How to check column value among all others values in same column in Pandas Data frame?

Question

I have a pandas data frame which having three columns. Normally for the Loan type it has 5 values. Let's say Conso, Immo, Pro, Autre, Tous. For this data frame only contain loan type 'Immo'. (At the beginning we don't know what the Loan type is). How do I check what the loan type is among all this loan type?

CodeProduit  LoanType   Year
301          Immo       2003
301          Immo       2004
301          Immo       2005
301          Immo       2006
...          ...        ....
301          Immo       2017

def check_type_pret(p):
    if p == 'Immo':
         return p
    elif p == 'Conso':
        return p
    elif p == 'Pro':
        return p
    elif p == 'Autres':
        return p
    elif p == 'Tous':
        return p
    else:
        return 0



df1['Answer']=df1.LoanType.map(check_type_pret)

As a output I'm getting 0 for Answer Column. How do I get expected out put as I explained?

Answer 1

If want check if exist all values in L in column LoanType use:

L = ['Immo', 'Conso', 'Pro', 'Autres', 'Tous']
a = all([(df['LoanType'] == x).any() for x in L])
print (a)
False

Or:

s = set(['Immo', 'Conso', 'Pro', 'Autres', 'Tous'])
a = s.issubset(set(df['LoanType'].tolist()))
print (a)
False

EDIT:

If your solution return 0 there is no match.

I guess some traling whitespaces, so need remove them first by strip :

df1['Answer'] = df1.LoanType.str.strip().map(check_type_pret)

Another solution with numpy.where or where and condition with isin :

print (df1)
   CodeProduit LoanType  Year
0          301    Immo1  2003
1          301    Conso  2004
2          301      Pro  2005
3          301   Autres  2006
4          301     Tous  2017

df1.LoanType = df1.LoanType.str.strip()

L = ['Immo', 'Conso', 'Pro', 'Autres', 'Tous']
df1['Answer'] = np.where(df1.LoanType.isin(L), df1.LoanType, 0)
#another solution
#df1['Answer'] = df1.LoanType.where(df1.LoanType.isin(L),  0)
print (df1)
   CodeProduit LoanType  Year  Answer
0          301    Immo1  2003       0
1          301    Conso  2004   Conso
2          301      Pro  2005     Pro
3          301   Autres  2006  Autres
4          301     Tous  2017    Tous