基于列值的 Pandas 排序

Question

I have a pandas dataframe like:

I have the data frame as like below one,

Input DataFrame
     id          ratio
 0   1           5.00%
 1   2           9.00%
 2   3           6.00%
 3   2           13.00%
 4   1           19.00%
 5   4           30.00%
 6   3           5.5%
 7   2           22.00%

How can I then group this like

         id          ratio
     0   1           5.00%
     4   1           19.00%
     6   3           5.5%
     2   3           6.00%
     1   2           9.00%
     3   2           13.00%
     7   2           22.00%
     5   4           30.00%

So essentially first looks at the ratio, takes the lowest for that value and groups the rest of the rows for which it has the same id. Then looks for the second lowest ratio and groups the rest of the ids again etc.

Answer 1

First convert your ratio column to numeric.

Then we get the lowest rank per group by using Groupby

Finally we sort based on rank and numeric ratio .

df['ratio_num'] = df['ratio'].str[:-1].astype(float).rank()
df['rank'] = df.groupby('id')['ratio_num'].transform('min')

df = df.sort_values(['rank', 'ratio_num']).drop(columns=['rank', 'ratio_num'])

   id   ratio
0   1   5.00%
1   1  19.00%
2   3    5.5%
3   3   6.00%
4   2   9.00%
5   2  13.00%
6   2  22.00%
7   4  30.00%

Answer 2

With help of pd.Categorical :

d = {'id':[1, 2, 3, 2, 1, 4, 3, 2],
     'ratio': ['5.00%', '9.00%', '6.00%', '13.00%', '19.00%', '30.00%', '5.5%', '22.00%']}

df = pd.DataFrame(d)

df['ratio_'] = df['ratio'].map(lambda x: float(x[:-1]))
df['id'] = pd.Categorical(df['id'], categories=df.sort_values(['id', 'ratio_']).groupby('id').head(1).sort_values(['ratio_', 'id'])['id'], ordered=True)
print(df.sort_values(['id', 'ratio_']).drop('ratio_', axis=1))

Prints:

  id   ratio
0  1   5.00%
4  1  19.00%
6  3    5.5%
2  3   6.00%
1  2   9.00%
3  2  13.00%
7  2  22.00%
5  4  30.00%