根据条件生成熊猫数据框

Question

我的样本数据如下：

sample_json = """{
    "P1":[
        {"Question":"Fruit",
        "Choices":["Yes","No"]}
    ],
    "P2":[
        {"Question":"Fruit Name",
        "Choices":["Mango","Apple","Banana"]}
    ],
        "P3":[
        {"Question":"Fruit color",
        "Choices":["Yellow","Red"]}
    ],
    
        "P4":[
        {"Question":"Vegetable",
        "Choices":["Yes","No"]}
    ],
    "P5":[
        {"Question":"Veggie Name",
        "Choices":["Tomato","Potato","Carrots"]}
    ],
        "P6":[
        {"Question":"Veggie Color",
        "Choices":["Red","Yellow","Brown"]}
    ],
        "P7":[
        {"Question":"Enjoy Eating?",
        "Choices":["Yes","No"]}
    ]
}"""

我正在尝试使用 pandas 生成数据框，如下所示：

import json, random
import pandas as pd

sample_data = json.loads(sample_json)

colHeaders = []
for k,v in sample_data.items():
    colHeaders.append(v[0]['Question'])

df = pd.DataFrame(columns= colHeaders)

for i in range (10):
    Answers = []
    for k,v in sample_data.items():
        Answers.append(random.choice(v[0]['Choices']))
    df.loc[len(df)] = Answers

它创建如下的df

虽然是随机的，但我想在以下条件下基于 P1 和 P4 填充它：

（附注：P1、P2、P3..P7 in sample_json）

• 如果P1.AnswerChoice = No，则将Null填入P2和P3
• 如果P4.AnswerChoice = No，则将Null填入P5和P6
• 如果P1.AnswerChoice = No和P4.AnswerChoice = No，则将Null填入p7
• P1.AnswerChoice 和 P4.AnswerChoice 不能是Yes

这样就可以产生如下数据框：

Fruit	Fruit Name	Fruit Color	Vegetable	Veggie Name	Veggie Color	Enjoy eating?
No	Null	Null	Yes	Carrots	Yellow	No
No	Null	Null	No	Null	Null	Null
Yes	Apple	Yellow	No	Null	Null	Yes
Yes	Banana	Yellow	No	Null	Null	No
No	Null	Null	Yes	Potato	Yellow	No
No	Null	Null	Yes	Tomato	Yellow	Yes
Yes	Mango	Red	No	Null	Null	Null
No	Null	Null	Yes	Carrots	Yellow	No
Yes	Apple	Yellow	No	Null	Null	No

编辑：

我想用遍历 json 的 for 循环来处理这个问题，以便为数据框准备行，而不是编辑数据框。

如果可能的话，例如在代码的以下部分：

for i in range (10):
        Answers = []
        for k,v in sample_data.items():
            Answers.append(random.choice(v[0]['Choices']))
        df.loc[len(df)] = Answers

Answer 1

在一个pd.DataFrame 中不应有两列具有相同名称。我将只使用 P1、P2、... 来指代这些列。你需要在 pandas 中建立逻辑索引：

# If P1.AnswerChoice = No, fill Null to P2 and P3
df.loc[df['P1']=='No', ['P2', 'P3']] = 'Null'

# P1.AnswerChoice = No and P4.AnswerChoice = No, fill Null to p7
df.loc[(df['P1']=='No')&(df['P4']=='No'), 'P7'] = 'Null'

# Both P1.AnswerChoice and P4.AnswerChoice cannot be Yes
df = df[(df['P1']!='Yes')&(df['P4']!='Yes')]