pandas 选择没有NaN的所有列 [英] Pandas select all columns without NaN

问题描述

我有一个200列的DF.他们大多数是NaN的.我想选择没有NaN或至少具有最小NaN的所有列.我试图用阈值或notnull()删除所有内容，但没有成功.任何想法.

I have a DF with 200 columns. Most of them are with NaN's. I would like to select all columns with no NaN's or at least with the minimum NaN's. I've tried to drop all with a threshold or with notnull() but without success. Any ideas.

df.dropna(thresh=2, inplace=True)
df_notnull = df[df.notnull()]

DF例如:

col1  col2 col3
23     45  NaN
54     39  NaN
NaN    45  76
87     32  NaN

输出应如下所示:

 df.dropna(axis=1, thresh=2)

    col1  col2
    23     45  
    54     39  
    NaN    45  
    87     32  

推荐答案

您可以使用非NaN列来创建

You can create with non-NaN columns using

df = df[df.columns[~df.isnull().all()]]

或

null_cols = df.columns[df.isnull().all()]
df.drop(null_cols, axis = 1, inplace = True)

如果您希望基于一定百分比的NaN删除列，则将数据超过90％的列设置为空

If you wish to remove columns based on a certain percentage of NaNs, say columns with more than 90% data as null

cols_to_delete = df.columns[df.isnull().sum()/len(df) > .90]
df.drop(cols_to_delete, axis = 1, inplace = True)

这篇关于 pandas 选择没有NaN的所有列的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！