根据字典重命名 PANDAS 中的列
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/36531675/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Rename columns in PANDAS based on dictionary
提问by km1234
I have a dataframe and I would like to rename the columns based on another dataframe that I plan to use as dictionary. For example, what I have as first dataframe is:
我有一个数据框，我想根据我计划用作字典的另一个数据框重命名列。例如，我的第一个数据帧是：
AAA BBB CCC DDD
index
1 1 2 3 4
2 5 6 7 8
and as a second dataframe that I would like to use as dictionary:
并作为我想用作字典的第二个数据框：
val1 val2
index
1 AAA A7
2 BBB B0
3 CCC C3
4 DDD D1
What I would like to get as result is the following:
我想得到的结果如下：
A7 B0 C3 D1
index
1 1 2 3 4
2 5 6 7 8
Initially I thought to reshape the first dataframe to long format, then merge with the dictionary dataframe and then reshape back to wide format. However I think this is quite inefficient, so I would like to use a more efficient way (if one exists). Thank you very much four your help.
最初我想将第一个数据帧重新整形为长格式，然后与字典数据帧合并，然后重新整形为宽格式。但是我认为这是非常低效的，所以我想使用一种更有效的方式（如果存在的话）。非常感谢四位的帮助。
回答by ayhan
df.renamehas a parameter called columnsthat accepts dictionaries:
df.rename有一个参数叫做columns接受字典：
df.rename(columns=dict(zip(df2["val1"], df2["val2"])))
Out:
出去：
A7 B0 C3 D1
0 1 2 3 4
1 5 6 7 8
It returns a new DataFrame. You can either use inplace=True, or assign it back to the original DataFrame.
它返回一个新的 DataFrame。您可以使用inplace=True，也可以将其分配回原始 DataFrame。
回答by jezrael
I think you can first create dictionaryfrom df2, then create Seriesfrom columns of df1by to_serieswhich you then mapusing dictionary:
我觉得你可以先创建dictionary从df2，然后创建Series从列df1由to_series您再map使用dictionary：
print df1
AAA BBB CCC DDD
index
1 1 2 3 4
2 5 6 7 8
print df2
val1 val2
index
1 AAA A7
2 BBB B0
3 CCC C3
4 DDD D1
d = df2.set_index('val1').to_dict()
print d['val2']
{'AAA': 'A7', 'BBB': 'B0', 'CCC': 'C3', 'DDD': 'D1'}
df1.columns = df1.columns.to_series().map(d['val2'])
print df1
A7 B0 C3 D1
index
1 1 2 3 4
2 5 6 7 8
回答by Joe T. Boka
You can also just use this one line:
您也可以只使用这一行：
df1.columns=[df2['val2']]
Output:
输出：
A7 B0 C3 D1
0 1 2 3 7
1 5 6 7 8
