从 pandas 中删除非重复行 [英] Remove non-duplicated rows from pandas

问题描述

这很简单，但我无法理解.假设对于以下数据框，我只想保留 y 列中具有重复值的行:

<预><代码>>>>dfxyxy0 1 11 2 22 3 23 4 34 5 35 6 36 7 57 8 2

所需的输出如下:

<预><代码>>>>dfxy1 2 22 3 23 4 34 5 35 6 37 8 2

我试过了:

df[~df.duplicated('y')]

但我明白了:

 x y0 1 11 2 23 4 36 7 5

解决方案

文档:https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.duplicated.html

<块引用>

保持:{'first','last', False}，默认'first'

• 第一:马克除第一次出现外，重复为 True.

• 最后:马克除最后一次出现外，重复为 True.

• False : 全部标记重复为 True.

表示您正在寻找:

df[df.duplicated('y',keep=False)]

输出:

 x y1 2 22 3 23 4 34 5 35 6 37 8 2

This is rather simple but I can't get me head around it. Let's say for the following data frame, I want to keep only the rows with duplicated values in column y:

>>> df
   x  y
    x   y
0   1   1
1   2   2
2   3   2
3   4   3
4   5   3
5   6   3
6   7   5
7   8   2

The desired output looks like:

>>> df
    x   y
1   2   2
2   3   2
3   4   3
4   5   3
5   6   3
7   8   2

I tried this:

df[~df.duplicated('y')]

but I get this:

    x   y
0   1   1
1   2   2
3   4   3
6   7   5

解决方案

Docs: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.duplicated.html

keep : {‘first’, ‘last’, False}, default ‘first’

• first : Mark duplicates as True except for the first occurrence.

• last : Mark duplicates as True except for the last occurrence.

• False : Mark all duplicates as True.

Meaning you are looking for:

df[df.duplicated('y',keep=False)]

Output:

    x   y
1   2   2
2   3   2
3   4   3
4   5   3
5   6   3
7   8   2

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