Pandas:在独立列中转换列的值

Question

我有Pandas DataFrame,它看起来像follow(df_olymic).我希望列的值Type在独立列中转换(df_olympic_table)

原始数据帧

In [3]: df_olympic
Out[3]: 
   Country    Type Num
0      USA    Gold  46
1      USA  Silver  37
2      USA  Bronze  38
3       GB    Gold  27
4       GB  Silver  23
5       GB  Bronze  17
6    China    Gold  26
7    China  Silver  18
8    China  Bronze  26
9   Russia    Gold  19
10  Russia  Silver  18
11  Russia  Bronze  19

转换后的数据帧

In [5]: df_olympic_table
Out[5]: 
  Country N_Gold N_Silver N_Bronze
0     USA     46       37       38
1      GB     27       23       17
2   China     26       18       26
3  Russia     19       18       19

实现这一目标最方便的方法是什么？

Answer 1

你可以使用DataFrame.pivot:

df = df.pivot(index='Country', columns='Type', values='Num')
print (df)
Type     Bronze  Gold  Silver
Country                      
China        26    26      18
GB           17    27      23
Russia       19    19      18
USA          38    46      37

另一种解决方案:DataFrame.set_index和Series.unstack:

df = df.set_index(['Country','Type'])['Num'].unstack()
print (df)
Type     Bronze  Gold  Silver
Country                      
China        26    26      18
GB           17    27      23
Russia       19    19      18
USA          38    46      37

但如果得到:

ValueError:索引包含重复的条目,无法重新整形

需要pivot_table一些aggreagte功能,默认情况下np.mean,但你可以使用sum,first...

#add new row with duplicates value in 'Country' and 'Type'
print (df)
   Country    Type  Num
0      USA    Gold   46
1      USA  Silver   37
2      USA  Bronze   38
3       GB    Gold   27
4       GB  Silver   23
5       GB  Bronze   17
6    China    Gold   26
7    China  Silver   18
8    China  Bronze   26
9   Russia    Gold   19
10  Russia  Silver   18
11  Russia  Bronze   20 < - changed value to 20
11  Russia  Bronze  100 < - add new row with duplicates


df = df.pivot_table(index='Country', columns='Type', values='Num', aggfunc=np.mean)
print (df)
Type     Bronze  Gold  Silver
Country                      
China        26    26      18
GB           17    27      23
Russia       60    19      18 < - Russia get ((100 + 20)/ 2 = 60
USA          38    46      37

或者groupby通过以下方式进行聚合mean和重塑unstack:

df = df.groupby(['Country','Type'])['Num'].mean().unstack()
print (df)
Type     Bronze  Gold  Silver
Country                      
China        26    26      18
GB           17    27      23
Russia       60    19      18 < - Russia get ((100 + 20)/ 2 = 60
USA          38    46      37