dict_sample={'HR':'Human Resource','FIN':'Finance','LG':'Legal'}

var=[]
 
for i in df["Gender"]:
    for x in df["Dept"]:
 
        if i[2].lower() in 'm' and x[2:4].lower() in 'mk':

data = {'name': ['John', 'Aaron', 'Anie', 'Nancy', 'Steve'],

data = {'id': ['BP001', ' Ba002', 'CA003', 'EF004', 'Pa005','EP001','EM050','XP095']}

Value $Broad_Funds
'DOMCSP','DOMRTS','DOMRTP'= " Domestic C'wealth Sup"
'DOMFEE'="Domestic Fee-Paying"
'INTON','INTRTP' ="

broad_format={"DOMCSP":"Domestic Common Wealth Supported","DOMFEE":"Domestic Fee Paying",
             "DOMRTS":"Domestic Research Training Scheme",
           "INTOFF":"International Offshore","INTON":"International"}

data = {'funds': ['DOMCSP','DOMCSP','DOMFEE','DOMFEE','INTON','INTON','INTOFF','INTOFF'], 
         'risk': [1, 2, 3, 4, 5,6,7,100]}
df = pd.DataFrame(data, columns = ['funds', 'risk',])
df

data = {'id': ['aa11bc', 'bb22cd', 'cc33ef', 'dd44gh', 'ee55ij','ff66kl','gg77mn','hh88op'], 
        'direction': ["north, south, east, west", "north, south, east, west", "north, south/, *east, \west%", "north, south, east, west",
                      "north, south, east,

heros['var1'] = heros['age'] * heros['salary']

heros.loc[((heros['name'] == 'spiderman') | (heros['name'] == 'antman')) & 
          ((heros['salary'] > 1000) & (heros['salary'] < 2000)) &
          
          ((heros['city'] != 'boston') |
          (heros['city'] != 'dubai')),'var1']

def fun_check(event):
    
    if name in ("spiderman","antman") and salary in range(1000,2000) and city not in ("boston","dubai"):
        return age+salary
    else:
        return age*salary

heros=heros.assign(new_col=heros.apply(fun_check(event),axis=1))

if name in ("spiderman","antman") and salary in range(1000,2000) and city not in ("boston","dubai"):
    new_column=age+salary