如何解决'列标签'平均威胁'得分'不是唯一的'？起诉Pandas

Question

当运行代码时，我面临以下错误。 错误-列标签“Avg_Threat_Score”不是唯一的。在

我正在创建一个数据透视表，希望将值从高到低排序。在

pt = df.pivot_table(index = 'User Name',values = ['Threat Score', 'Score'], 
        aggfunc = {
                   'Threat Score': np.mean,
                   'Score' :[np.mean, lambda x: len(x.dropna())]
                  }, 
        margins = False) 

new_col =['User Name Count', 'AVG_TH_Score', 'Avg_Threat_Score']
pt.columns = [new_col]
#befor this code is working, after that now working 
df = df.reindex(pt.sort_values
                    (by = 'Avg_Threat_Score',ascending=False).index)

需要对“Avg_Threat_Score”列的值进行高低排序

Answer 1

您需要通过列表而不是嵌套列表来传递新的列名称，因为pandas用一个级别创建MultiIndex。在

new_col =['User Name Count', 'AVG_TH_Score', 'Avg_Threat_Score']
pt.columns = [new_col]

就像：

^{pr2}$

ValueError: The column label 'Avg_Threat_Score' is not unique.
For a multi-index, the label must be a tuple with elements corresponding to each level.

所以使用：

pt.columns = ['User Name Count', 'AVG_TH_Score', 'Avg_Threat_Score']

样本：

df = pd.DataFrame({
        'User Name':list('ababaa'),
         'Threat Score':[4,5,4,np.nan,5,4],
         'Score':[np.nan,8,9,4,2,np.nan],
         'D':[1,3,5,7,1,0]})

pt = (df.pivot_table(index = 'User Name',values = ['Threat Score', 'Score'], 
        aggfunc = {
                   'Threat Score': np.mean,
                   'Score' :[np.mean, lambda x: len(x.dropna())]
                  }, 
        margins = False))

pt.columns = ['User Name Count', 'AVG_TH_Score', 'Avg_Threat_Score']
print (pt)
           User Name Count  AVG_TH_Score  Avg_Threat_Score
User Name                                                 
a                      2.0           5.5              4.25
b                      2.0           6.0              5.00

然后，如果要通过从Avg_Threat_Score排序，请使用有序的^{}作为列User Name，因此最后一个sort_values工作：

names = pt.sort_values(by = 'Avg_Threat_Score',ascending=False).index
print (names)
#Index(['b', 'a'], dtype='object', name='User Name')

df['User Name'] = pd.CategoricalIndex(df['User Name'], categories=names, ordered=True)
df = df.sort_values('User Name')

---

print (df)
  User Name  Threat Score  Score  D
1         b           5.0    8.0  3
3         b           NaN    4.0  7
0         a           4.0    NaN  1
2         a           4.0    9.0  5
4         a           5.0    2.0  1
5         a           4.0    NaN  0

Answer 2

pt = df.pivot_table(index = 'User Name', values = ['Threat Score', 'Score','Source IP'] ,  
                    aggfunc = {"Source IP" : 'count',
                              'Threat Score':np.mean,
                               'Score': np.mean})

pt = pt.sort_values('Threat Score', ascending = False) 
new_cols = ['Avg_Score', 'Count', 'Avg_ThreatScore']
pt.columns = new_cols