如果某行在任何列中包含“9999-Without Know”,如何删除数据框中的所有行?
我能够找到基于整个数据框中的值格式(字符串,数字等)删除行的解决方案,或者根据特定列中的值删除行,或者从具有的数据框中删除行几列使用他们的名字。
This是我发现的最接近的东西,但是这个解决方案对我不起作用,因为由于数量庞大(76 +列),我无法输入所有列名。
以下是样本数据集
pd.DataFrame.from_items([('RespondentId', ['1ghi3g','335hduu','4vlsiu4','5nnvkkt','634deds','7kjng']), ('Satisfaction - Timing', ['9-Excellent','9-Excellent','9999-Don\'t Know','8-Very Good','1-Very Unsatisfied','9999-Don\'t Know']),('Response Speed - Time',['9999-Don\'t Know','9999-Don\'t Know','9-Excellent','9-Excellent','9-Excellent','9-Excellent'])])
删除包含'9999 -Dad Know'的4行后,输出应该如下所示,这样我就可以用清理后的数据编写一个新的Excel文件。
pd.DataFrame.from_items([('RespondentId', ['5nnvkkt','634deds']), ('Satisfaction - Timing', ['8-Very Good','1-Very Unsatisfied']),('Response Speed - Time',['9-Excellent','9-Excellent'])])
答案 0 :(得分:5)
使用
In [677]: df[~(df == "9999-Don't Know").any(axis=1)]
Out[677]:
RespondentId Satisfaction - Timing Response Speed - Time
3 5nnvkkt 8-Very Good 9-Excellent
4 634deds 1-Very Unsatisfied 9-Excellent
或者
In [683]: df[(df != "9999-Don't Know").all(axis=1)]
Out[683]:
RespondentId Satisfaction - Timing Response Speed - Time
3 5nnvkkt 8-Very Good 9-Excellent
4 634deds 1-Very Unsatisfied 9-Excellent
与
相同In [686]: df[~df.eq("9999-Don't Know").any(axis=1)]
Out[686]:
RespondentId Satisfaction - Timing Response Speed - Time
3 5nnvkkt 8-Very Good 9-Excellent
4 634deds 1-Very Unsatisfied 9-Excellent
或
In [687]: df[df.ne("9999-Don't Know").all(axis=1)]
Out[687]:
RespondentId Satisfaction - Timing Response Speed - Time
3 5nnvkkt 8-Very Good 9-Excellent
4 634deds 1-Very Unsatisfied 9-Excellent
对于混合列类型,请参阅@ PiR的评论df.astype(object)
In [695]: df[df.astype(object).ne("9999-Don't Know").all(axis=1)]
Out[695]:
RespondentId Satisfaction - Timing Response Speed - Time
3 5nnvkkt 8-Very Good 9-Excellent
4 634deds 1-Very Unsatisfied 9-Excellent