python操作excel

关于excel，大部分操作应该主要用于数据分析，其中，最主流的方式应该是采用pandas库，这里主要介绍几种方式来读写excel

读取excel数据

处理一般的excel，尤其没有合并单元格之类的情况时，我们可以采用普通的方法

这里采用xlrd库

• 无单元格合并的情况下

  import xlrd
  
  def parse_list(data):
      """
      parse a list
      :param data: list
      :return:
      """
      data_list = []
      for i in range(1, len(data)):
          data_dict = {}
          for index, key in enumerate(data[0]):
              data_dict[key] = data[i][index]
          data_list.append(data_dict)
      return data_list
  
  def read_excel(filename, sheet=None):
      """
      read excel and parse every line
  
      :param name: file of absolute path
      :return: some dict which is about everyline of list
      """
      workbook = xlrd.open_workbook(filename)
      total_data = []
      if sheet:
          sh = workbook.sheet_by_name(sheet)
          for row in range(0, sh.nrows):
              row_list = sh.row_values(row)
              total_data.append(row_list)
      return parse_list(total_data)
  

  上述方法主要针对特定的sheet，当然，根据上述方法也也可进行改变，读取所有的sheet

  关键方法在于read_excel，所以我们来改写该方法

  import xlrd
  def read_excel(filename):
      """
      read excel and parse every line
  
      :param name: file of absolute path
      :return: some dict which is about everyline of list
      """
      workbook = xlrd.open_workbook(filename)
      total_data = []
      name_sheets = workbook.sheet_names()
      for sheet in name_sheets:
          sh = workbook.sheet_by_name(sheet)
          for row in range(0, sh.nrows):
              row_list = sh.row_values(row)
              total_data.append(row_list)
      return parse_list(total_data)
  

• 主要用于处理合并存在合并单元格的情况

  import xlrd
  
  
  def get_excel():
      data_list = []
      with xlrd.open_workbook(r‘D:\0325.xlsx‘) as workbook:
          name_sheets = workbook.sheet_names()  # 获取Excel的sheet表列表，存储是sheet表名
          for index in name_sheets:  # for 循环读取每一个sheet表的内容
              apply_dic = []
              sheet_info = workbook.sheet_by_name(index)  # 根据表名获取表中的所有内容，sheet_info也是列表，列表中的值是每个单元格里值
              first_line = sheet_info.row_values(0)  # 获取首行，我这里的首行是表头，我打算用表头作为字典的key，每一行数据对应表头的value，每一行组成一个字典
              values_merge_cell = merge_cell(sheet_info)  # 这里是调用处理合并单元格的函数
              for i in range(1, sheet_info.nrows):  # 开始为组成字典准备数据
                  other_line = sheet_info.row_values(i)
                  for key in values_merge_cell.keys():
                      if key[0] == i:
                          other_line[key[1]] = values_merge_cell[key]
                  # print(other_line)
                  dic = list_dic(first_line, other_line)  # 调用组合字典的函数，传入key和value，字典生成
                  apply_dic.append(dic)
              data_list.append({‘index‘: index, ‘data‘: apply_dic})
      return data_list
  
  
  def list_dic(list1, list2):
      ‘‘‘
      two lists merge a dict,a list as key,other list as value
      :param list1:key
      :param list2:value
      :return:dict
      ‘‘‘
      dic = dict(map(lambda x, y: [x, y], list1, list2))
      return dic
  
  
  def merge_cell(sheet_info):
      ‘‘‘
      #handle Merge transverse cells and handle Merge Vertical Cells, assign empty cells,
      :param rlow:row, include row exclusive of row_range
      :param rhigh:row_range
      :param clow:col, include col exclusive of col_range
      :param chigh:col_range
      :param sheet_info:object of sheet
      :return:dic contain all of empty cells value
      ‘‘‘
      merge = {}
      merge_cells = sheet_info.merged_cells
      for (rlow, rhigh, clow, chigh) in merge_cells:
          value_mg_cell = sheet_info.cell_value(rlow, clow)
          if rhigh - rlow == 1:
              # Merge transverse cells
              for n in range(chigh - clow - 1):
                  merge[(rlow, clow + n + 1)] = value_mg_cell
          elif chigh - clow == 1:
              # Merge Vertical Cells
              for n in range(rhigh - rlow - 1):
                  merge[(rlow + n + 1, clow)] = value_mg_cell
      return merge
  
  
  if __name__ == ‘__main__‘:
      get_excel()
  
  

写excel

• 生成xlsx，使用pandas

  import pandas as pd
  def write_excel(data, filename, keys=‘‘):
      data_df = pd.DataFrame(data)
      if keys != ‘‘:
          data_df.columns = list(keys)
      writer = pd.ExcelWriter(‘{}.xlsx‘.format(filename))
      data_df.to_excel(writer, index=None)
      writer.save()
  

• 生成csv

  import csv
  def write_csv(name, data_list):
      fieldnames = data_list[0]
      print(fieldnames)
      with open(name + ‘.csv‘, mode=‘w‘, newline=‘‘, encoding=‘utf-8-sig‘) as csv_file:
          writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
          writer.writeheader()
          for data in data_list:
              writer.writerow(data)
  

  当然，可以通过json去生成csv

  import json
  def json_to_csv(name):
      with open(name) as f:
          a = json.loads(f.read())
          write_csv(name, a)
  

  这里依旧可以使用pandas

  def data_to_csv(data_list, csv_key, name):
      """
      save data to csv
      :param data_list: data
      :param csv_key: csv column
      :param name: file name
      :return:
      """
      final_data = []
      for data in data_list:
          sign_key = []
          for key, value in data.items():
              sign_key.append(value)
          final_data.append(sign_key)
      # 将总数据转化为data frame再输出
      df = pd.DataFrame(data=final_data,
                        columns=csv_key)
      df.to_csv(name + ‘.csv‘, index=False, encoding=‘utf-8_sig‘)
  
  

python操作excel

原文：https://www.cnblogs.com/ZSMblog/p/13154984.html