Thanks for the feedback!

1. You can count on indexing if the is_unique flag is checked beforehand. The way pandas does indexing seems to be both clear to the user and powerful. It seems clear because indexing is the result of a Cartesian product after filtering for matching values. It is powerful because it allows indexing as complex as SQL INNER JOIN, which covers the trivial case of unique elements. For example, the following operation

```python import pandas as pd

df = pd.DataFrame(data=[0, 1, 2], index=list("aab")) print(df.loc[list("ab")])

0

a 0

a 1

b 2

```

is an INNER JOIN between the two indexes

INNER((a, b) x (a, a, b)) = INNER(aa, aa, ab, ba, ba, bb) = (aa, aa, bb)

Another example:

```python import pandas as pd

df = pd.DataFrame(data=[0, 1], index=list("aa")) print(df.loc[list("aa")])

0

a 0

a 1

a 0

a 1

```

is again an INNER JOIN between the two indexes

INNER((a, a) x (a, a)) = INNER(aa, aa, aa, aa) = (aa, aa, aa, aa)

1. Assume a bidimensional array with the following indexing:

0 1 a ! @ a # $

This translate into an unidimensional index: (a, 0), (a, 1), (a, 0), (a, 1). As such, it can be treated as usual. Assume you index the above matrix using [('a', 0), ('a', 0)]. This implies

INNER( ((a, 0), (a, 0)) x ((a, 0), (a, 1), (a, 0), (a, 1)) ) = INNER( (a,0)(a,0), (a,0)(a,1), (a,0)(a,0), (a,0)(a,1), (a,0)(a,0), (a,0)(a,1), (a,0)(a,0), (a,0)(a,1) ) = ((a,0)(a,0), (a,0)(a,0), (a,0)(a,0), (a,0)(a,0))

Converting it back to the matricial representation:

0 0 a ! ! a # #

In summary, my suggestion is to consider the possibility of defining indexing B by using A (i.e., B.loc(A)) as a Cartesian product followed by match filtering. Or in SQL terms, an INNER JOIN.

The multi-dimensional indexing, as far as I can see, can always be transformed into the uni-dimensional case and treated as such.