如何计算 pandas 数据框中的重复行? [英] How to count duplicate rows in pandas dataframe?

问题描述

我正在尝试计算数据框中每种类型的行的重复项.例如，假设我在熊猫中有一个数据框，如下所示:

I am trying to count the duplicates of each type of row in my dataframe. For example, say that I have a dataframe in pandas as follows:

df = pd.DataFrame({'one': pd.Series([1., 1, 1]),
                   'two': pd.Series([1., 2., 1])})

我得到一个看起来像这样的df:

I get a df that looks like this:

    one two
0   1   1
1   1   2
2   1   1

我想第一步是找到所有不同的唯一行，我这样做是:

I imagine the first step is to find all the different unique rows, which I do by:

df.drop_duplicates()

这给了我以下df:

    one two
0   1   1
1   1   2

现在，我想从上面的df([1 1]和[1 2])中获取每一行，并计算出初始df中每行的次数.我的结果看起来像这样:

Now I want to take each row from the above df ([1 1] and [1 2]) and get a count of how many times each is in the initial df. My result would look something like this:

Row     Count
[1 1]     2
[1 2]     1

我应该怎么做最后一步?

How should I go about doing this last step?

这里有一个更大的例子，可以使它更清楚:

Here's a larger example to make it more clear:

df = pd.DataFrame({'one': pd.Series([True, True, True, False]),
                   'two': pd.Series([True, False, False, True]),
                   'three': pd.Series([True, False, False, False])})

给我:

    one three   two
0   True    True    True
1   True    False   False
2   True    False   False
3   False   False   True

我想要一个告诉我的结果:

I want a result that tells me:

       Row           Count
[True True True]       1
[True False False]     2
[False False True]     1

推荐答案

您可以在所有列上groupby并调用size，索引指示重复的值:

You can groupby on all the columns and call size the index indicates the duplicate values:

In [28]:
df.groupby(df.columns.tolist(),as_index=False).size()

Out[28]:
one    three  two  
False  False  True     1
True   False  False    2
       True   True     1
dtype: int64

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