Python 将序列号添加到表中的groupby（）.head（n）表达式

我在熊猫中有一个表达，我按国家对前三个值进行排序：

Country              | Value
---------------------|------
Germany              | 102.1
Germany              | 90.3
Germany              | 44.6
Switzerland          | 59.9
Switzerland          | 35.3
Switzerland          | 21.6

...and so on

我使用

df.groupby（“Country”）.head（3）[[“Country”，“Value”]]

获得了这些数据。现在，我想添加第三列，将国家内的排名与值关联：

Country              | Value  | Rank
---------------------|--------|------
Germany              | 102.1  | 1
Germany              | 90.3   | 2
Germany              | 44.6   | 3
Switzerland          | 59.9   | 1
Switzerland          | 35.3   | 2
Switzerland          | 21.6   | 3

...and so on

我最好怎么做呢？

我相信您需要并且

方法class='dense'

因为组之间的秩总是通过

值

列的排序值增加1，并转换为

整数

：

df['Rank'] = df.groupby("Country")["Value"].rank(method='dense', ascending=False).astype(int)
print (df)
       Country  Value  Rank
0      Germany  102.1     1
1      Germany   90.3     2
2      Germany   44.6     3
3  Switzerland   59.9     1
4  Switzerland   35.3     2
5  Switzerland   21.6     3

如果需要计数器，则最好使用：

变化的数据最能体现差异：

print (df)
       Country  Value
0      Germany   90.3 second largest per group - 2
1      Germany  102.1 largest per group - 1
2      Germany   44.6 third largest per group - 3
3  Switzerland   21.6
4  Switzerland   35.3
5  Switzerland   59.9

df['Rank'] = df.groupby("Country")["Value"].rank(method='dense', ascending=False).astype(int)
df['Rank1'] = df.groupby("Country").cumcount() + 1

print (df)
       Country  Value  Rank  Rank1
0      Germany   90.3     2      1
1      Germany  102.1     1      2
2      Germany   44.6     3      3
3  Switzerland   21.6     3      1
4  Switzerland   35.3     2      2
5  Switzerland   59.9     1      3

print (df)
       Country  Value
0      Germany   90.3 second largest per group - 2
1      Germany  102.1 largest per group - 1
2      Germany   44.6 third largest per group - 3
3  Switzerland   21.6
4  Switzerland   35.3
5  Switzerland   59.9

df['Rank'] = df.groupby("Country")["Value"].rank(method='dense', ascending=False).astype(int)
df['Rank1'] = df.groupby("Country").cumcount() + 1

print (df)
       Country  Value  Rank  Rank1
0      Germany   90.3     2      1
1      Germany  102.1     1      2
2      Germany   44.6     3      3
3  Switzerland   21.6     3      1
4  Switzerland   35.3     2      2
5  Switzerland   59.9     1      3