95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
"""build_boxplot_stats — Tukey boxplot statistics from an EDA `numeric` sub-block.
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Pure function: no I/O, deterministic. Takes the `numeric` dict of a ColumnProfile
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(group `eda`, the output of describe_numeric) and derives the figures needed to
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draw a horizontal Tukey boxplot using the 1.5 * IQR rule.
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It only derives numbers from already-computed percentiles; it never sees the raw
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column values. Reading is defensive (.get throughout) and the function NEVER
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raises: if the key percentiles (p25 / p50 / p75) are missing it returns {} so the
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caller can simply skip the boxplot.
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"""
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def _num(value):
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"""Coerce to float defensively; return None for None/bool/non-numeric."""
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# bool is a subclass of int; a percentile value is never a real bool, so
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# treat True/False as missing instead of silently coercing to 1.0/0.0.
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if value is None or isinstance(value, bool):
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return None
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try:
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return float(value)
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except (TypeError, ValueError):
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return None
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def build_boxplot_stats(numeric: dict) -> dict:
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"""Derive Tukey boxplot statistics from the `numeric` sub-block of a profile.
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Reads the percentiles already computed by describe_numeric and applies the
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classic 1.5 * IQR fence rule to obtain the whisker extremes and outlier
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flags of a horizontal boxplot. No raw values are needed.
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Args:
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numeric: The `numeric` sub-block of an eda ColumnProfile (output of
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describe_numeric). Every value may be None; read defensively.
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Returns:
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Dict with the boxplot figures
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{q1, median, q3, iqr, lower_fence, upper_fence, whisker_lo, whisker_hi,
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min, max, has_low_outliers, has_high_outliers, n_outliers}.
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If p25, p50/median or p75 are missing (None) returns {} (empty dict) so
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the caller omits the plot.
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"""
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if not isinstance(numeric, dict):
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return {}
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q1 = _num(numeric.get("p25"))
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q3 = _num(numeric.get("p75"))
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# Prefer the explicit median; fall back to p50 (they are the same quantile).
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median = _num(numeric.get("median"))
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if median is None:
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median = _num(numeric.get("p50"))
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# Without the three quartiles a boxplot cannot be drawn.
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if q1 is None or q3 is None or median is None:
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return {}
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# Recompute the IQR from the quartiles rather than trusting numeric['iqr'],
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# which may be missing even when the percentiles are present.
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iqr = q3 - q1
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lower_fence = q1 - 1.5 * iqr
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upper_fence = q3 + 1.5 * iqr
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mn = _num(numeric.get("min"))
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mx = _num(numeric.get("max"))
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# Whisker extremes: the real data range clamped to the fences. When the
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# corresponding extreme is missing, fall back to the fence itself.
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whisker_lo = max(mn, lower_fence) if mn is not None else lower_fence
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whisker_hi = min(mx, upper_fence) if mx is not None else upper_fence
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has_low_outliers = bool(mn is not None and mn < lower_fence)
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has_high_outliers = bool(mx is not None and mx > upper_fence)
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# Informative only: these outliers come from the z-score block of the
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# profile, not from this IQR fence computation.
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raw_n = numeric.get("n_outliers")
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n_outliers = int(raw_n) if isinstance(raw_n, (int, float)) and not isinstance(raw_n, bool) else 0
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return {
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"q1": q1,
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"median": median,
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"q3": q3,
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"iqr": iqr,
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"lower_fence": lower_fence,
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"upper_fence": upper_fence,
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"whisker_lo": whisker_lo,
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"whisker_hi": whisker_hi,
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"min": mn,
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"max": mx,
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"has_low_outliers": has_low_outliers,
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"has_high_outliers": has_high_outliers,
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"n_outliers": n_outliers,
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}
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