summary_statistics

Compute descriptive summary statistics for each numeric column.

Returns per-column count, mean, standard deviation, minimum, quartiles, maximum, skewness, and excess kurtosis - a quick overview of each variable's distribution.

Examples

from openbb import obb
obb.econometrics.summary_statistics(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: Data | DataFrame | Series | dict | list | list[DataFrame] | list[Data] | list[Series] | ndarray
Input dataset.

---

Returns

results: list[SummaryStatisticsData]

Serializable results.

provider: str

Provider name.

warnings: Optional[list[Warning_]]

List of warnings.

chart: Optional[Chart]

Chart object.

extra: dict[str, Any]

Extra info.

---

Data

• standard

column: str
Name of the column.

count: int
Number of non-null observations.

mean: float
Arithmetic mean.

std: float
Sample standard deviation.

min: float
Minimum value.

p25: float
25th percentile.

median: float
Median (50th percentile).

p75: float
75th percentile.

max: float
Maximum value.

skew: float
Skewness of the distribution.

kurtosis: float
Excess kurtosis of the distribution.