a=pd.DataFrame({'a1':[1,2,3,4],'a2':[5,6,7,8]})
c=pd.DataFrame({'c1':[True,False,True,True],'c2':[True,False,False,True]})
How can I get the index of the elements in columns a1 and a2 which are True in columns c1 and c2 respectively?
The index of a1 should be [0,2,3] and the index of a2 [0,3]. The result could be a list of indexes like [[0,2,3],[0,3]].
I think you need where:
c.columns = a.columns
df1 = a.where(c)
idx = [df1[x].dropna().index.tolist() for x in df1]
print (idx)
[[0, 2, 3], [0, 3]]
Another solution:
c.columns = a.columns
idx = list(a.where(c).stack().reset_index()
.groupby('level_1')['level_0'].apply(list))
print (idx)
[[0, 2, 3], [0, 3]]
It isn't clear what you want exactly.
This is an approach using stack
a.stack().index[c.stack().values].to_series()
0 a1 (0, a1)
a2 (0, a2)
2 a1 (2, a1)
3 a1 (3, a1)
a2 (3, a2)
dtype: object
If you want just the list of index values
a.index.values[c.values.any(1)]
array([0, 2, 3])
