将有父子级的dict转换成有父子级的excel

（当前处理方式只适用于层数较少时，层数多了就会很麻烦，且原始数据中还有部分需要与字典值进行对应，所以代码并不纯粹，后续有时间再进行精简）

• 我这边的数据字典结构数据多少不定，层级多少不定，不过最多只有三层
• 先将所有的父子级别的字典按照|分隔符号生成新的字典

{
  父级|子级|孙级别：具体值
}

• 将上一步骤得到的新的字典通过pandas生成一个DataFrame对象
• 将空值赋值其他默认值
• split_r = pf.to_dict(orient='split')将DataFrame转回一个新的字典，得到三块列名，行数，数据（二维数组）
• 再进行循环将列名拆分开分成三级，然后生成新的DataFrame对象
• 将DataFrame转换成excel

之所以会分那么多步骤，是因为字典数据不定长，通过pandas的DataFrame对象，能帮助将不定长数据也按照对应填充

import pandas as pd
import numpy as np
def result_data_processing(result_data, result_dict):
    """
    将数据多层数据处理成键值对应
    :param result_data: 原始数据
    :param result_dict: 原始数据字典
    :return: 处理好的数据
    """
    # 生产结果字典列表
    excel_dict_list = []
    # excel名称列表
    listing_list = []
    # 遍历第一层患者基本信息
    for patient_key, patient_value in result_data.items():
        excel_dict = {}
        excel_dict["患者ID"] = patient_key
        # 遍历第二层crf文档
        for crf_key_id, crf_value in patient_value.items():
            # 有部分数据主表中没有，但是子表中有
            if crf_key_id not in result_dict.keys():
                result_dict[crf_key_id] = crf_key_id
            crf_key = result_dict[crf_key_id]
            # 粒度是页面，会有下一层
            if type(crf_value) == dict:
                # 遍历第三层 页面
                for page_key, page_value in crf_value.items():
                    # 如果粒度是截面
                    if type(page_value) == dict:
                        # 遍历第四层 截面
                        for section_key, section_value in page_value.items():
                            excel_dict[crf_key + "|" + page_key + "|" + section_key] = section_value
                    else:
                        excel_dict[crf_key + "|" + page_key] = page_value
            else:
                excel_dict[crf_key] = crf_value
        excel_dict_list.append(excel_dict)
        # 生成名称顺序列表
        for excel_dict_key in excel_dict.keys():
            if excel_dict_key not in listing_list:
                listing_list.append(excel_dict_key)

    return [excel_dict_list, listing_list]

# 先将所有的父子级别的字典按照|分隔符号生成新的字典
excel_dict_list = result_data_processing(yuan, {"1": "1111"})


# 将字典列表转换为DataFrame
pf = pd.DataFrame(list(excel_dict_list[0]))
# 指定列名顺序
pf = pf[excel_dict_list[1]]
# 将DataFrame中空的替换成固定值
pf.fillna('0.0%',inplace=True)
# DataFrame按照要求转dict
split_r = pf.to_dict(orient='split')
# 拿到所有字典的key-也是DataFrame的列名
columns_name = split_r["columns"]
# 拿到所有数据
all_data = split_r["data"]

# 创建新的结果字典
columns_one=[]
columns_two=[]
columns_three=[]
for columns in columns_name:
    if columns == "患者ID":
        columns_one.append(columns)
        columns_two.append(columns)
        columns_three.append(columns)
    else:
        columns_tree = columns.split("|")
        if len(columns_tree) == 1:
            columns_one.append(columns_tree[0])
        elif len(columns_tree) == 2:
            columns_one.append(columns_tree[0])
            columns_two.append(columns_tree[1])
        elif len(columns_tree) == 3:
            columns_one.append(columns_tree[0])
            columns_two.append(columns_tree[1])
            columns_three.append(columns_tree[2])
# 将二维数组转换称numpy的二维数组
numpy_data = np.array(all_data)
if columns_three == ["患者ID"] and columns_two != ["患者ID"]:
    pf = pd.DataFrame(numpy_data, index=split_r["index"],
                      columns=[columns_one, columns_two])
elif columns_two == ["患者ID"]:
    pf = pd.DataFrame(numpy_data, index=split_r["index"],
                      columns=[columns_one])
else:
    pf = pd.DataFrame(numpy_data, index=split_r["index"],
                      columns=[columns_one, columns_two, columns_three])
print(pf)

file_path = pd.ExcelWriter('test2.xlsx')
# 替换空单元格
pf.fillna('0.0%', inplace=True)
# 输出
pf.to_excel(file_path, encoding='utf-8', index=True)
# 保存表格
file_path.save()

