I have a df with numbers:
numbers = pd.DataFrame(columns=['number'], data=[
50,
65,
75,
85,
90
])
and a df with ranges (look up table):
ranges = pd.DataFrame(
columns=['range','range_min','range_max'],
data=[
['A',90,100],
['B',85,95],
['C',70,80]
]
)
I want to determine what range (in second table) a value (in the first table) falls in. Please note ranges overlap, and limits are inclusive. Also please note the vanilla dataframe above has 3 ranges, however this dataframe gets generated dynamically. It could have from 2 to 7 ranges.
Desired result:
numbers = pd.DataFrame(columns=['number','detected_range'], data=[
[50,'out_of_range'],
[65, 'out_of_range'],
[75,'C'],
[85,'B'],
[90,'overlap'] * could be A or B *
])
I solved this with a for loop but this doesn't scale well to a big dataset I am using. Also code is too extensive and inelegant. See below:
numbers['detected_range'] = nan
for i, row1 in number.iterrows():
for j, row2 in ranges.iterrows():
if row1.number<row2.range_min and row1.number>row2.range_max:
numbers.loc[i,'detected_range'] = row1.loc[j,'range']
else if (other cases...):
...and so on...
How could I do this?
In its simplest form, the VLOOKUP function says: =VLOOKUP(What you want to look up, where you want to look for it, the column number in the range containing the value to return, return an Approximate or Exact match – indicated as 1/TRUE, or 0/FALSE).
You can use a bit of numpy
vectorial operations to generate masks, and use them to select
your labels:
import numpy as np
a = numbers['number'].values # numpy array of numbers
r = ranges.set_index('range') # dataframe of min/max with labels as index
m1 = (a>=r['range_min'].values[:,None]).T # is number above each min
m2 = (a<r['range_max'].values[:,None]).T # is number below each max
m3 = (m1&m2) # combine both conditions above
# NB. the two operations could be done without the intermediate variables m1/m2
m4 = m3.sum(1) # how many matches?
# 0 -> out_of_range
# 2 -> overlap
# 1 -> get column name
# now we select the label according to the conditions
numbers['detected_range'] = np.select([m4==0, m4==2], # out_of_range and overlap
['out_of_range', 'overlap'],
# otherwise get column name
default=np.take(r.index, m3.argmax(1))
)
output:
number detected_range
0 50 out_of_range
1 65 out_of_range
2 75 C
3 85 B
4 90 overlap
It works with any number of intervals in ranges
example output with extra['D',50,51]
:
number detected_range
0 50 D
1 65 out_of_range
2 75 C
3 85 B
4 90 overlap
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