Create boolean mask for every unique element in array

Question

I have a list with numbers in it. I want to create a bool mask of this list (or array, doesn't matter) for every unique element of this list.

In the example below, I want to create four masks of length len(labels). The first mask has True at position i, if labels[i]==0, the second one has True at position i, if labels[i]==1 etc.

I tried to do this with pandas and the .isin method in a loop. However, this is too slow for my purpose since this is called many times in my algorithm and the list of labels can be very long so that the loop is not effective. How can I make this faster?

labels = [0,0,1,1,3,3,3,1,2,1,0,0]
d = dict()
y = pd.Series(labels)
for i in set(labels):
    d[i] = y.isin([i])

Answer 1

Method 1

Using list and set

In [989]: {x: [x==l for l in labels] for x in set(labels)}
Out[989]:
{0: [True, True, False, False, False, False, False, False, False, False, True, True],
 1: [False, False, True, True, False, False, False, True, False, True, False, False],
 2: [False, False, False, False, False, False, False, False, True, False, False, False],
 3: [False, False, False, False, True, True, True, False, False, False, False, False]}

If you want it as dataframe

In [994]: pd.DataFrame({x: [x==l for l in labels] for x in set(labels)})
Out[994]:
        0      1      2      3
0    True  False  False  False
1    True  False  False  False
2   False   True  False  False
3   False   True  False  False
4   False  False  False   True
5   False  False  False   True
6   False  False  False   True
7   False   True  False  False
8   False  False   True  False
9   False   True  False  False
10   True  False  False  False
11   True  False  False  False

---

Method 2

Using pd.get_dummies, if you anyway a series you can

In [997]: pd.get_dummies(y).astype(bool)
Out[997]:
        0      1      2      3
0    True  False  False  False
1    True  False  False  False
2   False   True  False  False
3   False   True  False  False
4   False  False  False   True
5   False  False  False   True
6   False  False  False   True
7   False   True  False  False
8   False  False   True  False
9   False   True  False  False
10   True  False  False  False
11   True  False  False  False

---

Benchmarks

Small

In [1002]: len(labels)
Out[1002]: 12

In [1003]: %timeit pd.get_dummies(y).astype(bool)
1000 loops, best of 3: 476 µs per loop

In [1004]: %timeit pd.DataFrame({x: [x==l for l in labels] for x in set(labels)})
1000 loops, best of 3: 580 µs per loop

In [1005]: %timeit pd.DataFrame({x : (y == x) for x in y.unique()})
1000 loops, best of 3: 1.15 ms per loop

Large

In [1011]: len(labels)
Out[1011]: 12000

In [1012]: %timeit pd.get_dummies(y).astype(bool)
1000 loops, best of 3: 875 µs per loop

In [1013]: %timeit pd.DataFrame({x: [x==l for l in labels] for x in set(labels)})
100 loops, best of 3: 4.97 ms per loop

In [1014]: %timeit pd.DataFrame({x : (y == x) for x in y.unique()})
1000 loops, best of 3: 1.32 ms per loop