循环通过 pandas 数据帧以使用 for 循环替换现有值

Question

Problem: I am trying to loop through a dataframe, row by row, by using a for loop. But its not working as desired. I know there are iterrows() and itertuple() by I want to experiment with for loop.

Can you tell me where this is going wrong?

sample data

data3 = {"one":['101', '102', '103' , '104'],
     "two":['101', '105', '106', '104'],
     "three": ['102', '5', '107', '108'],
     "other": ['101', '102', '103' , '104']
     }
df3 = pd.DataFrame(data3)

Goal: check column 'two' by each row, and if a value of column 'two' exists in column 'one' then create a new column 'new_col' with the value 'del'. If the value doesnt exist in column 'one' then create the 'new_col' as 'keep'. For example, if column 'two' has 101, i want to compare it with all the values of column 'one'

my code:

dfToList1 = df3['two'].tolist()
for x in dfToList1:
   if x in df3['one'].values:
       df3['new_col'] = 'del'
   else:
       df3['new_col'] = 'keep'

then I can replace the value in 'two' which matches with 'one' with a string like 'none'

df3.loc[df3['new_col'] == 'del', 'two'] = 'none'

my output:

Ideally in 2nd and 3rd row, 5 and 107 in 'two' doesn't not include in 'one' and therefore new_col in 2nd and 3rd row should have the value 'keep' but I am not getting it.

    one other   three   two new_col
0   101 101     102     101     del
1   102 102       5     105     del
2   103 103     107     106     del
3   104 104     108     104     del

expected output

    one other   three   two  new_col
0   101 101     102     101     del
1   102 102       5     105     keep
2   103 103     107     106     keep
3   104 104     108     104     del

Answer 1

Use np.where :

df3['new_col'] = np.where(df3['two'].isin(df3['one']), 'del', 'keep')

Result:

   one  two three new_col
0  101  101   102     del
1  102  105     5    keep
2  103  106   107    keep
3  104  104   108     del

Answer 2

Use np.where with Series.eq and Series.isin to check.

df3['newcol']=np.where(~df3.two.isin(df3.one),'keep','del')

---

or to select by columns 'one' with any common value with column two:

df3['newcol']=np.where(~df3.one.isin(df3.loc[df3.two.eq(df3.one),'two']),'keep','del')
print(df3)

   one  two three other newcol
0  101  101   102   101    del
1  102  105     5   102   keep
2  103  106   107   103   keep
3  104  104   108   104    del

Details

two_coincident_one=df3.loc[df3.two.eq(df3.one),'two']
print(two_coincident_one)
0    101
3    104
Name: two, dtype: object


~df3.one.isin(two_coincident_one)

0    False
1     True
2     True
3    False
Name: one, dtype: bool