On 6/01/13 20:44:08, Joseph L. Casale wrote:
> I have a dataset that consists of a dict with text descriptions and values
> that are integers. If
> required, I collect the values into a list and create a numpy array running
> it through a simple
> routine: data[abs(data - mean(data)) < m * std(data)] where m is the number
> of std deviations
> to include.
>
>
> The problem is I loos track of which were removed so the original display of
> the dataset is
> misleading when the processed average is returned as it includes the removed
> key/values.
>
>
> Ayone know how I can maintain the relationship and when I exclude a value,
> remove it from
> the dict?
Assuming your data and the dictionary are keyed by a common set of keys:
for key in descriptions:
if abs(data[key] - mean(data)) >= m * std(data):
del data[key]
del descriptions[key]
Hope this helps,
-- HansM
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