如何将pandas数据框转换为多个命名元组列表

Question

如何将pandas数据框转换为多个命名元组列表

3

我正在处理一段代码，需要将放置在列表中的多个NamedTuple映射起来。以下是示例代码 - 我主要的问题在于如何映射双重NamedTuplePeopleName和PeopleAge的List - 我不清楚该如何操作。应该分为两步完成，1 / 将整行提取成通用的NamedTuple，然后2 / 将记录拆分为不同的NamedTuple PeopleName和PeopleAge。

from typing import NamedTuple, List

import pandas as pd

data = [["tom", 10, "ab 11"], ["nick", 15, "ab 22"], ["juli", 14, "ab 11"]]
people = pd.DataFrame(data, columns=["Name", "Age", "PostalCode"])

PeopleName = NamedTuple("PeopleName", [("Name", str)])
PeopleAge = NamedTuple("PeopleAge", [("Age", int)])
PeoplePC = NamedTuple("PeoplePC", [("PostalCode", str)])

# The code below is not correct
Demography = NamedTuple(
    "Demography", [("names", List[(PeopleName, PeopleAge)]), ("postalcodes", PeoplePC)],
)


def to_nested_tuple(k, g):
    peoples = list(
        g["Name"].to_frame().itertuples(name="Person", index=False),
        # rec["Age"].to_frame().itertuples(name="PeopleAge", index=False),
    )
    return Demography(peoples, PeoplePC(k))


d = [to_nested_tuple(*item) for item in people.groupby("PostalCode")]

print(d)

- Michael

请您能否分享一些示例输出，我不太确定您试图做什么。 - AMC

2个回答

1

使用list(df.itertuples())，其中df是您的数据框。

- Baudouin Théobald

这是我想要做的事情，但对于列表中的每个数据帧记录，我应该有一个对PeopleAge和PeopleName的双重映射 - 最终我应该有一个(peopleAge和peopleName)列表，其中一个对应的PeoplePC如果有意义的话。 - Michael

网页内容由stack overflow 提供, 点击上面的

可以查看英文原文，
原文链接

- RomanPerekhrest · Accepted Answer

这个注释List [(PeopleName，PeopleAge)]会抛出TypeError：typing.List的参数太多；实际为2，期望为1。

那个包含2种不同类型的元组也应该用typing.Tuple进行注释：

List[Tuple[PeopleName, PeopleAge]]

然而，为了注释参数，最好使用抽象集合类型，例如Sequence或Iterable：

Demography = NamedTuple(
    "Demography", [("names", Sequence[Tuple[PeopleName, PeopleAge]]), ("postalcodes", PeoplePC)],
)

不必为每个组应用to_nested_tuple，我会直接按照以下方式进行：

d = [Demography([(PeopleName(row['Name']), PeopleAge(row['Age'])) for _, row in group.iterrows()], PeoplePC(k))
     for k, group in people.groupby("PostalCode")]

现在，结果将被打印为：

[Demography(names=[(PeopleName(Name='tom'), PeopleAge(Age=10)), (PeopleName(Name='juli'), PeopleAge(Age=14))], postalcodes=PeoplePC(PostalCode='ab 11')),
 Demography(names=[(PeopleName(Name='nick'), PeopleAge(Age=15))], postalcodes=PeoplePC(PostalCode='ab 22'))]