if values existes from given list in multiple column and counts the number of column - python-3.x

Question

i have below df
B C D E
2 2 4 11
11 0 5 3
12 10 1 11
5 9 7 15
1st i wants a unique value from whole df like below:
[0,1,2,3,4,5,7,9,10,11,12,15]
then i wants final output
value value exists in number of col
0 1
1 1
2 2
3 1
4 1
5 1
7 1
9 1
10 1
11 2
12 1
15 1
that means each value,how many columns its available
i wants that output

Answer 1

Using python you can do something like this:
# your input df as a list of lists
df = [[2,11,12,5], [2,0,10,9], [4,5,1,7], [11,3,11,15]]
#remove duplicates in each list
dfU = [list(set(l)) for l in df]
# sort each list (not required for this approach)
for l in dfU:
l.sort()
# the requested unique list
flatList = [item for sublist in df for item in sublist]
uniqueList = list(set(flatList))
print(uniqueList)
# output as a list of lists
output = []
for num in uniqueList:
cnt = 0
for idx in range(len(dfU)):
if dfU[idx].count(num) > 0:
cnt+=1
output.append([num,cnt])
print(output)
Side note, the count function is computationally expensive, so it would be better to do a linear scan along all sorted columns.

Answer 2

Use DataFrame.melt for reshape, remove duplicates by both columns and count by GroupBy.size with Series.reset_index for DataFrame:
df1 = (df.melt(value_name='value')
.drop_duplicates()
.groupby('value')
.size()
.reset_index(name='count'))
print (df1)
value count
0 0 1
1 1 1
2 2 2
3 3 1
4 4 1
5 5 2
6 7 1
7 9 1
8 10 1
9 11 2
10 12 1
11 15 1
Details:
print (df.melt(value_name='value'))
variable value
0 B 2
1 B 11
2 B 12
3 B 5
4 C 2
5 C 0
6 C 10
7 C 9
8 D 4
9 D 5
10 D 1
11 D 7
12 E 11
13 E 3
14 E 11
15 E 15
One 11 for index 14 is removed:
print (df.melt(value_name='value').drop_duplicates())
variable value
0 B 2
1 B 11
2 B 12
3 B 5
4 C 2
5 C 0
6 C 10
7 C 9
8 D 4
9 D 5
10 D 1
11 D 7
12 E 11
13 E 3
15 E 15
If want pure python solution:
from collections import Counter
L = sorted(Counter([y for x in df.T.values for y in set(x)]).items())
df1 = pd.DataFrame(L, columns=['value','count'])
print (df1)
value count
0 0 1
1 1 1
2 2 2
3 3 1
4 4 1
5 5 2
6 7 1
7 9 1
8 10 1
9 11 2
10 12 1
11 15 1