I have the following multi index data frame and I want to create a new column that tells me wether a company still exists or not. ID and Year are part of the multi index.
id Year Profit/Loss Total Sales
0 2008 300. 2000.
0 2009 400. 2000.
0 2010 500. 2000.
0 2011 NaN NaN
0 2012 NaN NaN
1 2008 300. 2000.
1 2009 300. 2000.
I would need a function that checks if both columns (Total Sales and Profit/Loss) are NaN in a specific year and if they are return a 0 in the solvency column. If one of them or both have values, then it should return a 1.
Desired output:
id Year Profit/Loss Total Sales Solvency
0 2008 300. 2000. 1
0 2009 400. 2000. 1
0 2010 500. 2000. 1
0 2011 NaN NaN 0
0 2012 NaN NaN 0
1 2008 300. 2000. 1
1 2009 300. 2000. 1
You can use notna to identify the non-NaN columns, aggregate to boolean if any is True per row and convert to integer with astype:
df['Solvency'] = df[['Profit/Loss', 'Total Sales']].notna().any(1).astype(int)
output:
id Year Profit/Loss Total Sales Solvency
0 0 2008 300.0 2000.0 1
1 0 2009 400.0 2000.0 1
2 0 2010 500.0 2000.0 1
3 0 2011 NaN NaN 0
4 0 2012 NaN NaN 0
5 1 2008 300.0 2000.0 1
6 1 2009 300.0 2000.0 1