Replace values in column of Pandas DataFrame using a Series lookup table

Question

I want to replace a column of values in a DataFrame with a more accurate/complete set of values generated by a look-up table in the form of a Series that I have prepared.

I thought I could do it this way but the result is not as expected.

Here is the DataFrame I want to fix:

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

(Note the missing name in row 2).

Here is the look-up table I created to do the fixing:

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

Here is the way I thought could do it:

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

However, I expected the resulting series above to have the same index as the index of df_normalised (i.e. 0, 1, 2, 3) not an index based on the code values.

So I'm not sure how to replace the original values in the 'name' column in df_normalised with these series values because the indexes are not the same.

Incidentally, how is it possible to have an index with duplicate values as above?

Answer 1

you can use map() function for that:

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