英文:
Python pandas drop columns if their partial name is in a list or column in pandas
问题
thisFilter = df.filter(like=dropthese['partname'].iloc[0], axis=1)df.drop(thisFilter.columns, axis=1, inplace=True)
thisFilter = df.filter(like=dropthese['partname'].iloc[0], axis=1)df.drop(thisFilter.columns, axis=1, inplace=True)
thisFilter = df.filter(like=dropthese['partname'].iloc[0], axis=1)df.drop(thisFilter.columns, axis=1, inplace=True)
thisFilter = df.filter(like=dropthese['partname'].iloc[0], axis=1)df.drop(thisFilter.columns, axis=1, inplace=True)
thisFilter = df.filter(like=dropthese['partname'].iloc[0], axis=1)df.drop(thisFilter.columns, axis=1, inplace=True)
thisFilter = df.filter(like=dropthese['partname'].iloc[0], axis=1)df.drop(thisFilter.columns, axis=1, inplace=True)
英文:
I have the following dataframe called dropthese.
| partname | x1 | x2 | x3....0 text1_mid11 another1_mid22 yet_another
And another dataframe called df that looks like this.
text1_mid1_suffix1 | text1_mid1_suffix2 | ... | something_else | another1_mid2_suffix1 | ....0 .....1 .....2 .....3 .....
I want to drop all the columns from df, if a part of the name is in dropthese['partname'].
So for example, since text1_mid1 is in partname, all columns that contain that partial string should be dropped like text1_mid1_suffix1 and text1_mid1_suffix2.
I have tried,
thisFilter = df.filter(dropthese.partname, regex=True)df.drop(thisFilter, axis=1)
But I get this error, TypeError: Keyword arguments `items`, `like`, or `regex` are mutually exclusive. What is the proper way to do this filter?
答案1
得分: 3
我会使用正则表达式与 str.contains（或 str.match 如果您想限制在字符串的开头）一起使用：
import repattern = '|'.join(dropthese['partname'].map(re.escape))out = df.loc[:, ~df.columns.str.contains(f'({pattern})')]
输出：
something_else0 ...
为什么您的命令失败了
您应该将模式传递给 filter 的 regex 参数，并在 drop 中使用列名：
pattern = '|'.join(dropthese['partname'].map(re.escape))thisFilter = df.filter(regex=pattern)df.drop(thisFilter.columns, axis=1)
英文:
I would use a regex with str.contains (or str.match if you want to restrict to the start of string):
import repattern = '|'.join(dropthese['partname'].map(re.escape))out = df.loc[:, ~df.columns.str.contains(f'({pattern})')]
Output:
something_else0 ...
Why your command failed
you should pass the pattern to the regex parameter of filter, and use the column names in drop:
pattern = '|'.join(dropthese['partname'].map(re.escape))thisFilter = df.filter(regex=pattern)df.drop(thisFilter.columns, axis=1)
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