How remove zeros and round multiple columns in a pandas dataframe

I have a pandas dataframe like this:

df = pd.DataFrame([
        {'A': 'aaa', 'B': 5.56000000, 'C': 0.01000000, 'D': 1.00000000, 'E': 0.00001000},
        {'A': 'bbb', 'B': 0.49000000, 'C': 0.00100000, 'D': 0.01000000, 'E': 0.00010000},
        {'A': 'ccc', 'B': 1.70000000, 'C': 0.10000000, 'D': 0.10000000, 'E': 0.00100000},
        {'A': 'ddd', 'B': 5.35000000, 'C': 0.00001000, 'D': 0.00001000, 'E': 0.01000000},
        {'A': 'eee', 'B': 6.00000000, 'C': 16.00000000, 'D': 0.00000100, 'E': 1.00000000},

    ])   

                A              B               C             D             E
0             aaa     5.56000000      0.01000000    1.00000000    0.00001000
1             bbb     0.49000000      0.00100000    0.01000000    0.00010000
2             ccc     1.70000000      0.10000000    0.10000000    0.00100000 
3             ddd     5.35000000      0.00001000    0.00001000    0.01000000 
4             eee     6.00000000     16.00000000    0.00000100    1.00000000

I need remove trailing zeros and at the same time, round values after the comma for their integer values

Getting a dataframe like this:

                A        B          C           D          E
0             aaa     5.56       0.01           1    0.00001
1             bbb     0.49      0.001        0.01     0.0001
2             ccc     1.70        0.1         0.1      0.001 
3             ddd     5.35    0.00001     0.00001       0.01
4             eee     6.00         16    0.000001          1

Note: column B round 2 value after comma and columns C, D, E trailing zeros and round as appropriate

How i can make those transformation? y try the following solution:

 df[['C', 'D', 'E']] = df[['C', 'D', 'E']].astype(str).apply(lambda x: x.str.rstrip('0'))

but i get this:

     A         B        C        D         E
0    aaa    5.56     0.01       1.     1e-05
1    bbb    0.49    0.001     0.01    0.0001
2    ccc     1.7      0.1      0.1     0.001
3    ddd    5.35    1e-05    1e-05      0.01
4    eee     6.0      16.    1e-06        1.

Thanks in advance!

Solution 1:

If you are doing it just to take the final output, then this works.

df['temp'] = df['B'].astype('float')
df['B'] = df['temp'].astype('string')

But surely, if you want to do more operations on it, you will have to change the type again and that can mess it up.