elab_mvp/excel_util.py

import pandas as pd
import openpyxl as ox
from itertools import groupby
import os


class ExcelUtil:
    # 当前项目路径
    dir_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + r'/elab_mvp/resources'
    """
        解析excel文件
    """

    def __init__(self, sheet_name=None, file_name=None):
        if file_name:
            self.path = os.path.join(self.dir_path, file_name)
        else:
            self.path = os.path.join(self.dir_path, 'mvp.xlsx')
        if sheet_name:
            self.sheet_name = sheet_name
        else:
            self.sheet_name = '硬标签+行为'

    def read_excel_by_pd(self):
        df = pd.read_excel(self.path)
        data = df.head()
        print('获取到的数据{}'.format(data))

    def read_excel_by_ox(self):
        work_book = ox.load_workbook(self.path, data_only=True)
        work_sheet = work_book.get_sheet_by_name(self.sheet_name)
        # print('max_row:{}, max_col:{}'.format(work_sheet.max_row, work_sheet.max_column))
        return work_sheet

    def init_crowd_info(self):
        """
            整理不同人群包含的父选序号
        :return:
        """
        rows = [row for row in self.read_excel_by_ox().rows]
        crowd_a = []
        crowd_b = []
        crowd_c = []
        crowd_d = []
        crowd_e = []
        crowd_f = []

        for row in rows[2:]:
            option = row[4].value
            a = row[6].value
            if a is not None and a == 1 and option not in crowd_a:
                crowd_a.append(option)
            b = row[7].value
            if b is not None and b == 1 and option not in crowd_b:
                crowd_b.append(option)
            c = row[8].value
            if c is not None and c == 1 and option not in crowd_d:
                crowd_c.append(option)
            d = row[9].value
            if d is not None and d == 1 and option not in crowd_d:
                crowd_d.append(option)
            e = row[10].value
            if e is not None and e == 1 and option not in crowd_e:
                crowd_e.append(option)
            f = row[11].value
            if f is not None and f == 1 and option not in crowd_f:
                crowd_f.append(option)
        return {'A': crowd_a, 'B': crowd_b, 'C': crowd_c, 'D': crowd_d, 'E': crowd_e, 'F': crowd_f}

    def init_mvp_data(self):
        """
            获取每个标签包括的父题父选项编号
        :return:
        """
        rows = [row for row in self.read_excel_by_ox().rows][24:]
        tag_name = None
        tag_type = None
        datas = []
        for row in rows:
            tag_type_1 = row[0].value
            tag = row[1].value
            values = row[3].value
            corr = row[4].value
            if tag_type_1:
                tag_type = tag_type_1
            if tag:
                tag_name = tag
            if values is not None and values != '找不到':
                datas.append([tag_type, tag_name, values, corr])
        result = {}
        datas.sort(key=lambda obj: obj[0])
        for tag_type, sub_datas in groupby(datas, key=lambda obj: obj[0]):
            sub_list = [x for x in sub_datas]
            sub_list.sort(key=lambda obj: obj[1])
            sub_result = {}
            for name, items in groupby(sub_list, key=lambda obj: obj[1]):
                orders = []
                for n in items:
                    orders.append([n[2], n[3]])
                sub_result[name] = orders
            result[tag_type] = sub_result
        return result

    def init_scores(self):
        work_sheet = self.read_excel_by_ox()
        rows = [row for row in work_sheet.rows]
        datas = []
        for row in rows[1:]:
            if row[0].value is not None:
                datas.append([row[0].value, row[1].value, row[2].value, row[3].value, row[4].value])
        return datas

    def init_module_info(self):
        work_sheet = self.read_excel_by_ox()
        max_column = work_sheet.max_column
        rows = [row for row in work_sheet.rows][3:]
        crowd_name = None
        datas = []
        for row in rows:
            crowd = row[1].value
            if crowd is not None:
                crowd_name = crowd
            behavior = row[2].value
            score = row[4].value
            for index in range(6, max_column - 1, 2):
                module_name = row[index].value
                if module_name is not None:
                    weight = row[index + 1].value
                    datas.append([crowd_name, behavior, score, module_name, weight])
        results = {}
        datas.sort(key=lambda obj: obj[0])
        for name, items in groupby(datas, key=lambda obj: obj[0]):
            sub_results = {}
            sub_list = []
            for it in items:
                sub_list.append([x for x in it])
            sub_list.sort(key=lambda obj: obj[3])
            for name_1, itmes_1 in groupby(sub_list, key=lambda obj: obj[3]):
                sub_data = []
                for n in itmes_1:
                    # print('         {}'.format(n[1]))
                    sub_data.append([n[1], n[2], n[4]])
                sub_results[name_1] = sub_data
            results[name] = sub_results
        return results