如果行在Pandas中包含特定值，则删除列 [英] Drop columns if rows contain a specific value in Pandas

问题描述

我开始学习熊猫。我在SO中看到了很多问题，人们问如果列匹配特定值，如何删除行。

I am starting to learn Pandas. I have seen a lot of questions here in SO where people ask how to delete a row if a column matches certain value.

在我看来，情况恰恰相反。想象一下具有以下数据帧：

In my case it is the opposite. Imagine having this dataframe:

您想知道的是，如果任何一列在其任何一行的值都为 salty ，则应删除该列，其结果是：

Where you want to know is, if any column has in any of its row the value salty, that column should be deleted, having as a result:

我尝试了以下类似方法：

I have tried with several similarities to this:

if df.loc[df['A'] == 'salty']:
   df.drop(df.columns[0], axis=1, inplace=True)

但是我很迷失在寻找关于如何基于行值删除列的文档该列。该代码混合了查找特定列并始终删除第一列的过程（因为我的想法是在中为

But I am quite lost at finding documentation onto how to delete columns based on a row value of that column. That code is a mix of finding a specific column and deleting always the first column (as my idea was to search the value of a row in that column, in ALL columns in a for loop.

推荐答案

对值进行比较，然后使用 DataFrame.any 以获得要索引的掩码：

Perform a comparison across your values, then use DataFrame.any to get a mask to index:

df.loc[:, ~(df == 'Salty').any()]

如果您坚持使用 drop ，这就是您需要执行的操作。传递索引列表：

If you insist on using drop, this is how you need to do it. Pass a list of indices:

df.drop(columns=df.columns[(df == 'Salty').any()])

---

---

df = pd.DataFrame({
    'A': ['Mountain', 'Salty'], 'B': ['Lake', 'Hotty'], 'C': ['River', 'Coldy']})
df
          A      B      C
0  Mountain   Lake  River
1     Salty  Hotty  Coldy

(df == 'Salty').any()
A     True
B    False
C    False
dtype: bool

df.loc[:, ~(df == 'Salty').any()]
       B      C
0   Lake  River
1  Hotty  Coldy

df.columns[(df == 'Salty').any()]
# Index(['A'], dtype='object')

df.drop(columns=df.columns[(df == 'Salty').any()])
       B      C
0   Lake  River
1  Hotty  Coldy

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