I currently have some time series data that I applied a rolling mean on with a window of 17520.

Thus before the head of my data looked like this:

```
SETTLEMENTDATE NSW DEMAND ... VIC DEMAND VIC RRP
0 2006/01/01 00:30:00 8013.27833 ... 5657.67500 20.03
1 2006/01/01 01:00:00 7726.89167 ... 5460.39500 18.66
2 2006/01/01 01:30:00 7372.85833 ... 5766.02500 20.38
3 2006/01/01 02:00:00 7071.83333 ... 5503.25167 18.59
4 2006/01/01 02:30:00 6865.44000 ... 5214.01500 17.53
```

And now it looks like this:

```
SETTLEMENTDATE NSW DEMAND ... VIC DEMAND VIC RRP
0 2006/01/01 00:30:00 NaN ... NaN NaN
1 2006/01/01 01:00:00 NaN ... NaN NaN
2 2006/01/01 01:30:00 NaN ... NaN NaN
3 2006/01/01 02:00:00 NaN ... NaN NaN
4 2006/01/01 02:30:00 NaN ... NaN NaN
```

How can I get it so that my data only begins, when there is not a NaN? (also making sure that the date matches)

This is the code I am using for rolling mean:

```
import pandas as pd
data = pd.read_csv("master_file.csv")
data['NSW DEMAND'] = data['NSW DEMAND'].rolling(17520,min_periods=17520).mean()
data['QLD DEMAND'] = data['QLD DEMAND'].rolling(17520,min_periods=17520).mean()
data['SA DEMAND'] = data['SA DEMAND'].rolling(17520,min_periods=17520).mean()
data['TAS DEMAND'] = data['TAS DEMAND'].rolling(17520,min_periods=17520).mean()
data['VIC DEMAND'] = data['VIC DEMAND'].rolling(17520,min_periods=17520).mean()
data['NSW RRP'] = data['NSW RRP'].rolling(17520,min_periods=17520).mean()
data['QLD RRP'] = data['QLD RRP'].rolling(17520,min_periods=17520).mean()
data['SA RRP'] = data['SA RRP'].rolling(17520,min_periods=17520).mean()
data['TAS RRP'] = data['TAS RRP'].rolling(17520,min_periods=17520).mean()
data['VIC RRP'] = data['VIC RRP'].rolling(17520,min_periods=17520).mean()
```

EDIT:

what my first row (excluding the headings) is:

what my first row (excluding the headings) should be: