pandas 根据列索引重命名 Dataframe 列
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Rename Dataframe column based on column index
提问by aberger
Is there a built in function to rename a pandas dataframe by index?
是否有内置函数可以按索引重命名Pandas数据框？
I thought I knew the name of my column headers, but it turns out the second column has some hexadecimal characters in it. I will likely come across this issue with column 2 in the future based on the way I receive my data, so I cannot hard code those specific hex characters into a dataframe.rename() call.
我以为我知道列标题的名称，但结果发现第二列中有一些十六进制字符。根据我接收数据的方式，我将来可能会在第 2 列中遇到这个问题，因此我无法将这些特定的十六进制字符硬编码到 dataframe.rename() 调用中。
Is there a function that would be appropriately named rename_col_by_index() that I have not been able to find?
是否有一个函数可以适当地命名为 rename_col_by_index() ，但我无法找到？
Ex:
前任：
>>> df = pd.DataFrame({'a':[1,2], 'b':[3,4]})
>>> df.rename_col_by_index(1, 'new_name')
>>> df
a new_name
0 1 3
1 2 4
回答by taoufik A
You can simply use
你可以简单地使用
df.columns.values[index] = "New name"
or replace all the existing names with new ones by
或用新名称替换所有现有名称
df.columns = ["col1", "col2"]
回答by MaxU
UPDATE: thanks to @Vincenzzzochi:
更新：感谢@Vincenzzzochi：
In [138]: df.rename(columns={df.columns[1]: 'new'})
Out[138]:
a new c
0 1 3 5
1 2 4 6
In [140]: df
Out[140]:
a b c
0 1 3 5
1 2 4 6
or bit more flexible:
或者更灵活一点：
In [141]: mapping = {df.columns[0]:'new0', df.columns[1]: 'new1'}
In [142]: df.rename(columns=mapping)
Out[142]:
new0 new1 c
0 1 3 5
1 2 4 6
