At first, I write a small example of to lists:

```
F = [[1,2,3],[3,2,7],[4,4,1],[5,6,3],[1,3,7]] # (1*5) 5 lists
S = [[1,3,7],[6,8,1],[3,2,7]] # (1*3) 3 lists
```

I want to get Boolean matrix for the same 'list's in two F and S:

```
[False, True, False, False, True] # (1*5) 5 Booleans for 5 lists of F
```

By using `IM = reduce(np.in1d, (F, S))`

it gives results for each number in each lists of F:

```
[ True True True True True True False False True False True True
True True True] # (1*15)
```

By using `IM = reduce(np.isin, (F, S))`

it gives results for each number in each lists of F, too, but in another shape:

```
[[ True True True]
[ True True True]
[False False True]
[False True True]
[ True True True]] # (5*3)
```

The true result will be achieved by code `IM = [i in S for i in F]`

for the example lists, but when I'm using this code for my two main bigger numpy arrays of lists:

https://drive.google.com/file/d/1YUUdqxRu__9-fhE1542xqei-rjB3HOxX/view?usp=sharing

numpy array: 3036 lists

https://drive.google.com/file/d/1FrggAa-JoxxoRqRs8NVV_F69DdVdiq_m/view?usp=sharing

numpy array: 300 lists

It gives wrong answer. For the main files it must give 3036 Boolean, in which 'True' is only 300 numbers. I didn't understand why this get wrong answers?? It seems it applied only on the 3rd characters in each lists of F. It is preferred to use reduce function by the two functions, np.in1d and np.isin, instead of the last method. How could to solve each of the three above methods??