Logical Operation on two different dataframes-CodePudding

I have 3 dataframes of the sizes 1x100 , 20x100 and 20x100.

a1    b1 b2 .... b20    c1 c2 .... c20
0      1  0 ....  0      1  0 ....  0
1      0  1 ....  1      0  0 ....  0
0      0  0 ....  0      1  1 ....  1
1      0  0 ....  0      0  0 ....  1
0      1  1 ....  1      1  0 ....  0
1      0  0 ....  1      1  0 ....  0

I want to run a logical operation as ((a1==0) & (b1==1) & (c1==1)).astype(int) then ((a1==0) & (b2==1) & (c2==1)).astype(int) and so on until ((a1==0) & (b20==1) & (c20==1)).astype(int) and store in a new dataframe.

The final output dataframe should consist of 20 columns.

CodePudding user response：

Because there are different columns names is necessary convert DataFrames to numpy arrays:

df = (df2.eq(1) & df3.eq(1).to_numpy() & df1.eq(0).to_numpy()).astype(int)
print (df)
   b1  b2  b20
0   1   0    0
1   0   0    0
2   0   0    0
3   0   0    0
4   1   0    0
5   0   0    0

CodePudding user response：

The code below can give you the indices that satisfy the conditions for all three dataframes for each iteration in columns,

[{f"{i}": list(filter(lambda item:(a["a1"][item] == 1) & (b[f"b{i}"][item] == 1) & (c[f"c{i}"][item] == 1), range(100)))} for i in range(1, 21)]

Subset of the output -

[{'1': [1, 9, 14, 18, 23, 24, 28, 33, 45, 51, 53, 77, 88, 89, 90]},
 {'2': [5, 17, 27, 32, 44, 56, 73, 76, 79, 88, 89, 92]},
 {'3': [9, 14, 24, 25, 43, 46, 53, 55, 73, 76, 86, 91, 92, 94, 96]},
 {'4': [17, 20, 22, 24, 26, 34, 46, 65, 73, 76, 77, 81, 88, 92]},
 {'5': [9, 17, 23, 34, 36, 44, 48, 49, 75, 76, 88, 91, 94, 95]}]

I had fun solving this question. Thanks for asking it!