本文旨在为不熟悉python的开发人员提供excel便捷处理工具，以方便日常工作。

一、python安装

进入https://www.python.org/downloads/，根据操作系统版本选择对应安装包。建议安装3.x版本。

安装过程注意勾选 Add to path，将python路径信息添加到系统环境变量PATH中。

IDE工具建议安装jetbrains提供的pycharm社区版。

二、Excel介绍

电子表格工具，后缀为 .xlsx。

一个Excel文档为一个工作簿，工作簿可以包含多个sheet，一个sheet为一个工作表。

三、安装openpyxl模块

在DOS命令窗口执行： pip install openpyxl==2.6.2

四、Excel处理

本模块代码及实验文件均已上传Github。

仓库地址： github.com/WWindmill/p…

代码目录：

4.1 读取操作

import openpyxl
from openpyxl.utils import get_column_letter, column_index_from_string

# 工作簿对象
workbook = openpyxl.load_workbook(".\source\automate_online-materials\example.xlsx")
print('type of result: ', type(workbook))
print("all sheet names: ", workbook.sheetnames)
# 获取工作表
sheet = workbook['Sheet1']
print("sheet obj: ", sheet, " sheet title:",  sheet.title)
# 获取工作簿的活动表
anotherSheet = workbook.active
print("active sheet: ", anotherSheet)
# 获取单元格
print("cell A1: ", sheet['A1'])
print("cell A1 val: ", sheet['A1'].value)
c = sheet['B1']
print('Row %s, Column %s is %s' % (c.row, c.column, c.value))
print('Cell %s is %s' % (c.coordinate, c.value))
print("cell[B1]: ", sheet.cell(row=1, column=2))
# 步长为2
for i in range(1, 8, 2):
    print('row:%s,column:2, value:%s' % (i, sheet.cell(row=i, column=2).value))
# 获取工作表大小
print("max row: ", sheet.max_row)
print("max column: ", sheet.max_column)
# 列字母与数字转换
print("1 mean letter: ", get_column_letter(1))
print(sheet.max_column, "mean letter: ", get_column_letter(sheet.max_column))
print("column A point at num: ", column_index_from_string('A'))
# 按行遍历 method1
print(tuple(sheet['A1':'C3']))
for rowCell in sheet['A1':'C3']:
    for eachCell in rowCell:
        print(eachCell.coordinate, eachCell.value)
    print('--- END OF ROW ---')
# 按行遍历 method2
print(list(sheet.rows)[0])
for cellObj in list(sheet.rows)[0]:
 print(cellObj.value)
print('--- END OF ROW ---')
# 按列遍历
print(list(sheet.columns)[0])
for cellObj in list(sheet.columns)[0]:
 print(cellObj.value)
print('--- END OF column ---')

4.2 写操作

import openpyxl

# 工作簿对象
workbook = openpyxl.load_workbook(".\source\example.xlsx")
print('type of result: ', type(workbook))
print("all sheet names: ", workbook.sheetnames)
# 获取工作表
sheet = workbook['Sheet1']
print("sheet obj: ", sheet, " sheet title:",  sheet.title)
# 修改sheet名称 并转储为另一个文件
sheet.title = 'Spam Spam Spam'
workbook.save('.\source\example_copy.xlsx')
# 创建和删除工作表
workbook.create_sheet(index=3, title="the fourth sheet")
print('sheet names: ', workbook.sheetnames)
del workbook['the fourth sheet']
print('sheet names: ', workbook.sheetnames)
workbook.save('.\source\example_copy.xlsx')
# 修改单元格属性值
sheet['B1'] = 'Hello, world!'
print('B1 modified value: ', sheet['B1'].value)
workbook.save('.\source\example_copy.xlsx')

4.3 其他操作

import openpyxl

# 公式
workbookCal = openpyxl.Workbook()
sheet = workbookCal.active
sheet['A1'] = 200
sheet['A2'] = 300
# 设置公式.
sheet['A3'] = '=SUM(A1:A2)'
workbookCal.save('.\source\writeFormula.xlsx')

