python：分离出在 pandas 数据帧中有重复的行 [英] python: separate out rows which have duplicates in panda dataframe

问题描述

假设数据框 df 有三列 c1，c2，c3 。

df=pd.DataFrame()
df['c1']=[1,2,3,3,4]
df['c2']=["a1","a2","a2","a2","a1"]
df['c3']=[1,2,3,3,5]
print df
df1=df[df.duplicated()]
print df1

df1只有一行，这是

df1 has only one row, which is

    c1  c2  c3
3   3  a2   3

但我想要有

    c1  c2  c3
2   3  a2   3
3   3  a2   3

如何得到它？还有一件事，如果我尝试使用参数'keep'作为 df1 = df [df.duplicated（keep = False）] ，它给我错误

How to get it? One more thing, if I try to use argument 'keep' as df1 = df[df.duplicated(keep=False)], it gives me error

 Traceback (most recent call last):

 File "<ipython-input-572-188a22102b3e>", line 1, in <module>
 df1 = df[df.duplicated(keep=False)]

 File "C:\Users\Kanika\Anaconda\lib\site-packages\pandas\util\decorators.py", line 88, in wrapper
  return func(*args, **kwargs)

TypeError: duplicated() got an unexpected keyword argument 'keep'

推荐答案

您为保留指定的值。我认为，在你的情况下传递False作为保留值可能会解决问题。 熊猫重复文件。希望它有帮助。

What is the value that you specified for keep . I think, In your case Passing False as the keep value might solve the issue. Pandas Duplicated Doc's . Hope it helps.

df1 = df[df.duplicated(keep=False)]

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