如何从嵌套字典创建指定格式的多层索引pandas DataFrame

实现代码 #

import pandas as pd

# 此处替换为你自己的嵌套字典
raw_dict = {
    0: {
        "geometry": {
            "type": "Point",
            "coordinates": [75.4516454, 27.2520587]
        },
        "type": "Feature",
        "properties": {
            "state": "Rajasthan",
            "code": "BDHL",
            "name": "Badhal",
            "zone": "NWR",
            "address": "Kishangarh Renwal, Rajasthan"
        }
    }
}

# 提取行数据与对应索引
processed_rows = []
row_index = []
for key, val in raw_dict.items():
    row_index.append(key)
    processed_rows.append([
        val["geometry"]["type"],
        val["geometry"]["coordinates"],
        val["type"],
        val["properties"]["state"],
        val["properties"]["code"],
        val["properties"]["name"],
        val["properties"]["zone"],
        val["properties"]["address"]
    ])

# 构造双层列索引
multi_col = pd.MultiIndex.from_tuples([
    ("Geometry", "Type"),
    ("Geometry", "Coordinates"),
    ("Type", ""),
    ("Properties", "State"),
    ("Properties", "Code"),
    ("Properties", "Name"),
    ("Properties", "Zone"),
    ("Properties", "Address")
])

# 生成目标DataFrame
target_df = pd.DataFrame(processed_rows, index=row_index, columns=multi_col)

# 打印验证结果
print(target_df)

关键说明 #

• 遍历原始嵌套字典时直接收集第一层键作为行索引列表，传入DataFrame的index参数即可保留原始键作为行主索引
• 用pd.MultiIndex.from_tuples定义双层列的层级关系，无二级子列的顶层列（如示例中的Type）二级索引设为空字符串即可
• 若原始数据存在字段缺失，可在遍历取值时增加判断逻辑补入默认值，避免报错

内容的提问来源于stack exchange，提问作者saransh agarkar