Create pandas columns based on multiple conditions

Create columns based on other values in the row

 df1['Verschil']=df1.apply(lambda x : x['aantal_new'] - x['aantal_old'], axis=1)

Create pandas columns based on multiple conditions

With pandas and numpy we barely have to write our own functions, especially since our own functions will perform slow because these are not vectorized and pandas + numpy provide a rich pool of vectorized methods for us.

In this case your are looking for np.select since you want to create a column based on multiple conditions:

definieer 2 lijsten van dezelfde lengte
1 lijst met de where clauses
1 lijst met de values


lstValues=[(df1['aantal_new'] - df1['aantal_old']) /(df1['aantal_old']),'99999999999']

lstConditions = [df1['aantal_old'].notna() & df1['aantal_new'].notna(), df1['aantal_old'].isna() &  
df1['aantal_new'].isna()]
 
df1['Stijging2']= np.select(lstConditions,lstValues,'99')