根据条件重命名 pandas 数据框的多列 [英] rename multiple columns of pandas dataframe based on condition

问题描述

我有一个df，其中我需要将40个列名重命名为空字符串.这可以通过使用.rename()来实现，但是我需要在dict中提供所有列名，而这些列名需要重命名.我正在寻找通过模式匹配来重命名列的更好方法.凡在列名中找到NULL/UNNAMED的地方，都用空字符串替换.

I have a df in which I need to rename 40 column names to empty string. this can be achieved by using .rename(), but I need to provide all the column names in dict, which needs to be renamed. I was searching for some better way to rename columns by some pattern matching. wherever it finds NULL/UNNAMED in column name, replace that with empty string.

df1:原始df(在实际df中，我大约有20列为NULL1-NULL20，还有20列为UNNAMED1-UNNAMED20)

df1: original df (In actual df, i have around 20 columns as NULL1-NULL20 and 20 columns as UNNAMED1-UNNAMED20)

    NULL1   NULL2   C1  C2  UNNAMED1    UNNAMED2
0   1   11  21  31  41  51
1   2   22  22  32  42  52
2   3   33  23  33  43  53
3   4   44  24  34  44  54

所需的输出df:

            C1  C2      
0   1   11  21  31  41  51
1   2   22  22  32  42  52
2   3   33  23  33  43  53
3   4   44  24  34  44  54

这可以通过

df.rename(columns={'NULL1':'', 'NULL2':'', 'UNNAMED1':'', 'UNNAMED2':''}, inplace=True)

但是我不想创建40个元素的长字典

But I dont want to create the long dictionary of 40 elements

推荐答案

如果您要坚持使用rename:

def renaming_fun(x):
    if "NULL" in x or "UNNAMED" in x:
        return "" # or None
    return x

df = df.rename(columns=renaming_fun)

如果映射功能变得更复杂，可能会很方便.否则，列表理解将起作用:

It can be handy if the mapping function gets more complex. Otherwise, list comprehensions will do:

df.columns = [renaming_fun(col) for col in cols]

另一种可能性:

df.columns = map(renaming_fun, df.columns)

但是正如已经提到的，用空字符串重命名通常不是您要做的事情.

But as it was already mentioned, renaming with empty strings is not something you would usually do.

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