I have a csv file that looks like

```
Year Mo Da (01,52) 
1950  1    1    0.00    
1950  1    2    0.00    
1950  1    3    0.05    
1950  1    4    0.00    
1950  1    5    0.07    
1950  1    6    0.07

```

and I would like transform it into a dataframe with 2 columns: a datetime column of YYYYMMDD (using the "Year", "Mo", and "Da" columns in the raw data) and then the rainfall at the grid point (e.g. 01, 52) as the second column. 

A desired output would be:

```
Datetime Rainfall
19500101 0.00
19500102 0.00
19500103 0.05

```

I am stuck on two issues: appropriately accounting for the whitespace during the read-in and properly using `parse_dates`. 

The simple read-in command:

```
df = pd.read_csv(csv_fl)

```

Almost correctly reads in the headers, but splits the (01,52) into separate columns, yielding a trailing NaN, which shouldn't be there. 

```
     Year Mo Da (01  52)
0  1950  1    1    0.00  NaN

```

And trying to parse the dates using 

```
df = pd.read_csv(csv_fl, parse_dates={'Datetime':[0,1,2]}, index_col=0)

```

leads to an IndexError

```
    colnames.append(str(columns[c]))
 IndexError: list index out of range

```

Any guidance is much appreciated.