How to efficiently use bin values in overlapping cells using Pandas?
I would like to remove all values from a float column into cells that overlap. The resulting column can be a series of 1-D vectors with bools - one vector for each value from the original column. The resulting vectors contain True
for each bin, the value of which falls False
for other boxes as well.
For example, if I have four cells [(0, 10), (7, 20), (15, 30), (30, 60)]
and the original value is 9.5, the resulting vector should be [True, True, False, False]
.
I know how to iterate over all ranges using a custom function using "apply", but is there a way to do this binning more efficiently and concisely?
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Will a simple checklist check suit your needs?
Bins = [(0, 10), (7, 20), (15, 30), (30, 60)]
Result = [((9.5>=y[0])&(9.5<=y[1])) for y in Bins]
If your data is stored in a data
pandas DataFrame ( df
) column , you can define a function:
def in_ranges(x,bins):
return [((x>=y[0])&(x<=y[1])) for y in bins]
and apply it to the column:
df[data].apply(lambda x: pd.Series(in_ranges(x,Bins),Bins))
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