python - 如何转换键值对 Pandas dataframe

Question

我收到了这个数据集，其中包含 a.csv 格式的键值对中的房地产数据。

如果我删除第一行，我可以用 Pandas 加载它并得到一个 dataframe，如下所示：

编号 1	[{'键'": '"地板'"	'"value'": '"2. Floor'"}	{'“关键'”：'“可用_日期'”	"价值'": '"nach Vereinbarung'"}
编号 2	[{'键'": '"地板'"	'"value'": '"1. Floor'"}	{'“钥匙'”：'“生活空间'”	“价值”：81.0}
编号 3	[{'key'": '"生活空间'"	'“价值”：240.0}	{'“key'”：'“construction_year'”	'“价值”：2012}
编号 4	[{'key'": '"生活空间'"	'“价值”：280.0}	{'“key'”：'“construction_year'”	'“价值”：1851}

但是，我不知道如何使用 Python 中的键值对，所以我想将此数据转换为 Pandas dataframe，其中“键”作为标题，每行中它们各自的值，如下所示：

ID	地面	可用_日期	居住空间	施工年
编号 1	2.地板		交际会
编号 2	1.地板	81
编号 3		240.0		2012
编号 4		280.0		1851年

我找到了很多关于如何将 Pandas dataframe 转换为键值对的说明，但反之则不然......

先感谢您。

更新

我的数据内容如下所示：

print(df.head(10))
           [{'key'": '"floor'"   '"value'": '"3. Stock'"}        {'"key'": '"living_space'"    '"value'": 50.0}      {'"key'": '"available_date'"  ... Unnamed: 49 Unnamed: 50 Unnamed: 51 Unnamed: 52 Unnamed: 53
0          [{'key'": '"floor'"   '"value'": '"2. Stock'"}        {'"key'": '"living_space'"   '"value'": 113.0}   {'"key'": '"construction_year'"  ...         NaN         NaN         NaN         NaN         NaN
1          [{'key'": '"floor'"   '"value'": '"1. Stock'"}        {'"key'": '"living_space'"    '"value'": 52.0}   {'"key'": '"construction_year'"  ...         NaN         NaN         NaN         NaN         NaN
..                         ...                        ...                               ...                 ...                               ...  ...         ...         ...         ...         ...         ...
8   [{'key'": '"living_space'"          '"value'": 240.0}   {'"key'": '"construction_year'"    '"value'": 2012}      {'"key'": '"available_date'"  ...         NaN         NaN         NaN         NaN         NaN
9   [{'key'": '"living_space'"          '"value'": 280.0}   {'"key'": '"construction_year'"    '"value'": 1851}      {'"key'": '"available_date'"  ...         NaN         NaN         NaN         NaN         NaN

[10 rows x 54 columns]

更新

.csv 的内容如下所示（对于第 2 次观察）：

1,[{'key'"": '""floor'"""," '""value'"": '""3. Stock'""}","{'""key'"" : '""living_space'"""," '""value'"": 50.0}"," {'""key'"": '""available_date'"""," '""value'"" : '""01.04.2022'""}"," {'""键'"": '""有用区域'""", """值'"": 60.0}","{'"" key'"": '""pets_allowed'"""," '""value'"": true}"," {'""key'"": '""child_friendly'"""," '"" value'"": true}"," {'""key'"": '""balcony'"""," '""value'"": true}"," {'""key'"" : '""parking_outdoor'"""," '""value'"": true}"," {'""key'"": '""lift'"""," '""value'"" : true}"," {'""key'"": '""cable_tv'"""," '""value'"": true}]""","[{'date'"": ' ""2022-02-25'""","'""price_amount'"": 1550}]"""

2,[{'key'"": '""floor'"""," '""value'"": '""2. Stock'""}","{'""key'"" : '""living_space'"""," '""value'"": 113.0}"," {'""key'"": '""construction_year'"""," '""value'"" : 2022}"," {'""key'"": '""available_date'"""," '""value'"": '""01.04.2022'""}","{'"" key'"": '""wheelchair_accessible'"""," '""value'"": true}"," {'""key'"": '""child_friendly'"""," '"" value'"": true}"," {'""key'"": '""balcony'"""," '""value'"": true}"," {'""key'"" : '""parking_indoor'"""," '""value'"": true}"," {'""key'"": '""lift'"""," '""value'"" : true}]""","[{'日期'"": '""2022-02-27'"""," '""price_amount'"": 2990}]"""

这些数据似乎是从房地产在线市场中删除的。 我认为也与 state 有关，即每次观察都有不同数量的特征。

Answer 1

可能的解决方案如下：

文件“data.csv”内容

1,"[{'key'"": '""floor'"""," '""value'"": '""3. Stock'""}"," {'""key'"": '""living_space'"""," '""value'"": 50.0}"," {'""key'"": '""available_date'"""," '""value'"": '""01.04.2022'""}"," {'""key'"": '""useful_area'"""," '""value'"": 60.0}"," {'""key'"": '""pets_allowed'"""," '""value'"": true}"," {'""key'"": '""child_friendly'"""," '""value'"": true}"," {'""key'"": '""balcony'"""," '""value'"": true}"," {'""key'"": '""parking_outdoor'"""," '""value'"": true}"," {'""key'"": '""lift'"""," '""value'"": true}"," {'""key'"": '""cable_tv'"""," '""value'"": true}]""","[{'date'"": '""2022-02-25'"""," '""price_amount'"": 1550}]"""
2,"[{'key'"": '""floor'"""," '""value'"": '""2. Stock'""}"," {'""key'"": '""living_space'"""," '""value'"": 113.0}"," {'""key'"": '""construction_year'"""," '""value'"": 2022}"," {'""key'"": '""available_date'"""," '""value'"": '""01.04.2022'""}"," {'""key'"": '""wheelchair_accessible'"""," '""value'"": true}"," {'""key'"": '""child_friendly'"""," '""value'"": true}"," {'""key'"": '""balcony'"""," '""value'"": true}"," {'""key'"": '""parking_indoor'"""," '""value'"": true}"," {'""key'"": '""lift'"""," '""value'"": true}]""","[{'date'"": '""2022-02-27'"""," '""price_amount'"": 2990}]"""

---

import pandas as pd
import json

# read data from csv file
with open("data.csv", "r", encoding="utf-8") as file:
    data = file.read().replace('"', '').replace("'", '"').replace("[", '').replace("]", '').splitlines()

# convert string to list
data_dict = [json.loads("[" + d + "]") for d in data]

data_all = []

for list_item in data_dict:
    data_prepared = {}
    for idx, item in enumerate(list_item):
        if idx == 0:
            data_prepared["id"] = item
        else:
            if 'key' in item:
                data_prepared[item['key']] = item['value']
            else:
                data_prepared.update(item)
    data_all.append(data_prepared)

# create dataframe
df = pd.DataFrame(data_all)
df = df.fillna("-")
df = df.replace(True, 'Yes')
df = df.replace(False, 'No')
df

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