Pandas Use Index to Replace Row Values with Zero - pandas

Question

I need to apply an index from one dataframe "df" (not shown) with a condition True/False of whether the row value in df contains NaN. I have found that index and it is called "rowswithnan" and is shown below is a series. I need to use this index to set the row values of a different dataframe "df2" to zero (0). I have tried lots of things and am getting different errors.
the series rowswithnan index looks like this:
0 False
1 True
2 True
3 False
and df2 looks like this:
plant_name power_kwh hour day month year
0 AREC 32963.4 23 31 12 2020
1 AREC 35328.2 22 31 12 2020
2 AREC 37523.6 21 31 12 2020
3 AREC 36446.0 20 31 12 2020
After using the index of "rowswithnan", I need df2 to look like this with the "zero" replacements in the column "power_kwh":
df2
plant_name power_kwh hour day month year
0 AREC 32963.4 23 31 12 2020
1 AREC 0 22 31 12 2020
2 AREC 0 21 31 12 2020
3 AREC 36446.0 20 31 12 2020
Thank you.

Answer 1

Since you have a series that describes where NaN are in the dataframe, I'm guessing there's a simpler way to handle this whole situation. However, here's answer to your request in a comment to drop the rows where the index of the series rowswithnan is False.
First make that series a column of your dataframe:
df['contains_nan'] = `rowswithnan`
Then use boolean filtering:
df = df.loc[~df.contains_nan]
To set the values power_kwh to zero instead of dropping the rows, do this:
df.loc[df.contains_nan, 'power_kwh'] = 0