# 行、列操作
workbookOpt = openpyxl.Workbook()
sheetOpt = workbookOpt.active
sheetOpt['A1'] = 'Tall row'
sheetOpt['B2'] = 'Wide column'
# 设置宽高
sheetOpt.row_dimensions[1].height = 70
sheetOpt.column_dimensions['B'].width = 20
# 合并单元格
sheetOpt.merge_cells('A1:D3')
sheetOpt['A1'] = 'Twelve cells merged together.'
sheetOpt.merge_cells('C5:D5')
sheetOpt['C5'] = 'Two merged cells.'
workbookOpt.save('.\source\dimensions.xlsx')
# 分拆单元格
sheetOpt.unmerge_cells('C5:D5')
workbookOpt.save('.\source\dimensions.xlsx')
# 冻结窗口
sheetOpt.freeze_panes = 'C5'
workbookOpt.save('.\source\dimensions.xlsx')

#图表
workbookDraw = openpyxl.Workbook()
sheetDraw = workbookDraw.active
for i in range(1, 11):
    sheetDraw['A' + str(i)] = i
refObj = openpyxl.chart.Reference(sheet, min_col=1, min_row=1,max_col=1, max_row=10)
seriesObj = openpyxl.chart.Series(refObj, title='First series')
chartObj = openpyxl.chart.BarChart()
chartObj.title = 'My Chart'
chartObj.append(seriesObj)
sheetDraw.add_chart(chartObj, 'C5')
workbookDraw.save('.\source\sampleChart.xlsx')

五、综合实践

根据如下表结构统计各县人口总数以及普查区数，并输出为Json文件。

其中：

• CensusTract表示普查区编号
• State表示州简称
• County表示县名称
• POP表示普查区人口数

文件下载地址：censuspopdata.xlsx

实现代码如下：

import openpyxl, pprint

print('Opening workbook...')
workbook = openpyxl.load_workbook('.\source\censuspopdata.xlsx')
sheet = workbook['Population by Census Tract']
countyData = {}
print('Reading rows...')
for row in range(2, sheet.max_row + 1):
    # Each row in the spreadsheet has data for one census tract.
    state = sheet['B' + str(row)].value
    county = sheet['C' + str(row)].value
    pop = sheet['D' + str(row)].value
    # Make sure the key for this state exists.if already exist, execute nothing.
    countyData.setdefault(state, {})
    # Make sure the key for this county in this state exists.if already exist, execute nothing.
    countyData[state].setdefault(county, {'tracts': 0, 'pop': 0})
    # Each row represents one census tract, so increment by one.
    countyData[state][county]['tracts'] += 1
    # Increase the county pop by the pop in this census tract.
    countyData[state][county]['pop'] += int(pop)
# Open a new text file and write the contents of countyData to it.
print('Writing results...')
resultFile = open('.\source\census.json', 'w')
resultFile.write('allData = ' + pprint.pformat(countyData)) resultFile.close() print(pprint.pformat(countyData)) # print Anchorage population # print(countyData['AK']['Anchorage']['pop']) print('Done.') 

返回如下：

{
  "AK": {
    "Aleutians East": {
      "pop": 3141,
      "tracts": 1
    },
    "Aleutians West": {
      "pop": 5561,
      "tracts": 2
    },
    "Anchorage": {
      "pop": 291826,
      "tracts": 55
    },
    "Bethel": {
      "pop": 17013,
      "tracts": 3
    },
    "Bristol Bay": {
      "pop": 997,
      "tracts": 1
    },
    "Denali": {
      "pop": 1826,
      "tracts": 1
    },
    "Dillingham": {
      "pop": 4847,
      "tracts": 2
    },
    ...

六、参考资料

《Python编程快速上手 让繁琐工作自动化 第2版》