我这边的数据结构

{
    "26404112": {
        "e216b428-e123-492a-af79-c9ffe72271a9": {
            "产检情况": {
                "孕产妇基本情况": "85.71%",
                "孕周": "100.0%",
                "不适主诉": "100.0%",
                "妊娠合并症": "0.0%"
            },
            "实验室检查": {
                "血脂": "100.0%",
                "24小时尿蛋白定量": "100.0%",
                "尿常规": "94.12%",
                "肾功能": "85.71%",
                "血糖及微量元素": "80.0%",
                "凝血功能": "100.0%",
                "肝功能": "100.0%",
                "电解质": "87.5%",
                "甲状腺功能": "85.71%",
                "血常规": "100.0%"
            },
            "影像学检查": {
                "产科B超检查": "75.0%",
                "心电图检查": "100.0%"
            },
            "患者信息": {
                "不良嗜好": "50.0%",
                "基础信息": "100.0%",
                "家族史": "0.0%",
                "月经婚育史": "92.5%",
                "既往史": "93.55%"
            }
        },
        "8e078749-f263-43be-a371-d8ccf4656ac2": {
            "产检和实验室检查": {
                "产科检查": "53.33%",
                "孕产妇基本情况": "0.0%",
                "尿常规": "87.5%",
                "妊娠合并症": "0.0%",
                "孕周": "100.0%",
                "不适主诉": "100.0%"
            }
        }
    },
    "111111": {
        "e216b428-e123-492a-af79-c9ffe72271a9": {
            "产检情况": {
                "孕产妇基本情况": "85.71%",
                "孕周": "100.0%",
                "不适主诉": "100.0%",
                "妊娠合并症": "0.0%"
            },
            "患者信息": {
                "不良嗜好": "50.0%",
                "基础信息": "100.0%",
                "家族史": "0.0%",
                "月经婚育史": "92.5%",
                "既往史": "93.55%"
            }
        },
        "e1190c2d-0032-477c-b051-6ffdffb208b6": {
            "第一页": {
                "疑似子痫前期：满足>1项检查发现": "100.0%",
                "排除：明显的子痫前期、确诊的HELLP综合症、90天内服用过药物": "100.0%",
                "疑似子痫前期：首次出现尿蛋白": "100.0%",
                "疑似子痫前期：满足>1项症状": "100.0%",
                "患者信息": "57.14%",
                "排除：患者登记之后进一步排除部分病例": "100.0%",
                "疑似子痫前期：血压首次升高": "85.19%",
                "疑似子痫前期：血压进一步升高": "63.64%"
            }
        }
        "201444cc-3788-43ef-bcd4-3fcdb3555e6b": {
            "辅助检查": {
                "肝功能": "100.0%",
                "血常规": "100.0%",
                "肾功能": "85.71%",
                "电解质": "0.0%",
                "凝血功能": "92.31%",
                "心电图检查": "50.0%",
                "血脂": "87.5%",
                "尿常规": "85.71%"
            },
            "产检信息": {
                "妊娠合并症": "0.0%",
                "孕周": "0.0%",
                "产科检查": "87.5%",
                "不适主诉": "100.0%",
                "孕产妇基本情况": "0.0%"
            }
        }
        "8e078749-f263-43be-a371-d8ccf4656ac2": {
            "产检和实验室检查": {
                "产科检查": "53.33%",
                "孕产妇基本情况": "0.0%",
                "尿常规": "87.5%",
                "妊娠合并症": "0.0%",
                "孕周": "100.0%",
                "不适主诉": "100.0%"
            }
        }
    }
}