在 Python/Pandas 中对一列执行值计数，但在多列中返回值

Question

在 Python 中，我试图在 Pandas 列上执行 value_counts。我可以让它工作，但我不知道如何获得其他一些相关的列。
代码：

import pandas as pd

myframe = pd.DataFrame({"Server":["Server_1","Server_1","Server_1","Server_1","Server_1","Server_2","Server_2","Server_2","Server_2","Server_3","Server_3","Server_3","Server_3","Server_3"], 
"CVE_ID":["CVE-2017-1111","CVE-2017-1112","CVE-2017-1113","CVE-2017-1114","CVE-2017-1115","CVE-2017-1111","CVE-2017-1112","CVE-2017-1113","CVE-2017-1114","CVE-2017-1113","CVE-2017-1114","CVE-2017-1115","CVE-2017-1116","CVE-2017-1117"],
"VulnName":["Java Update 1","Java Update 2","Java Update 3","Adobe 1","Chrome 1","Java Update 1","Java Update 2","Java Update 3","Adobe 1","Java Update 3","Adobe 1","Chrome 1","Chrome 2","Chrome 3"],
"ServerOwner":["Alice","Alice","Alice","Alice","Alice","Bob","Bob","Bob","Bob","Carol","Carol","Carol","Carol","Carol"]})

print "The dataframe: \n", myframe
print "Top 10 offending CVEs, Vulnerability and Count: \n"
print myframe['CVE_ID'].value_counts()

最后一行打印出两列：一列 CVE，一列是它发生的次数。但我想打印出类似这样的内容，其中维护了 CVE 和漏洞名称之间的链接（见中间列）：

Top 10 offending CVEs, Vulnerability and Count:
CVE-2017-1113   Java Update 1     3
CVE-2017-1114   Java Update 2     3
...etc...

我该怎么做？我所做的一切都不断抛出错误。

Answer 1

编辑：更改为在输出中具有列名访问权限

（请注意在 [1] 中添加了 as_index=False 和 .reset_index，参见来源 5 和 6

[1] 在CVE_ID 列上的第一个groupby 并使用size：

counts = myframe.groupby(['CVE_ID','VulnName','ServerOwner'], as_index=False).size().unstack(fill_value=0).reset_index()


ServerOwner         CVE_ID       VulnName  Alice  Bob  Carol
0            CVE-2017-1111  Java Update 1      1    1      0
1            CVE-2017-1112  Java Update 2      1    1      0
2            CVE-2017-1113  Java Update 3      1    1      1
3            CVE-2017-1114        Adobe 1      1    1      1
4            CVE-2017-1115       Chrome 1      1    0      1
5            CVE-2017-1116       Chrome 2      0    0      1
6            CVE-2017-1117       Chrome 3      0    0      1

[2] 然后对 Alice、Bob 和 Carol 列求和得到：

counts['Count'] = counts[['Alice','Bob','Carol']].sum(axis=1)

ServerOwner         CVE_ID       VulnName  Alice  Bob  Carol  Count
0            CVE-2017-1111  Java Update 1      1    1      0      2
1            CVE-2017-1112  Java Update 2      1    1      0      2
2            CVE-2017-1113  Java Update 3      1    1      1      3
3            CVE-2017-1114        Adobe 1      1    1      1      3
4            CVE-2017-1115       Chrome 1      1    0      1      2
5            CVE-2017-1116       Chrome 2      0    0      1      1
6            CVE-2017-1117       Chrome 3      0    0      1      1

[3] 然后在names上使用df.drop删除名称列：

counts.drop(['Carol','Bob','Alice'],inplace=True,axis=1)

ServerOwner         CVE_ID       VulnName  Count
0            CVE-2017-1111  Java Update 1      2
1            CVE-2017-1112  Java Update 2      2
2            CVE-2017-1113  Java Update 3      3
3            CVE-2017-1114        Adobe 1      3
4            CVE-2017-1115       Chrome 1      2
5            CVE-2017-1116       Chrome 2      1
6            CVE-2017-1117       Chrome 3      1

[4] 然后在sum 列上使用sort_values：

counts.sort_values(by='Count', ascending=False, inplace=True)

ServerOwner         CVE_ID       VulnName  Count
2            CVE-2017-1113  Java Update 3      3
3            CVE-2017-1114        Adobe 1      3
0            CVE-2017-1111  Java Update 1      2
1            CVE-2017-1112  Java Update 2      2
4            CVE-2017-1115       Chrome 1      2
5            CVE-2017-1116       Chrome 2      1
6            CVE-2017-1117       Chrome 3      1

