一列值的和基於另一個列的每個值，然后將其除以總計

Question

今天，我再次在python和數據分析領域苦苦掙扎。

我得到了一個數據幀，看起來像這樣：

    name         totdmgdealt
0   Warwick      96980.0
1   Nami         25995.0
2   Draven       171568.0
3   Fiora        113721.0
4   Viktor       185302.0
5   Skarner      148791.0
6   Galio        130692.0
7   Ahri         145731.0
8   Jinx         182680.0
9   VelKoz       85785.0
10  Ziggs        46790.0
11  Cassiopeia   62444.0
12  Yasuo        117896.0
13  Warwick      129156.0
14  Evelynn      179252.0
15  Caitlyn      163342.0
16  Wukong       122919.0
17  Syndra       146754.0
18  Karma        35766.0
19  Warwick      117790.0
20  Draven       74879.0
21  Janna        11242.0
22  Lux          66424.0
23  Amumu        87826.0
24  Vayne        76085.0
25  Ahri         93334.0
..
..
..

這是一個數據幀，其中包括一個游戲冠軍的總傷害。 現在，我想對這些信息進行分組，這樣我就可以看到哪個冠軍造成的傷害最大。 我嘗試了groupby（'name'），但是根本沒有用。 我已經通過了一些有關groupby和求和值的線程，但是我沒有解決我的特定問題。

每個冠軍造成的傷害也應該以總數的百分比顯示。

我正在尋找這樣的東西作為輸出：

    name     totdmgdealt  percentage
0   Warwick  2378798098     2.1  %
1   Nami     2837491074     2.3  %
2   Draven   1231451224     ..
3   Fiora    1287301724     ..
4   Viktor   1239808504     ..
5   Skarner  1487911234     ..
6   Galio    1306921234     ..

Answer 1

我們可以GROUPBY上名，並獲得sum那么我們用總將每個值.div和100乘以.mul最后它四舍五入至小數點后與.round ：

total = df['totdmgdealt'].sum()

summed = df.groupby('name', sort=False)['totdmgdealt'].sum().reset_index()

summed['percentage'] = summed.groupby('name', sort=False)['totdmgdealt']\
                             .sum()\
                             .div(total)\
                             .mul(100)\
                             .round(1).values

          name  totdmgdealt  percentage
0      Warwick     343926.0        12.2
1         Nami      25995.0         0.9
2       Draven     246447.0         8.7
3        Fiora     113721.0         4.0
4       Viktor     185302.0         6.6
5      Skarner     148791.0         5.3
6        Galio     130692.0         4.6
7         Ahri     239065.0         8.5
8         Jinx     182680.0         6.5
9       VelKoz      85785.0         3.0
10       Ziggs      46790.0         1.7
11  Cassiopeia      62444.0         2.2
12       Yasuo     117896.0         4.2
13     Evelynn     179252.0         6.4
14     Caitlyn     163342.0         5.8
15      Wukong     122919.0         4.4
16      Syndra     146754.0         5.2
17       Karma      35766.0         1.3
18       Janna      11242.0         0.4
19         Lux      66424.0         2.4
20       Amumu      87826.0         3.1
21       Vayne      76085.0         2.7

Answer 2

您可以使用sum()來獲取總dmg，並apply計算與每一行相關的precent，如下所示：

import pandas as pd
from io import StringIO

df = pd.read_csv(StringIO("""
    name         totdmgdealt
0   Warwick      96980.0
1   Nami         25995.0
2   Draven       171568.0
3   Fiora        113721.0
4   Viktor       185302.0
5   Skarner      148791.0
6   Galio        130692.0
7   Ahri         145731.0
8   Jinx         182680.0
9   VelKoz       85785.0
10  Ziggs        46790.0
11  Cassiopeia   62444.0
12  Yasuo        117896.0
13  Warwick      129156.0
14  Evelynn      179252.0
15  Caitlyn      163342.0
16  Wukong       122919.0
17  Syndra       146754.0
18  Karma        35766.0
19  Warwick      117790.0
20  Draven       74879.0
21  Janna        11242.0
22  Lux          66424.0
23  Amumu        87826.0
24  Vayne        76085.0
25  Ahri         93334.0"""), sep=r"\s+")

summed_df = df.groupby('name')['totdmgdealt'].agg(['sum']).rename(columns={"sum": "totdmgdealt"}).reset_index()
summed_df['percentage'] = summed_df.apply(
    lambda x: "{:.2f}%".format(x['totdmgdealt'] / summed_df['totdmgdealt'].sum() * 100), axis=1)
print(summed_df)

輸出：

          name  totdmgdealt percentage
0         Ahri     239065.0      8.48%
1        Amumu      87826.0      3.12%
2      Caitlyn     163342.0      5.79%
3   Cassiopeia      62444.0      2.21%
4       Draven     246447.0      8.74%
5      Evelynn     179252.0      6.36%
6        Fiora     113721.0      4.03%
7        Galio     130692.0      4.64%
8        Janna      11242.0      0.40%
9         Jinx     182680.0      6.48%
10       Karma      35766.0      1.27%
11         Lux      66424.0      2.36%
12        Nami      25995.0      0.92%
13     Skarner     148791.0      5.28%
14      Syndra     146754.0      5.21%
15       Vayne      76085.0      2.70%
16      VelKoz      85785.0      3.04%
17      Viktor     185302.0      6.57%
18     Warwick     343926.0     12.20%
19      Wukong     122919.0      4.36%
20       Yasuo     117896.0      4.18%
21       Ziggs      46790.0      1.66%

Answer 3

也許您可以嘗試以下方法：我嘗試使用示例數據實現相同的效果，並嘗試將以下代碼運行到Jupyter Notebook中：

---

import pandas as pd
name=['abhit','mawa','vaibhav','dharam','sid','abhit','vaibhav','sid','mawa','lakshya']
totdmgdealt=[24,45,80,22,89,55,89,51,93,85]
name=pd.Series(name,name='name')               #converting into series 
totdmgdealt=pd.Series(totdmgdealt,name='totdmgdealt')  #converting into series
data=pd.concat([name,totdmgdealt],axis=1)
data=pd.DataFrame(data)                      #converting into Dataframe 
final=data.pivot_table(values="totdmgdealt",columns="name",aggfunc="sum").transpose()  #actual aggregating method
total=data['totdmgdealt'].sum()            #calculating total for calculating percentage
def calPer(row,total):                     #actual Function for Percentage
    return ((row/total)*100).round(2)
total=final['totdmgdealt'].sum()
final['Percentage']=calPer(final['totdmgdealt'],total)  #assigning the function to the column
final

---

樣本數據 ：

    name    totdmgdealt
0   abhit   24
1   mawa    45
2   vaibhav 80
3   dharam  22
4   sid     89
5   abhit   55
6   vaibhav 89
7   sid     51
8   mawa    93
9   lakshya 85

---

輸出：

        totdmgdealt     Percentage
name        
abhit     79               12.48
dharam    22               3.48
lakshya   85               13.43
mawa      138              21.80
sid       140              22.12
vaibhav   169              26.70

---

了解並運行代碼，然后將數據集替換為Yours。 也許有幫助。