將 Pandas 數據幀轉換為 JSON 並分隔字符串

Question

我有一個名為“df”的 pandas.dataframe，格式如下：

團隊名字	Positive_Sentiment	負面情緒
第一組	樂於助人，大力支持	客戶服務緩慢，界面薄弱，管理不善

我想將此數據幀轉換為具有以下格式的 JSON 文件：

[{
"Group Name": "group1",
"Postive Sentiment": [
"helpful",
"great support"
],
"Negative Sentiment": [
"slow customer service",
"weak interface",
"bad management"
]
}
]

到目前為止，我已經使用了這個：

    import json
    b = []
    for i in range(len(df)):
        x={}
        x['Group Name']=df.iloc[i]['group_name']
        x['Positive Sentiment']= [df.iloc[i]['Positive_Sentiment']]
        x['Negative Sentiment']= [df.iloc[i]['Negative_Sentiment']]
        b.append(x)
    
    ##Export
    with open('AnalysisResults.json', 'w') as f:
        json.dump(b, f, indent = 2)

這導致：

[{
"Group Name": "group1",
"Postive Sentiment": [
"helpful,
great support"
],
"Negative Sentiment": [
"slow customer service,
weak interface,
bad UX"
]
}
]

你可以看到它非常接近。 關鍵的區別在於每行的整個內容周圍都用雙引號（例如，“有用，非常支持”）而不是行中的每個逗號分隔字符串（例如，“有用”，“非常支持”）。 我想在每個字符串周圍加上雙引號。

Answer 1

您可以將split(",")應用於您的列：

from io import StringIO
import pandas as pd
import json

inp = StringIO("""group_name    Positive_Sentiment  Negative_Sentiment
group1  helpful, great support  slow customer service, weak interface, bad management
group2  great, good support     interface meeeh, bad management""")

df = pd.read_csv(inp, sep="\s{2,}")
df["Positive_Sentiment"] = df["Positive_Sentiment"].apply(lambda x : x.split(","))
df["Negative_Sentiment"] = df["Negative_Sentiment"].apply(lambda x : x.split(","))

print(json.dumps(df.to_dict(orient="record"), indent=4))

輸出：

[
    {
        "group_name": "group1",
        "Positive_Sentiment": [
            "helpful",
            " great support"
        ],
        "Negative_Sentiment": [
            "slow customer service",
            " weak interface",
            " bad management"
        ]
    },
    {
        "group_name": "group2",
        "Positive_Sentiment": [
            "great",
            " good support"
        ],
        "Negative_Sentiment": [
            "interface meeeh",
            " bad management"
        ]
    }
]