initial data exploration, ensuring you have a solid foundation for further analysis or reporting.

Examples​

from openbb import obb
# Get Summary Statistics.
stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.quantitative.summary(data=stock_data, target='close')
obb.quantitative.summary(target='close', data='[{'date': '2023-01-02', 'open': 110.0, 'high': 120.0, 'low': 100.0, 'close': 115.0, 'volume': 10000.0}, {'date': '2023-01-03', 'open': 165.0, 'high': 180.0, 'low': 150.0, 'close': 172.5, 'volume': 15000.0}, {'date': '2023-01-04', 'open': 146.67, 'high': 160.0, 'low': 133.33, 'close': 153.33, 'volume': 13333.33}, {'date': '2023-01-05', 'open': 137.5, 'high': 150.0, 'low': 125.0, 'close': 143.75, 'volume': 12500.0}, {'date': '2023-01-06', 'open': 132.0, 'high': 144.0, 'low': 120.0, 'close': 138.0, 'volume': 12000.0}]')

Parameters​

data: list[openbb_core.provider.abstract.data.Data]
Time series data.

target: str
Target column name.

Returns​

results: list[SummaryModel]

Serializable results.

provider: None

Provider name.

warnings: Optional[list[Warning_]]

list of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

Data​

count: int
mean: float
std: float
var: float
min: float
max: float
p_25: float
p_50: float
p_75: float

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