Question: I have list of Active Directory Users's Guid, I need to load a set of properties of those users for a specific reporting. To do this, if we make a bind for each Guid, then it will be a costly one. Whether with DirectorySearcher, can we provide multiple Guid(say 1000) as filter and load the properties?.

Answer: 
I found a faster way to solve the problem, at least on realistically large datasets using:
`df.set_index(KEY).to_dict()[VALUE]`

Proof on 50,000 rows:

```
df = pd.DataFrame(np.random.randint(32, 120, 100000).reshape(50000,2),columns=list('AB'))
df['A'] = df['A'].apply(chr)

%timeit dict(zip(df.A,df.B))
%timeit pd.Series(df.A.values,index=df.B).to_dict()
%timeit df.set_index('A').to_dict()['B']

```

Output:

```
100 loops, best of 3: 7.04 ms per loop  # WouterOvermeire
100 loops, best of 3: 9.83 ms per loop  # Jeff
100 loops, best of 3: 4.28 ms per loop  # Kikohs (me)

```
```
dict (zip(data['position'], data['letter']))

```

this will give you:

```
{1: 'a', 2: 'b', 3: 'c', 4: 'd', 5: 'e'}

```