pandas return rows when certain columns are null or zero

We could use isin + all:

out = df[df[['City','fas']].isin(['NaN', 0]).all(axis=1)]

Output:

   Name City  fas  san
2  shan    0    0    0
3   van  NaN  NaN  saf

This will replace all the NaN in your database as np.nan (if they aren't already didn't know if that was just for us to see as an example), then it will replace all np.nan with a 0 then do a lookup which finds only results were both fas and san are 0

df = pd.DataFrame(data=[['Stephen','Oslo','NaN','NaN'],['Jane','Stockholm','osgar',0],['shan',0,0,0],['van','NaN','NaN','saf']],columns=['Name','City','fas','san'])
df = df.apply(lambda x : x.replace('NaN', np.nan))
df.fillna(0, inplace = True)
df.loc[(df['fas'] == 0) & (df['san'] == 0)]

We can also use 'and' 'or' combination to filter

df[((df["City"] == 0)|(df["City"] == 'NaN')) & ((df["fas"] == 0)|(df["fas"] == 'NaN'))]