pandas idxmax：在出现并列的情况下返回所有的行

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问题描述

我正在使用一个数据框架，其中每行的重量都具有其概率.现在，我想选择具有最高概率的行，并且我正在使用pandas idxmax()来做到这一点，但是当有领带时，它只是返回了绑扎的第一行.就我而言，我想获取所有领带的行.

此外，我正在作为一个研究项目的一部分，在该项目中，我正在处理以下数百万个数据框架，因此保持速度是一个问题.

示例:

我的数据看起来像这样:

data = [['chr1',100,200,0.2],
    ['ch1',300,500,0.3],
    ['chr1', 300, 500, 0.3],
    ['chr1', 600, 800, 0.3]]

在此列表中，我创建一个pandas dataframe如下:

weighted = pd.DataFrame.from_records(data,columns=['chrom','start','end','probability'])

看起来像这样:

  chrom  start  end  probability
0  chr1    100  200          0.2
1   ch1    300  500          0.3
2  chr1    300  500          0.3
3  chr1    600  800          0.3

然后选择适合Argmax(概率)的行:

selected =  weighted.ix[weighted['probability'].idxmax()]

当然哪个返回:

chrom          ch1
start          300
end            500
probability    0.3
Name: 1, dtype: object

有联系时，是否有(快速)获得所有值?

谢谢！

其他推荐答案

瓶颈在于计算布尔索引器.您可以通过与基础numpy数组进行计算来绕过与pd.Series对象关联的开销:

df2 = df[df['probability'].values == df['probability'].values.max()]

用熊猫等效制定性能基准:

# tested on Pandas v0.19.2, Python 3.6.0

df = pd.concat([df]*100000, ignore_index=True)

%timeit df['probability'].eq(df['probability'].max())               # 3.78 ms per loop
%timeit df['probability'].values == df['probability'].values.max()  # 416 µs per loop

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问题描述

I am working with a dataframe where I have weight each row by its probability. Now, I want to select the row with the highest probability and I am using pandas idxmax() to do so, however when there are ties, it just returns the first row among the ones that tie. In my case, I want to get all the rows that tie.

Furthermore, I am doing this as part of a research project where I am processing millions a dataframes like the one below, so keeping it fast is an issue.

Example:

My data looks like this:

data = [['chr1',100,200,0.2],
    ['ch1',300,500,0.3],
    ['chr1', 300, 500, 0.3],
    ['chr1', 600, 800, 0.3]]

From this list, I create a pandas dataframe as follows:

weighted = pd.DataFrame.from_records(data,columns=['chrom','start','end','probability'])

Which looks like this:

  chrom  start  end  probability
0  chr1    100  200          0.2
1   ch1    300  500          0.3
2  chr1    300  500          0.3
3  chr1    600  800          0.3

Then select the row that fits argmax(probability) using:

selected =  weighted.ix[weighted['probability'].idxmax()]

Which of course returns:

chrom          ch1
start          300
end            500
probability    0.3
Name: 1, dtype: object

Is there a (fast) way to the get all the values when there are ties?

thanks!

其他推荐答案

The bottleneck lies in calculating the Boolean indexer. You can bypass the overhead associated with pd.Series objects by performing calculations with the underlying NumPy array:

df2 = df[df['probability'].values == df['probability'].values.max()]

Performance benchmarking with the Pandas equivalent:

# tested on Pandas v0.19.2, Python 3.6.0

df = pd.concat([df]*100000, ignore_index=True)

%timeit df['probability'].eq(df['probability'].max())               # 3.78 ms per loop
%timeit df['probability'].values == df['probability'].values.max()  # 416 µs per loop

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