Sorting data in DataFrame Pandas

Question

I obtained the following DataFrame:

    Dis        System_num  Energy
0   0.9           1       -2.3108
1   0.7           1       11.8735
2   1.2           1       -2.3408
3   2.0           1       -0.3485
4   2.0           2       -0.9379
5   0.7           2       7.4776
6   1.5           2       -2.2877
7   0.9           2       -4.1789
8   2.0           3       -3.6596
9   1.0           3       -18.4582
10  0.9           3       -16.2202
11  0.7           3       16.6290

I want to sort values (ascending) in the Dis column for each number in System_num, I mean:

0   0.7           1       11.8735
1   0.9           1       -2.3108
2   1.2           1       -2.3408
3   2.0           1       -0.3485
4   0.7           2       7.4776
5   0.9           2       -4.1789
6   1.5           2       -2.2877
7   2.0           2       -0.9379
8   0.7           3       16.6290
8   0.9           3       -16.2202
10  1.0           3       -18.4582
11  2.0           3       -3.6596

piRSquared · Accepted Answer · 2016-12-01 05:36:45Z

1

use sort_values with System_num as the first column to sort by

df.sort_values(['System_num', 'Dis'])

Another way to do it without sorting the System_num column

setup

df = pd.DataFrame([
        [  2.    ,   2.    ,  -0.9379],
        [  0.7   ,   2.    ,   7.4776],
        [  1.5   ,   2.    ,  -2.2877],
        [  0.9   ,   2.    ,  -4.1789],
        [  0.9   ,   1.    ,  -2.3108],
        [  0.7   ,   1.    ,  11.8735],
        [  1.2   ,   1.    ,  -2.3408],
        [  2.    ,   1.    ,  -0.3485],
        [  2.    ,   3.    ,  -3.6596],
        [  1.    ,   3.    , -18.4582],
        [  0.9   ,   3.    , -16.2202],
        [  0.7   ,   3.    ,  16.629 ]
    ], columns=['Dis', 'System_num', 'Energy'])

df.groupby('System_num', sort=False) \
    .apply(pd.DataFrame.sort_values, by='Dis') \
    .reset_index(drop=True)

edited Dec 1, 2016 at 5:36

answered Dec 1, 2016 at 5:29

piRSquared

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1 Comment

Monica Over a year ago

Thank you! It was very helpful!

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