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
- standard
data: ForwardRef('Data') | ForwardRef('DataFrame') | ForwardRef('Series') | ForwardRef('ndarray') | dict | list
Time series data.
target: str
Target column name.
Returns
results: SummaryModel
Serializable results.
provider: str
Provider name.
warnings: Optional[list[Warning_]]
list of warnings.
chart: Optional[Chart]
Chart object.
extra: dict[str, Any]
Extra info.