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)

```
In Python 3.6 the fastest way is still the WouterOvermeire one. Kikohs' proposal is slower than the other two options.

```
import timeit

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

timeit.Timer('dict(zip(df.A,df.B))', setup=setup).repeat(7,500)
timeit.Timer('pd.Series(df.A.values,index=df.B).to_dict()', setup=setup).repeat(7,500)
timeit.Timer('df.set_index("A").to_dict()["B"]', setup=setup).repeat(7,500)

```

Results:

```
1.1214002349999777 s  # WouterOvermeire
1.1922008498571748 s  # Jeff
1.7034366211428602 s  # Kikohs

```