1

## finding all columns that have nan:

droping_list_all=[]
for j in range(0,7):
    if not df.iloc[:, j].notnull().all():
        droping_list_all.append(j)        
        #print(df.iloc[:,j].unique())
droping_list_all

2

# filling nan with mean in any columns

for j in range(0,7):        
        df.iloc[:,j]=df.iloc[:,j].fillna(df.iloc[:,j].mean())

3

# another sanity check to make sure that there are not more any nan
df.isnull().sum()