Meaning
Computes a rolling quantile (e.g., median, 90th percentile) over a time-ordered sequence of numeric values, returning a series where each point reflects the quantile of the preceding window.
Primary Function
Time-series analysis
Communicative Purpose
Summarize the distribution of values within a sliding window to monitor trends in extremes or central tendency.
Pattern
def quantile_over_time(data, window, q): """Return list of rolling q‑quantile values for data.""" from statistics import quantiles result = [] for i in range(len(data)): start = max(0, i - window + 1) window_data = data[start:i+1] if len(window_data) >= 2: q_val = quantiles(window_data, n=int(1/q))[0] # simplified else: q_val = window_data[0] if window_data else None result.append(q_val) return result
Core Structure
def quantile_over_time(...): for ... in ...: ... = ...[...:...] ... = ...(...) ... append(...)
Função primária
Time-series analysis
Propósito comunicativo
Summarize the distribution of values within a sliding window to monitor trends in extremes or central tendency.
Situações de gatilho
Monitoring latency or response‑time percentiles in service metrics Analyzing rolling volatility in financial price series Detecting shifts in sensor readings over time
Contextos
Performance monitoring, finance, IoT analytics, any domain with ordered numeric streams.
Padrão
def quantile_over_time(data, window, q): """Return list of rolling q‑quantile values for data.""" from statistics import quantiles result = [] for i in range(len(data)): start = max(0, i - window + 1) window_data = data[start:i+1] if len(window_data) >= 2: q_val = quantiles(window_data, n=int(1/q))[0] # simplified else: q_val = window_data[0] if window_data else None result.append(q_val) return result
Estrutura central
def quantile_over_time(...): for ... in ...: ... = ...[...:...] ... = ...(...) ... append(...)
Slots de substituição
data: sequence of numeric values (list, array, Series); window: int > 0, size of sliding window; q: float in (0,1], quantile to compute (e.g., 0.5 for median).
Colocados típicos
- pandas.DataFrame.rolling
- numpy.percentile
- statistics.quantiles
- time‑indexed series
- datetime indexing.
Substituições comuns
- Using pandas: series.rolling(window).quantile(q)
- using numpy: np.percentile(window
- q*100).
Erros comuns
Using an unsorted time series, causing the window to mix past and future values Choosing a window larger than the data length without handling edge cases Confusing q (0‑1) with percentile (0‑100) when calling numpy.percentile
Similar / contraste
moving_average (computes mean, not quantile); exponential_weighted_moving_average (gives more weight to recent values).
Interferências
Coming from SQL: quantile OVER (PARTITION BY ... ORDER BY ...) computes a global quantile, not a sliding window; ensure you specify a frame (ROWS BETWEEN ...).
Família do chunk
- rolling_statistics
- moving_average
- exponential_weighted_moving_average
- percentile_ranking
Nuance
Complexity is O(w·log w) per step if sorting each window; can be optimized with histogram‑based or tree‑based structures for large windows. Returns None or edge values for incomplete windows unless otherwise handled.
Efeito pragmático
Makes it easy to track distributional shifts (e.g., rising tail latency) without storing full histories.
Dica de memória
‘Quantile over time = sliding percentile’.
Nota
The current implementation relies on statistics.quantiles which requires an integer n; for arbitrary quantiles you may need a custom selection algorithm or a library that handles fractional quantiles
Upgrade path
Use pandas.DataFrame.rolling(...).quantile(q) or specialized streaming quantile algorithms (e.g., t-digest) for O(1) updates.
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