analysis_data_statistics
analysis_data_statistics(data, all_data=False, standardized=False)Summary statistics for an observation record.
Runs the DataFrame summary-statistics facades over the exact series of an :class:AnalysisData frame (or a plain sequence): record length, low-outlier count, min/max, the first four product moments in real space and log10 space, and seven percentiles.
By default (all_data=False) the statistics are computed directly from the exact series (SummaryStatisticsExactDataOnly). With all_data=True, they instead come from a nonparametric distribution fit through the Hirsch-Stedinger plotting positions of the combined exact/interval/uncertain record (SummaryStatisticsAllData) – the same fit :func:analysis_data_summary uses – so "Record Length" counts the full record (censored series included) rather than the exact series alone, and the two methods can disagree even on a frame with no censored data because their percentile and moment estimators differ. Both report NaN for every value when the exact series has fewer than 10 points.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| data | AnalysisData or array_like | The observations, or an :class:AnalysisData frame (only its exact series is read). |
required |
| all_data | bool | Use SummaryStatisticsAllData (see above) instead of the default exact-series-only statistics. |
False |
| standardized | bool | Also run SetStandardizedValues() (after computing plotting positions) and return the exact series’ standardized values and standardized log10 values, parallel to :func:analysis_data_summary’s "value". |
False |
Returns
| Name | Type | Description |
|---|---|---|
| dict | "value" holds the twenty summary statistics, keyed by "Record Length", "Events Per Index (λ)", "Low Outliers", "Minimum", "Maximum", "Mean", "Std Dev", "Skewness", "Kurtosis", "Mean (of log)", "Std Dev (of log)", "Skewness (of log)", "Kurtosis (of log)", "1%", "5%", "25%", "50%", "75%", "95%", "99%". With standardized=True, "standardized_value" and "standardized_log10_value" are added: lists parallel to the exact series, in series order. |
Examples
>>> peaks = [12500, 15300, 8900, 22100, 18700, 14200, 9800, 28500, 17400, 11600]
>>> s = analysis_data_statistics(peaks)
>>> s["value"]["Mean"]
...