将数据框行转换为Python集 [英] Convert dataframe rows to Python set

问题描述

我有这个数据集:

import pandas as pd
import itertools

A = ['A','B','C']
M = ['1','2','3']
F = ['plus','minus','square']

df = pd.DataFrame(list(itertools.product(A,M,F)), columns=['A','M','F'])
print(df)

示例输出如下:

   A  M       F
0   A  1    plus
1   A  1   minus
2   A  1  square
3   A  2    plus
4   A  2   minus
5   A  2  square

我想从该数据帧中逐行比较(雅卡相似度)，例如，比较

I want to pairwise comparison (jaccard similarity) of each row from this data frame, for example, comparing

A 1 plus和A 2 square并获得两者之间的相似性值.

A 1 plus and A 2 square and get the similarity value between those both set.

我写了一个jaccard函数:

I have wrote a jaccard function:

def jaccard(a, b):
    c = a.intersection(b)
    return float(len(c)) / (len(a) + len(b) - len(c))

因为我使用了intersection

我想要这样的输出(此预期结果值只是随机数):

I want the output like this (this expected result value is just random number):

    0     1     2     3     45
0  1.00  0.43  0.61  0.55  0.46
1  0.43  1.00  0.52  0.56  0.49
2  0.61  0.52  1.00  0.48  0.53
3  0.55  0.56  0.48  1.00  0.49
45  0.46  0.49  0.53  0.49  1.00

获得成对度量标准结果的最佳方法是什么?

What is the best way to get the result of pairwise metrics?

谢谢

推荐答案

可以在此处找到所需内容的完整实现:​​

A full implementation of what you want can be found here:

series_set = df.apply(frozenset, axis=1)
new_df = series_set.apply(lambda a: series_set.apply(lambda b: jaccard(a,b)))

这篇关于将数据框行转换为Python集的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！