|
|
|
|
| import pandas as pd
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| from core.estimation.descriptive import compute_descriptive_statistics
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|
|
|
|
| def run_descriptive_statistics(
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| *,
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| df: pd.DataFrame,
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| column: str,
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| quantile_probs: list[float],
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| trim_alpha: float | None,
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| winsor_limits: tuple[float, float] | None,
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| weights_col: str | None,
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| round_digits: int,
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| ) -> pd.DataFrame:
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|
|
| if df is None:
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| raise ValueError("No dataset loaded.")
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|
|
| if column not in df.columns:
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| raise ValueError(f"Column '{column}' not found.")
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|
|
| series = df[column].dropna()
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|
|
| if series.empty:
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| raise ValueError("Selected column has no valid data.")
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|
|
| if not pd.api.types.is_numeric_dtype(series):
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| raise ValueError("Selected column must be numeric.")
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|
|
| weights = None
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|
|
| if weights_col:
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| if weights_col not in df.columns:
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| raise ValueError(f"Weights column '{weights_col}' not found.")
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|
|
| weights = df.loc[series.index, weights_col]
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|
|
| if not pd.api.types.is_numeric_dtype(weights):
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| raise ValueError("Weights must be numeric.")
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|
|
| if (weights < 0).any():
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| raise ValueError("Weights must be non-negative.")
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|
|
| stats_df = compute_descriptive_statistics(
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| data=series.values,
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| quantile_probs=quantile_probs,
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| trim_alpha=trim_alpha,
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| winsor_limits=winsor_limits,
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| weights=weights.values if weights is not None else None,
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| )
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|
|
|
|
|
|
|
|
| stats_df[["Value", "Bias Corrected"]] = stats_df[["Value", "Bias Corrected"]].round(round_digits)
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|
|
| return stats_df |
