I have a dataframe in Pandas with collected data;
import pandas as pd
df = pd.DataFrame({'Group': ['A','A','A','A','A','A','A','B','B','B','B','B','B','B'], 'Subgroup': ['Blue', 'Blue','Blue','Red','Red','Red','Red','Blue','Blue','Blue','Blue','Red','Red','Red'],'Obs':[1,2,4,1,2,3,4,1,2,3,6,1,2,3]})
+-------+----------+-----+
| Group | Subgroup | Obs |
+-------+----------+-----+
| A | Blue | 1 |
| A | Blue | 2 |
| A | Blue | 4 |
| A | Red | 1 |
| A | Red | 2 |
| A | Red | 3 |
| A | Red | 4 |
| B | Blue | 1 |
| B | Blue | 2 |
| B | Blue | 3 |
| B | Blue | 6 |
| B | Red | 1 |
| B | Red | 2 |
| B | Red | 3 |
+-------+----------+-----+
The Observations ('Obs') are supposed to be numbered without gaps, but you can see we have 'missed' Blue 3 in group A and Blue 4 and 5 in group B. The desired outcome is a percentage of all 'missed' Observations ('Obs') per group, so in the example:
+-------+--------------------+--------+--------+
| Group | Total Observations | Missed | % |
+-------+--------------------+--------+--------+
| A | 8 | 1 | 12.5% |
| B | 9 | 2 | 22.22% |
+-------+--------------------+--------+--------+
I tried both with for loops and by using groups (for example:
df.groupby(['Group','Subgroup']).sum()
print(groups.head)
) but I can't seem to get that to work in any way I try. Am I going about this the wrong way?
From another answer (big shoutout to @Lie Ryan) I found a function to look for missing elements, however I don't quite understand how to implement this yet;
def window(seq, n=2):
"Returns a sliding window (of width n) over data from the iterable"
" s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ... "
it = iter(seq)
result = tuple(islice(it, n))
if len(result) == n:
yield result
for elem in it:
result = result[1:] + (elem,)
yield result
def missing_elements(L):
missing = chain.from_iterable(range(x + 1, y) for x, y in window(L) if (y - x) > 1)
return list(missing)
Can anyone give me a pointer is the right direction?
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