python - 删除没有最长列表的数据帧行

Question

我的搜索技能一定让我失望，因为这一定是一个常见问题。我有一个带有嵌套列表的数据框，并且正在尝试删除所有没有最长列表的数据框:

df = pd.DataFrame(data = [["a", "b", "c", ["d", "e"]],
                          ["a", "b", "c", ["e"]],
                          ["l", "m", "n", ["o"]], 
                  columns = ["c1", "c2", "c3", "c4"])

# max doesn't evaluate length ~ this is wrong
df.groupby(by=["c1", "c2", "c3"])["c4"].apply(max)
c1  c2  c3
a   b   c        [e]
l   m   n        [o]
Name: c4, dtype: object

# but length does ~ but using an int to equate to another row isn't guaranteed
df.groupby(by=["c1", "c2", "c3"])["c4"].apply(len)
c1  c2  c3
a   b   c     2
l   m   n     1
Name: c4, dtype: int64

必须首先对它们进行分组，因为这三列中的每一列都构成一个唯一的主 key ，我需要其中最长的列表。每个组也有不同长度的列表，对于大多数组，其大小为 1，对于其他组，它可以高达 5。最终目标应该是像这样的新数据帧:

c1  c2  c3  c4
a   b   c   ["d", "e"]
l   m   n   ["o"]

Answer 1

这个怎么样:

df = pd.DataFrame(data =[["a", "b", "c", ["d", "e"]],
                         ["a", "b", "c", ["e"]],
                         ["l", "m", "n", ["o"]]],
                  columns = ["c1", "c2", "c3", "c4"])

df['len'] = df['c4'].apply(len)

max_groups = df[df.groupby(['c1', 'c2', 'c3'])['len'].transform(max) == df['len']]

我们添加一个与c4中列表的长度相对应的额外列，然后将数据帧过滤到c4长度与c4长度相同的记录分组的最大长度为c4。这将 max_groups 返回为:

  c1 c2 c3      c4  len
0  a  b  c  [d, e]    2
2  l  m  n     [o]    1