Adding a date_unit option to .read_csv to make parse_dates more flexible.
When reading in a CSV file, you can easily convert a column into the `datetime` format using:

```
pd.read_csv(filepath, parse_dates=['TIMESTAMP'])
```

If the column holding the original time information uses an unsupported format, you can currently pass a parser function to it too, for example if the timestamp is "unixtime in milliseconds":

```
convert = lambda x: datetime.datetime.fromtimestamp(float(x) / 1e3)
df = pd.read_csv(filepath, parse_dates=['TIMESTAMP'], date_parser=convert)
```

However, if the data is already read in, you can also do such conversions easily with `.to_datetime()` which provides a `unit` parameter for the most common cases:

```
pd.to_datetime(df['UNIXTIME'], unit='ms')
```

I think it would be quite useful to have this `unit` parameter in `.read_csv()` too, calling it `date_unit` to avoid confusion. The example with the extra conversion function would then look like:

```
df = pd.read_csv(filepath, parse_dates=['TIMESTAMP'], date_unit='ms')
```

Or using a list of units to allow multiple columns with different units:

```
df = pd.read_csv(filepath, parse_dates=['TIMESTAMP', 'ANOTHER_TIMESTAMP], date_unit=['ms', 'us'])
```
