python - Pandas 0.24 replace regex issue

Question

With pandas 0.19.2 python 3.6.0 DataFrame.replace with a dictionary acts on substrings (like "find"), and so does Series.replace. Pandas 0.24.0 python 3.6.8 seems to act on the entire string (like "match") for DataFrames, and still act on substrings for Series (like "find").

df = pd.DataFrame({'c1':['AD','BD'],'c2':['AD','BD']})
print(df)
print(df.replace(to_replace={'c1':{r'D': ''}, 'c2':{r'BD': ''}},regex=True))
print(df.replace(to_replace={r'D': ''},regex=True))
print(df['c1'].replace(to_replace=r'D', value='',regex=True))

Pandas 0.19.2 produces (I added some blank lines for legibility):

   c1  c2
0  AD  AD
1  BD  BD

  c1  c2
0  A  AD
1  B    

  c1 c2
0  A  A
1  B  B

0    A
1    B
Name: c1, dtype: object

With Pandas 0.24.0:

   c1  c2
0  AD  AD
1  BD  BD

   c1  c2
0  AD  AD
1  BD    

   c1  c2
0  AD  AD
1  BD  BD

0    A
1    B
Name: c1, dtype: object

Looks like a pandas bug to me, or am I missing something?

Answer 1

The bug is listed among the Fixed regressions for Pandas 0.24.2:

Fixed regression in DataFrame.replace() where regex=True was only replacing patterns matching the start of the string (GH25259)

As you see, only

print(df.replace(to_replace={'c1':{r'D': ''}, 'c2':{r'BD': ''}},regex=True))
print(df.replace(to_replace={r'D': ''},regex=True))

did not work correctly. Now, the issue is fixed.