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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!

xarc
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  • Does this answer your question? [how to round/remove traling ".0" zeros in pandas column?](https://stackoverflow.com/questions/42403907/how-to-round-remove-traling-0-zeros-in-pandas-column) – Irfanuddin Jan 16 '22 at 18:13
  • That question is similar, but it is not the same, note that here we want to keep the float value, only eliminate the trailing zeros where it corresponds and round to the integer value where it corresponds as well – xarc Jan 16 '22 at 18:17
  • It's unclear if you're looking to modify the _display_ behaviour or modify the _actual_ data in the DataFrame. Mixed precision is not allowed within numeric dtypes. Converting to object (str) could work, however, you'd lose the ability to use them as numbers. – Henry Ecker Jan 16 '22 at 18:32
  • @HenryEcker, i'm trying to make both, modify the actual data, and the display behavior to make it more understandable just by looking at it – xarc Jan 16 '22 at 18:51

2 Answers2

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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.

sk _47
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here my solutions after trying a while:

df['B'] = df['B'].astype(float).round(2).map("{:,.2f}".format)
df[['C', 'D', 'E']] = df[['C', 'D', 'E']].astype(float)
xarc
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