# Handle Spike or overflow value in dataset

**URL:** <https://forum.inductiveautomation.com/t/handle-spike-or-overflow-value-in-dataset/74898>\
**Category:** Ignition\
**Tags:** vision\
**Created:** [June 12, 2023, 9:23am UTC](https://forum.inductiveautomation.com/t/handle-spike-or-overflow-value-in-dataset/74898 "2023-06-12T09:23:40Z")\
**Posts on this page:** 1\
**Showing post:** 7

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**Author:** ![JordanCClark](https://sea1.discourse-cdn.com/inductiveautomation/user_avatar/forum.inductiveautomation.com/jordancclark/32/83362_2.png) [@JordanCClark](https://forum.inductiveautomation.com/u/JordanCClark)\
**Post date:** [June 12, 2023, 11:53am UTC](https://forum.inductiveautomation.com/t/handle-spike-or-overflow-value-in-dataset/74898/7 "2023-06-12T11:53:13Z")

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This is looking like a totalizer value. I'd say the median would be the one to use here. Using statsutils' quantiles, you should be able to filter out spikes in both directions.

[Statistics Extensions for Ignition](https://forum.inductiveautomation.com/t/statistics-extensions-for-ignition/17100)

```python
data = dataset.getColumnAsList(dataset.getColumnIndex('value'))

s = shared.statsutils.Stats(data)

print s.get_quantile(.25)
print s.get_quantile(.75)

```

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