python - 获取与 SQL rank 不同的 pandas dataframe rank answer

Question

我在学习 SQL 中的排名函数，发现它使用的排名与 pandas 方法不同。如何得到相同的答案？

提问链接:https://www.windowfunctions.com/questions/ranking/1

SQL 代码

select rank() over (order by weight desc) as ranking, weight, name from cats order by ranking, name

我的 Pandas 代码

df[['weight','name']].assign(ranking=df['weight'].rank(method='dense',ascending=False)).sort_values('weight',ascending=False)

# I am getting a different answer than SQL
# How to get the same answer?

数据

df = pd.DataFrame({'name': ['Molly', 'Ashes', 'Felix', 'Smudge', 'Tigger', 'Alfie', 'Oscar', 'Millie', 'Misty', 'Puss', 'Smokey', 'Charlie'],
          'breed': ['Persian', 'Persian', 'Persian', 'British Shorthair', 'British Shorthair', 'Siamese', 'Siamese', 'Maine Coon', 'Maine Coon', 'Maine Coon', 'Maine Coon', 'British Shorthair'],
          'weight': [4.2, 4.5, 5.0, 4.9, 3.8, 5.5, 6.1, 5.4, 5.7, 5.1, 6.1, 4.8],
          'color': ['Black', 'Black', 'Tortoiseshell', 'Black', 'Tortoiseshell', 'Brown', 'Black', 'Tortoiseshell', 'Brown', 'Tortoiseshell', 'Brown', 'Black'],
          'age': [1, 5, 2, 4, 2, 5, 1, 5, 2, 2, 4, 4]})

我的输出

    weight  name    ranking
6   6.1 Oscar   1.0
10  6.1 Smokey  1.0
8   5.7 Misty   2.0 # this should be 3 and so on
5   5.5 Alfie   3.0
7   5.4 Millie  4.0
9   5.1 Puss    5.0
2   5.0 Felix   6.0
3   4.9 Smudge  7.0
11  4.8 Charlie 8.0
1   4.5 Ashes   9.0
0   4.2 Molly   10.0
4   3.8 Tigger  11.0

Answer 1

正如评论中所建议的，我们可以使用这种方法在 SQL 和 pandas 中得到相同的答案:

df[['weight','name']].assign(ranking=df['weight'].rank(method='min',ascending=False)).sort_values('weight',ascending=False)