python pandas select rows where two columns are (not) equal

Question

hsp.loc[hsp[\'Len_old\'] == hsp[\'Len_new\']]

I try this code, it\'s working.

But I tried these three

hsp.loc[hsp[\'Type_o         


        

Answer 1

Ways to be confused by == versus != when comparing pd.Series

As expected

df[['Len_old', 'Len_new']].assign(NE=df.Len_old != df.Len_new)

   Len_old  Len_new     NE
0       15       15  False
1       12       12  False
2       10        8   True
3        4        5   True
4        9       10   True

---

But if one of the column's values were strings!

df[['Len_old', 'Len_new']].assign(NE=df.Len_old.astype(str) != df.Len_new)

   Len_old  Len_new    NE
0       15       15  True
1       12       12  True
2       10        8  True
3        4        5  True
4        9       10  True

---

Make sure both are the same types.

Answer 2

Use the complement operator ~

hsp.loc[~(hsp['Type_old'] == hsp['Type_new'])]

which gives:

   id Type_old Type_new  Len_old  Len_new
1   2      Num     Char       12       12
2   3     Char      Num       10        8

When dealing with Boolean operations, the complement operator is a handy way to invert True with False