Python数据分析基础读书笔记 - 3.4 在 Excel 工

借用书中的原文来讲一下这个功能的用途:

有些情况下,你只需要处理工作簿中的一组工作表.例如,你的工作簿可能包含很多工作表,但是你只需要处理其中的 20 个.在这种情况下,可以使用工作簿的 sheet_by_index 或 sheet_by_name 函数来处理一组工作表.

在一组工作表中筛选特定行

此示例中,我们将从第一个和第二个工作表筛选出销售额大于 $1900.00 的那些行.

1.基础 Python

文件名称:11excel_value_meets_condition_set_of_worksheets.py

所需文件:sales_2013.xlsx

输出文件:11output.xls

import sys
from datetime import date 
from xlrd import open_workbook,xldate_as_tuple
from xlwt import Workbook
input_file = sys.argv[1]
output_file = sys.argv[2]
output_workbook = Workbook()
output_worksheet = output_workbook.add_sheet('set_of_worksheets')
my_sheets = [0,1] # 表示要处理的工作表的索引值
threshold = 1900.0
sales_column_index = 3
first_worksheet = True
with open_workbook(input_file) as workbook:
    data = []
    for sheet_index in range(workbook.nsheets):
        if sheet_index in my_sheets:
            worksheet = workbook.sheet_by_index(sheet_index)
            if first_worksheet:
                header_row = worksheet.row_values(0)
                data.append(header_row)
                first_worksheet = False
            for row_index in range(1,worksheet.nrows):
                row_list = []
                sales_amount = worksheet.cell_value(row_index, sales_column_index)
                if sales_amount > threshold:
                    for column_index in range(worksheet.ncols):
                        cell_value = worksheet.cell_value(row_index, column_index)
                        cell_type = worksheet.cell_type(row_index, column_index)
                        if cell_type == 3:
                            date_cell = xldate_as_tuple(cell_value, workbook.datemode)
                            date_cell = date(*date_cell[0:3]).strftime('%m/%d/%Y')
                            row_list.append(date_cell)
                        else:
                            row_list.append(cell_value)
                if row_list:
                    data.append(row_list)
    for list_index,output_list in enumerate(data):
        for element_index,element in enumerate(output_list):
            output_worksheet.write(list_index,element_index,element)
output_workbook.save(output_file)

如果您一直从第 2 章看到现在,应该可以发现规律了,又是打开表格,按行列进行读取,满足要求的取出,不满足要求的就进入到下一行列查找,这样的思路贯穿了这本书.接下来看看用 Pandas 怎么处理.

2.Pandas

文件名称:pandas_value_meets_condition_set_of_worksheets.py

所需文件:sales_2013.xlsx

输出文件:11pandas_output.xls

import pandas as pd
import sys
input_file = sys.argv[1]
output_file = sys.argv[2]
my_sheets = [0,1]
threhold = 1900.0   
data_frame = pd.read_excel(input_file,sheet_name=my_sheets,index_col=None)
row_list = []
for worksheet_name,data in data_frame.items():
    row_list.append(data[data['Sale Amount'].astype(float) > threhold])
filtered_rows = pd.concat(row_list,axis=0,ignore_index=True)
writer = pd.ExcelWriter(output_file)
filtered_rows.to_excel(writer,sheet_name='set_of_worksheets',index=False)
writer.save()

怎么样,是不是依然比用基础 Python 简单得多.