基于另一列的每个值的列和值，并将其除以总数

Question

今天，我又一次为Python和数据分析而挣扎。

我有一张数据，看起来是这样的：

    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
..
..
..

这是一个数据，其中包括一个冠军的总损害为一场比赛。现在我想对这些信息进行分组，这样我就可以看到哪个冠军的伤害处理得最多。我试过群比(“名字”)，但它根本不起作用。我已经看过了一些关于groupby和求和值的线程，但是我没有解决我的具体问题。

每个冠军的伤害也应该显示在总数的百分比上。

我正在寻找这样的输出：

    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

我们可以按名称分组，得到sum，然后用.div除以每个值的总和，再用.mul乘以100，最后用.round将其四舍五入为一个十进制。

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()获得总dmg，并使用apply计算与每一行相关的百分比，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)

输出：

          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

也许您可以尝试这样做:我尝试使用我的示例数据来实现相同的目标，并尝试在您的木星笔记本中运行以下代码：

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

样本数据：

    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

输出：

        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

理解并运行代码，只需将数据集替换为您的数据集。也许这个有帮助。