On 2013-01-06 22:33, Hans Mulder wrote:
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]
It's generally a bad idea to modify a collection over which you're
iterating. It's better to, say, make a list of what you're going to
delete and then iterate over that list to make the deletions:
deletions = []
for key in in descriptions:
if abs(data[key] - mean(data)) >= m * std(data):
deletions.append(key)
for key in deletions:
del data[key]
del descriptions[key]
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