删除一列中的值等于另一列中的值的行 [英] Remove rows where value in one column equals value in another

问题描述

我正在努力找出如何从其中两个指定列在一行中具有相同值的pandas数据框中删除行的方法.

I'm struggling to figure out how to remove rows from a pandas dataframe in which two specified columns have the same value across a row.

例如，在以下示例中，我要删除第2列和第4列中具有重复值的行.

For example, in the below examples I would like to remove the rows which have duplicate values in the columns 2 and 4.

例如:

Column1 Column2 Column3 Column4
  Pat     123     John    456
  Pat     123     John    345 
  Jimmy   678     Mary    678 
  Larry   678     James   983

将变成:

Column1 Column2 Column3 Column4
  Pat     123     John    456 
  Pat     123     John    345
  Larry   678     James   983

感谢您的帮助，谢谢！

推荐答案

Series.ne(!=)

df[df['Column2'] != df['Column4']]

  Column1  Column2 Column3  Column4
0     Pat      123    John      456
1     Pat      123    John      345
3   Larry      678   James      983

或者，使用operator.ne:

df[operator.ne(df['Column2'], df['Column4'])]

  Column1  Column2 Column3  Column4
0     Pat      123    John      456
1     Pat      123    John      345
3   Larry      678   James      983

比较两者；得到一个面具，然后过滤.

Compare the two; get a mask, then filter.

使用loc，我们还可以提供回调(由@ W-B建议！).

With loc, we can also supply a callback (suggested by @W-B!).

df.loc[lambda x : x['Column2'] != x['Column4']]

  Column1  Column2 Column3  Column4
0     Pat      123    John      456
1     Pat      123    John      345
3   Larry      678   James      983

---

query

---

query

df.query('Column2 != Column4')

  Column1  Column2 Column3  Column4
0     Pat      123    John      456
1     Pat      123    John      345
3   Larry      678   James      983

---

np.vectorize

---

np.vectorize

import operator
f = pd.np.vectorize(lambda x, y: x != y)
df[f(df['Column2'], df['Column4'])]

  Column1  Column2 Column3  Column4
0     Pat      123    John      456
1     Pat      123    John      345
3   Larry      678   James      983

...只是为了好玩.

...Just for fun.

df[[x != y for x, y in zip(df['Column2'], df['Column4'])]]

  Column1  Column2 Column3  Column4
0     Pat      123    John      456
1     Pat      123    John      345
3   Larry      678   James      983

比您想象的要快！

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