使用 Series 查找表替换 Pandas DataFrame 列中的值

Question

我想用我准备的系列形式的查找表生成的更准确/完整的一组值替换 DataFrame 中的一列值。

我以为我可以这样做，但结果并不像预期的那样。

这是我要修复的 DataFrame：

In [6]: df_normalised.head(10)
Out[6]: 
  code                                          name
0    8                             Human development
1   11                                              
2    1                           Economic management
3    6         Social protection and risk management
4    5                         Trade and integration
5    2                      Public sector governance
6   11  Environment and natural resources management
7    6         Social protection and risk management
8    7                   Social dev/gender/inclusion
9    7                   Social dev/gender/inclusion

（注意第 2 行中缺少的名称）。

这是我为进行修复而创建的查找表：

In [20]: names
Out[20]: 
1                              Economic management
10                               Rural development
11    Environment and natural resources management
2                         Public sector governance
3                                      Rule of law
4         Financial and private sector development
5                            Trade and integration
6            Social protection and risk management
7                      Social dev/gender/inclusion
8                                Human development
9                                Urban development
dtype: object

这是我认为可以做到的方式：

In [21]: names[df_normalised.head(10).code]
Out[21]: 
code
8                                Human development
11    Environment and natural resources management
1                              Economic management
6            Social protection and risk management
5                            Trade and integration
2                         Public sector governance
11    Environment and natural resources management
6            Social protection and risk management
7                      Social dev/gender/inclusion
7                      Social dev/gender/inclusion
dtype: object

但是，我希望上面的结果系列具有与 df_normalised 的索引相同的索引（即 0、1、2、3），而不是基于代码值的索引。

所以我不确定如何用这些系列值替换 df_normalised 中“名称”列中的原始值，因为索引不一样。

顺便说一句，上面的索引怎么可能有重复值？

Answer 1

您可以为此使用map() 函数：

In [38]: df_normalised['name'] = df_normalised['code'].map(name)

In [39]: df_normalised
Out[39]:
   code                                          name
0     8                             Human development
1    11  Environment and natural resources management
2     1                           Economic management
3     6         Social protection and risk management
4     5                         Trade and integration
5     2                      Public sector governance
6    11  Environment and natural resources management
7     6         Social protection and risk management
8     7                   Social dev/gender/inclusion
9     7                   Social dev/gender/inclusion

Answer 2

这行得通。不过，我很确定一定有更简单的方法来做到这一点。

In [50]: df_normalised.name = pd.Series(names[df_normalised.code].values)

In [51]: df_normalised.head(10)
Out[51]: 
  code                                          name
0    8                             Human development
1   11  Environment and natural resources management
2    1                           Economic management
3    6         Social protection and risk management
4    5                         Trade and integration
5    2                      Public sector governance
6   11  Environment and natural resources management
7    6         Social protection and risk management
8    7                   Social dev/gender/inclusion
9    7                   Social dev/gender/inclusion