熊猫:研究对象的两两比较，以消除占主导地位的替代品

Question

我有大量的实验结果数据，我需要对这些数据进行分类，以消除多个标准上的“主导”主题。下面的“玩具”数据反映了总体结构，但不一定是“实验”数据的尺寸。

df = pd.DataFrame({'Subject': ['Alpha', 'Bravo', 'Charlie'],
                   'A': [6, 7, 8],
                   'B': [11, 7, 12],
                   'C': [13, 6, 6],
                   'D': [5, 9, 4],
                   'E': [11, 9, 5],
                   'F': [9, 10, 3],
                   'G': [2, 6, 5],
                   'H': [8, 12, 11]})

     Subject  A     B      C     D     E     F     G      H
0    Alpha    6     11     13    5     11    9     2      8
1    Bravo    7     7      6     9     9     10    6      12
2    Charlie  8     12     6     4     5     3     5      11

如何使用“小于”成对比较生成以下结果。

[0, 1]: w=5, l=3, d=0
[0, 2]: w=4, l=4, d=0
[1, 2]: w=2, l=5, d=1

并将它们与以下伪码结合起来，创建主导主题“Bravo”的子集，并将其从原始数据中删除？

tx = 3
i = 0

subject[0]='Alpha'
subject[1]='Bravo'

if w > l and l < tx
then y[i] = subject[0]
     z[i] = subject[1]
elseif w < l and w < tx
then y[i] = subject[1]
     z[i] = subject[0]

i += 1

请指点？

Answer 1

下面的代码似乎工作正常

def pairwise_compare(dfq, pairs, tx):
    winners = []
    losers = []
    for pair in pairs:
        w = 0
        l = 0
        x = 0
        for i in dfq['Subject']:
            for j in dfq['Subject']:
                if i == pair[0] and j == pair[1]:
                    alt_first = dfq.loc[dfq['Subject'] == i, 'A':'H'].values
                    alt_second = dfq.loc[dfq['Subject'] == j, 'A':'H'].values
                    diffs = (alt_first - alt_second).astype(int)
                    w = np.sum(diffs < 0)
                    l = np.sum(diffs > 0)
                    x = np.sum(diffs == 0)
                    if w > l and l < tx:
                        winners.append(i)
                        losers.append(j)
                    elif w < l and w < tx:
                        winners.append(j)
                        losers.append(i)
    return winners, losers

pair_order_list = itertools.combinations(df['Subject'],2)
pairs = list(pair_order_list)

print('')
tx = 3
winners, losers = pairwise_compare(df, pairs, tx)

print('')
for winner, loser in zip(winners, losers):
    df.drop(df[df['Subject'] == loser].index, inplace=True)
    print(f'{loser} is dominated by {winner}')

df.set_index('Subject', inplace=True)
print('')
print(df)

并产生所需的输出。

Bravo is dominated by Charlie

         A   B   C  D   E  F  G   H
Subject                            
Alpha    6  11  13  5  11  9  2   8
Charlie  8  12   6  4   5  3  5  11

如果有一位“熊猫”专家能制作出一个更地道的版本，我将不胜感激！