Enhance `read_csv` to handle columns with year, month, day without the need for a `date_parser`.   
It would be nice to have `read_csv` handle columns with `year`, `month`, `day`, `hour` without the need for a `date_parser`.   

Seems `to_datetime` already has this capability:
```python
import pandas as pd
df = pd.DataFrame({'year': [2015, 2016],
                'month': [2, 3],
                'day': [4, 5],
                'hour': [1, 23],
                'minute': [30, 45]})
df = pd.to_datetime(df)
```
producing:
```
0   2015-02-04 01:30:00
1   2016-03-05 23:45:00
dtype: datetime64[ns]
```
But the same thing using `read_csv` does not work:
```python
import pandas as pd
from pandas.compat import StringIO

csv = '''Year, Month, Day, Hour, Minute
2015, 2, 4,  1, 30
2016, 3, 5, 23, 45
'''
df = pd.read_csv(StringIO(csv), skipinitialspace=True,
                 parse_dates={'time':['Year','Month','Day','Hour','Minute']},
                 index_col='time')
```
producing:
```
time
2015 2 4 1 30
2016 3 5 23 45
```

To get `read_csv` to work, we have to use a `date_parser`, like:

```python
def dateparse(x):
    return pd.datetime.strptime(x, '%Y %m %d %H %M')

df = pd.read_csv(StringIO(csv), skipinitialspace=True,
                 parse_dates={'time':['Year','Month','Day','Hour', 'Minute']},
                 index_col='time', date_parser=dateparse)
```
which produces:
```
time
2015-02-04 01:30:00
2016-03-05 23:45:00
```