python - 按一列中出现的频率对整个 csv 进行排序

Question

我有一个很大的 CSV 文件，它是调用者数据的日志。

我的文件的一小段:

CompanyName    High Priority     QualityIssue
Customer1         Yes             User
Customer1         Yes             User
Customer2         No              User
Customer3         No              Equipment
Customer1         No              Neither
Customer3         No              User
Customer3         Yes             User
Customer3         Yes             Equipment
Customer4         No              User

我想按客户出现的频率对整个列表进行排序，因此它会像:

CompanyName    High Priority     QualityIssue
Customer3         No               Equipment
Customer3         No               User
Customer3         Yes              User
Customer3         Yes              Equipment
Customer1         Yes              User
Customer1         Yes              User
Customer1         No               Neither
Customer2         No               User
Customer4         No               User

我试过 groupby，但只打印出公司名称和频率而不是其他列，我也试过

df['Totals']= [sum(df['CompanyName'] == df['CompanyName'][i]) for i in xrange(len(df))]

和

df = [sum(df['CompanyName'] == df['CompanyName'][i]) for i in xrange(len(df))]

但是这些给我错误:

ValueError: The wrong number of items passed 1, indices imply 24

我看过这样的东西:

for key, value in sorted(mydict.iteritems(), key=lambda (k,v): (v,k)):
    print "%s: %s" % (key, value)

但这只打印出两列，我想对整个 CSV 进行排序。我的输出应该是按第一列排序的整个 CSV。

提前感谢您的帮助!

Answer 1

这似乎可以满足您的要求，基本上是通过执行 groupby 添加一个计数列和 transform与 value_counts然后您可以对该列进行排序:

df['count'] = df.groupby('CompanyName')['CompanyName'].transform(pd.Series.value_counts)
df.sort_values('count', ascending=False)

输出:

  CompanyName HighPriority QualityIssue count
5   Customer3           No         User     4
3   Customer3           No    Equipment     4
7   Customer3          Yes    Equipment     4
6   Customer3          Yes         User     4
0   Customer1          Yes         User     3
4   Customer1           No      Neither     3
1   Customer1          Yes         User     3
8   Customer4           No         User     1
2   Customer2           No         User     1

您可以使用 df.drop 删除无关的列:

df.drop('count', axis=1)

输出:

  CompanyName HighPriority QualityIssue
5   Customer3           No         User
3   Customer3           No    Equipment
7   Customer3          Yes    Equipment
6   Customer3          Yes         User
0   Customer1          Yes         User
4   Customer1           No      Neither
1   Customer1          Yes         User
8   Customer4           No         User
2   Customer2           No         User