综合：

counts = myframe.groupby(['CVE_ID','VulnName','ServerOwner'], as_index=False).size().unstack(fill_value=0).reset_index()
counts['Count'] = counts[['Alice','Bob','Carol']].sum(axis=1)
counts.drop(['Carol','Bob','Alice'],inplace=True,axis=1)
counts.sort_values(by='Count', ascending=False, inplace=True)

print "The dataframe: \n", myframe
print "Top 10 offending CVEs, Vulnerability and Count: \n"
print counts

Top 10 offending CVEs, Vulnerability and Count: 

ServerOwner         CVE_ID       VulnName  Count
2            CVE-2017-1113  Java Update 3      3
3            CVE-2017-1114        Adobe 1      3
0            CVE-2017-1111  Java Update 1      2
1            CVE-2017-1112  Java Update 2      2
4            CVE-2017-1115       Chrome 1      2
5            CVE-2017-1116       Chrome 2      1
6            CVE-2017-1117       Chrome 3      1

如果需要，此时可以使用reset_index() 重置索引。

编辑：针对serverOwner索引的评论，您可以重置索引、删除旧索引和重命名新索引：

counts.reset_index(drop=True, inplace = True)
counts.index.names = ['index']

给予：

ServerOwner         CVE_ID       VulnName  Count
index                                           
0            CVE-2017-1113  Java Update 3      3
1            CVE-2017-1114        Adobe 1      3
2            CVE-2017-1111  Java Update 1      2
3            CVE-2017-1112  Java Update 2      2
4            CVE-2017-1115       Chrome 1      2
5            CVE-2017-1116       Chrome 2      1
6            CVE-2017-1117       Chrome 3      1

（ServerOwner 名称保留为原始 groupby 命令的残余，以详细说明使用了哪一列。）

此答案的来源：

[1] Groupby value counts on the dataframe pandas 数据框-熊猫

[2] Pandas: sum DataFrame rows for given columns

[3] Delete column from pandas DataFrame

[4] python, sort descending dataframe with pandas

[5] Converting a Pandas GroupBy object to DataFrame

[6] How to GroupBy a Dataframe in Pandas and keep Columns

Answer 2

使用join 附加value_counts

myframe.join(myframe['CVE_ID'].value_counts().rename('Count'), on='CVE_ID')

           CVE_ID    Server ServerOwner       VulnName  Count
0   CVE-2017-1111  Server_1       Alice  Java Update 1      2
1   CVE-2017-1112  Server_1       Alice  Java Update 2      2
2   CVE-2017-1113  Server_1       Alice  Java Update 3      3
3   CVE-2017-1114  Server_1       Alice        Adobe 1      3
4   CVE-2017-1115  Server_1       Alice       Chrome 1      2
5   CVE-2017-1111  Server_2         Bob  Java Update 1      2
6   CVE-2017-1112  Server_2         Bob  Java Update 2      2
7   CVE-2017-1113  Server_2         Bob  Java Update 3      3
8   CVE-2017-1114  Server_2         Bob        Adobe 1      3
9   CVE-2017-1113  Server_3       Carol  Java Update 3      3
10  CVE-2017-1114  Server_3       Carol        Adobe 1      3
11  CVE-2017-1115  Server_3       Carol       Chrome 1      2
12  CVE-2017-1116  Server_3       Carol       Chrome 2      1
13  CVE-2017-1117  Server_3       Carol       Chrome 3      1

如果要将其限制在前 n 位，（我的示例显示 2），请使用 head 和 how='inner'

myframe.join(
    myframe['CVE_ID'].value_counts().head(2).rename('Count'),
    on='CVE_ID', how='inner')

           CVE_ID    Server ServerOwner       VulnName  Count
2   CVE-2017-1113  Server_1       Alice  Java Update 3      3
7   CVE-2017-1113  Server_2         Bob  Java Update 3      3
9   CVE-2017-1113  Server_3       Carol  Java Update 3      3
3   CVE-2017-1114  Server_1       Alice        Adobe 1      3
8   CVE-2017-1114  Server_2         Bob        Adobe 1      3
10  CVE-2017-1114  Server_3       Carol        Adobe 1      3