如何将object作为值的dict转换为dataframe？

Question

我有一个以object为值的Dict，我想从中创建一个DF （忽略“南”）

list_of_actors[key] = value

key -> string

value -> Actor()

class Actor:
  def __init__(self,title,link):
    self.link = link
    self.title = title
    self.count = 1
    self.yearOfBirth ="NaN"
    self.countryOfBirth ="NaN" 
    self.numberOfAwards = "0"

columns = ['Name', 'Year of birth', 'Country of birth', 'Awards']

Name = self.title

Year of birth = self.yearOfBirth 

Country of birth = self.countryOfBirth

Awards = self.numberOfAwards

Answer 1

对我来说，你想要什么还不是100%清楚。但我假设您想将Actor实例列表转换为数据帧。你知道吗

我们可以在Actor上添加一个to_dict()方法，将参与者序列化到字典中，并通过简单地使用pandas.DataFrame()将此类字典的列表转换为数据帧：

import pandas as pd
import typing as tp


class Actor:
    def __init__(
        self,
        title: tp.Optional[str] = None,
        link: tp.Optional[str] = None,
        count: int = 1,
        year_of_birth: tp.Optional[int] = None,
        country_of_birth: tp.Optional[str] = None,
        number_of_awards: int = 0
    ):
        self.link = link
        self.title = title
        self.count = count
        self.yearOfBirth = year_of_birth
        self.countryOfBirth = country_of_birth
        self.numberOfAwards = number_of_awards

    def to_dict(self) -> dict:
        return {
            "Name": self.title,
            "Year of Birth": self.yearOfBirth,
            "Country of Birth": self.countryOfBirth,
            "Awards": self.numberOfAwards,
        }

actors = [Actor() for i in range(10)]  # Just to create a list of 10 
                                       # actors, put your actual actors 
                                       # list here 

actor_dicts = [actor.to_dict() for actor in actors]

df = pd.DataFrame(actor_dicts)

>>> df

   Name Year of Birth Country of Birth  Awards
0  None          None             None       0
1  None          None             None       0
2  None          None             None       0
3  None          None             None       0
4  None          None             None       0
5  None          None             None       0
6  None          None             None       0
7  None          None             None       0
8  None          None             None       0
9  None          None             None       0