xmind转excel

from xmindparser import xmind_to_dict
import pandas as pd
from openpyxl.workbook import Workbook
import os


# 可以设想为一个树结构，利用递归函数，获取由根至各叶子节点的路径。
def xm_parse(dic, pre_data=[]):
    """输入一个由xmindparser，转换而来的字典形式的数据，将之转换成列表"""
    title_list = []
    topic_list = []
    try:
        topics = dic.get("topics")
        title = dic.get("title")
        # 将前缀追加
        title_list.append(title)
        title_list = pre_data + title_list
        # 如果到达末尾，就返回
        if topics is None and title:
            yield title, title_list
            #             print(title,title_list)
            return
        # 如果是列表，就暂存起来（若每个对象为标准的列表，即 topics= topic_list,则可以跳过该步骤）
        elif isinstance(topics, list) and title:
            for topic in topics:
                topic_list.append(topic)
    except AttributeError as e:
        print("异常结束")
        return
    if topic_list:
        for topic in topic_list:
            yield from xm_parse(topic, title_list)


def main():
    currently_path = os.getcwd()
    for filename in os.listdir(currently_path):
        if filename.endswith('.xmind'):
            xm_path = currently_path + "/" + filename
            break
    x_flie = xm_path
    new_filename = filename[:-6] + ".xlsx"
    out_file = currently_path + "/" + new_filename
    temp = []
    max_cols = 0
    json_data = xmind_to_dict(x_flie)
    # 提取数据，并找出最大深度(列数)
    for i, j in xm_parse(json_data[0]['topic']):
        temp.append(j)
        max_cols = max_cols if max_cols > len(j) else len(j)
    # 对缺失数据采用补全
    for i in range(len(temp)):
        temp[i] = temp[i] + (max_cols - len(temp[i])) * [None]
    result = pd.DataFrame.from_records(temp, columns=["标题-{}".format(i + 1) for i in range(max_cols)])
    # result.to_excel(out_file,index=False,encoding='utf-8-sig')
    result.to_excel(out_file, index=False)


if __name__ == '__main__':
    main()