从包含数据帧中元组列表的每个单元格中检索最大值



我有一个熊猫数据帧,df[lists],它包含整数和字符串,它具有以下格式:

0 [(a,b,89), (a,y,992), (a,t, 99), (a,m, 1028)]
1 [(b,u,855), (b,tt,934), (b, g, 69)]
2 [(c,k, 546),(c,gf,134), (c, dd, 569)]
3 [(d,zv, 546),(d,gyr,8834), (d, dds, 5693), (d, ddd, 3459)]

实际上字符 a、b、tt 等更长,并使用计算汉明距离我想得到的是每一行中的最大值,并将其写为 df[max]:

0 [1028]
1 [934]
2 [569]
3 [8834]

我通过使用:

combined = ((x, y, (5x - 3y) for x, y in combinations(df['elements'], if x != y) 
series = Series(list(g) for k, g in groupby(combined, key=itemgetter(0)))
series = df[lists]

当我使用时:

from operator import itemgetter
df['lst'].apply(lambda x: [max(x, key=itemgetter(2))[-1]])

我收到以下错误:

Traceback (most recent call last):
  File "C:UsersDesktopphashdene_2.py", line 78, in <module>
    df['similarity'].apply(lambda x: [max(x, key=itemgetter(2))[-1]])
  File "C:UsersAppDataLocalProgramsPythonPython35libsite-packagespandascoreseries.py", line 2294, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandassrcinference.pyx", line 1207, in pandas.lib.map_infer (pandaslib.c:66124)
  File "C:UsersDesktopphashdene_2.py", line 78, in <lambda>
    df['similarity'].apply(lambda x: [max(x, key=itemgetter(2))[-1]])
TypeError: 'float' object is not iterable

最好的选择是使用不太快的apply变体。假设列名包含list个单元格,由 "lst" 表示,您可以抓取元组中存在的每三个元素,并通过比较它们找到最大值。然后从计算tuple中,选择它的最后一个元素并将其转换为单个项目list

from operator import itemgetter
df['lst'].apply(lambda t: [max(t, key=itemgetter(2))[-1]])
0    [1028]
1     [934]
2     [569]
3    [8834]
Name: lst, dtype: object

使用的数据:

df = pd.DataFrame(dict(lst=[[('a','b', 89), ('a','y', 992), ('a','t', 99), ('a','m', 1028)], 
                            [('b','u', 855), ('b','tt', 934), ('b', 'g', 69)],
                            [('c','k', 546),('c','gf', 134), ('c', 'dd', 569)], 
                            [('d','zv', 546),('d','gyr', 8834), ('d', 'dds', 5693), ('d', 'ddd', 3459)]]))

编辑:

由于可能存在映射为float对象的缺失值,因此您可以根据单元格的类型过滤单元格并对其执行迭代,并使其他单元格保持不变:

df['lst'].apply(lambda t: [max(t, key=itemgetter(2))[-1] if isinstance(t, list) else t])

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