ENH: Allow selection of reference values in get_dummies
When one-hot encoding a pandas categorical column, with drop_first = True, there is no control over which value is dropped. So if I need to specify the reference value to drop, I can't use drop_first. I have to manually drop the columns that have been unnecessarily created.

I would like to enhance the get_dummies method to be able to specify for each column in 'columns'  a reference value to be used as the dropped column. For example:

```python

df = pd.DataFrame(data={'ageband': np.random.choice(range(20,80,10),100),
                        'country':np.random.choice(['uk', 'nl', 'be', 'fr'],100)}).astype('category')
ref_values = {'ageband': '40', 'country':'be'}

df_encoded = pd.get_dummies(df, columns=['ageband', 'country'],  ref_values= ref_values)
```

for each categorical column specified in the new ref_values parameter: 
if value does exists, use that as the reference value 
if value does not exist, proceed with normal behaviour - i.e. drop the (lexical) first (or ignore with warning?) 


I don't think there are any API breaking implications?

The achieve this now, without the enhancement, I have to do something like : 

```python
df = pd.DataFrame(data={'ageband': np.random.choice(range(20,80,10),100),
                        'country':np.random.choice(['uk', 'nl', 'be', 'fr'],100)}).astype('category')

prefix_sep = ':'  # say
df = pd.get_dummies(df, columns=['ageband', 'country'], prefix_sep=prefix_sep)
ref_values = {'ageband': 40, 'country': 'be'}

columns_to_drop = [ col + prefix_sep + str(val) for col, val in ref_values.items()]
df.drop(columns=columns_to_drop, inplace=True, errors='ignore')
# additional code would be required to handle errors such as reference value not present
```

I am willing to have a go at this if it is accepted as an enhancement 

