在大熊貓，如何根據

Question

我有以下的大熊貓據幀

+---------+-------+ 
| Country | value | 
+---------+-------+ 
| UK  | 42 | 
| US  |  9 | 
| US  | 10 | 
| France | 15 | 
| France | 16 | 
| Germany | 17 | 
| Germany | 18 | 
| Germany | 20 | 
+---------+-------+ 

我想創造的是，根據每個國家的排名新列的另一列的平均值有秩創建新列他們的價值觀，從最大到最小

平均輸出將類似於以下

+---------+-------+---------+------+ 
| Country | value | Average | Rank | 
+---------+-------+---------+------+ 
| UK  | 42 |  42 | 1 | 
| US  |  9 |  9.5 | 4 | 
| US  | 10 |  9.5 | 4 | 
| France | 15 | 15.5 | 3 | 
| France | 16 | 15.5 | 3 | 
| Germany | 17 |  18 | 2 | 
| Germany | 18 |  18 | 2 | 
| Germany | 20 |  18 | 2 | 
+---------+-------+---------+------+ 

注意，我不需要平均列，它只是在那裏幫助解釋。

非常感謝

Answer 1

使用groupby + transform爲mean，然後rank：

df['Average'] = df.groupby('Country')['value'].transform('mean') 
df['Rank'] = df['Average'].rank(method='dense', ascending=False) 
print (df) 
    Country value Average Rank 
0  UK  42 42.000000 1.0 
1  US  9 9.500000 4.0 
2  US  10 9.500000 4.0 
3 France  15 15.500000 3.0 
4 France  16 15.500000 3.0 
5 Germany  17 18.333333 2.0 
6 Germany  18 18.333333 2.0 
7 Germany  20 18.333333 2.0 

類似的解決方案：

a = df.groupby('Country')['value'].transform('mean') 
b = a.rank(method='dense', ascending=False) 

df = df.assign(Average=a, Rank=b) 
print (df) 
    Country value Average Rank 
0  UK  42 42.000000 1.0 
1  US  9 9.500000 4.0 
2  US  10 9.500000 4.0 
3 France  15 15.500000 3.0 
4 France  16 15.500000 3.0 
5 Germany  17 18.333333 2.0 
6 Germany  18 18.333333 2.0 
7 Germany  20 18.333333 2.0 

Answer 2

解決方案
我用的pd.DataFrame.join和pd.concat組合爲groupby與mean

m = df.groupby('Country').value.mean() 
df.join(
    pd.concat([m, m.rank(ascending=False)], axis=1, keys=['Average', 'Rank']), 
    on='Country') 

    Country value Average Rank 
0  UK  42 42.000000 1.0 
1  US  9 9.500000 4.0 
2  US  10 9.500000 4.0 
3 France  15 15.500000 3.0 
4 France  16 15.500000 3.0 
5 Germany  17 18.333333 2.0 
6 Germany  18 18.333333 2.0 
7 Germany  20 18.333333 2.0 

---

同樣後，用雙join

m = df.groupby('Country').value.mean() 
df.join(m.rename('Avergage'), on='Country') \ 
    .join(m.rank(ascending=False).rename('Rank'), on='Country') 

    Country value Average Rank 
0  UK  42 42.000000 1.0 
1  US  9 9.500000 4.0 
2  US  10 9.500000 4.0 
3 France  15 15.500000 3.0 
4 France  16 15.500000 3.0 
5 Germany  17 18.333333 2.0 
6 Germany  18 18.333333 2.0 
7 Germany  20 18.333333 2.0 

---

或用map和assign

m = df.groupby('Country').value.mean() 
df.assign(
    Average=df.Country.map(m), 
    Rank=df.Country.map(m.rank(ascending=False)) 
) 

    Country value Average Rank 
0  UK  42 42.000000 1.0 
1  US  9 9.500000 4.0 
2  US  10 9.500000 4.0 
3 France  15 15.500000 3.0 
4 France  16 15.500000 3.0 
5 Germany  17 18.333333 2.0 
6 Germany  18 18.333333 2.0 
7 Germany  20 18.333333 2.0 

Answer 3

我會使用現代化的鏈式方法調用的方法，以避免突變狀態，並創造新的變量：

df = pd.DataFrame(
    {'Country': ['Russia', 'Russia', 'USA'], 'Value': [12, 15, 16]}) 

df.join(df.groupby('Country'). 
      mean(). 
      rank(). 
      rename(columns={'Value': 'Rank'}), 
     on='Country')