611 lines
25 KiB
Python
611 lines
25 KiB
Python
# 1. 打开监测任务表格
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import pandas as pd
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import numpy as np
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import os, glob, re
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import matplotlib.pyplot as plt
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from matplotlib.ticker import FuncFormatter
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import datetime
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from docxtpl import DocxTemplate
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from docxtpl import InlineImage
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from docx.shared import Mm
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def toDate(strDT):
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dt = pd.to_datetime(strDT, errors='coerce')
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dts = ''
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# print('-+-+:', type(dt), dt)
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if not pd.isna(dt):
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dts = dt.strftime('%m-%d')
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return dts
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# word模板替换
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def temp_word(tmep_path, word_apth, dContext, pathImage, city):
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tpl = DocxTemplate(tmep_path)
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dC = {'annulusMediaCount': InlineImage(tpl, os.path.join(pathImage, city + 'annulusMediaCount.png'), width=Mm(120)),
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'annulusCountyCount': InlineImage(tpl, os.path.join(pathImage, city + 'annulusCountyCount.png'),
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width=Mm(120)),
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'annulusCountyArticle': InlineImage(tpl, os.path.join(pathImage, city + 'annulusCountyArticle.png'),
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width=Mm(120)),
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'annulusResult': InlineImage(tpl, os.path.join(pathImage, city + 'annulusResult.png'), width=Mm(120)),
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'barCountyRatio': InlineImage(tpl, os.path.join(pathImage, city + 'barCountyRatio.png'), width=Mm(120))
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}
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dContext.update(dC)
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tpl.render(dContext)
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tpl.save(word_apth)
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# 画柱状图
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def drawBar(data, recipe, title='', fn=''):
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plt.figure(figsize=(6, 4))
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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counties = recipe
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countyRates = data
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plt.bar(counties, countyRates, width=0.5)
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plt.xticks(counties, counties, rotation=35)
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plt.ylim((0, 1))
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def to_percent(temp, position):
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return '%2.0f' % (100 * temp) + '%'
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plt.gca().yaxis.set_major_formatter(FuncFormatter(to_percent))
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plt.title(title, fontsize=16)
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plt.tight_layout()
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plt.savefig(fn)
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# plt.show()
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plt.cla()
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plt.clf()
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plt.close()
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# 画环状图
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def drawAnnulus(data, recipe, title='', fn=''):
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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xxx = 8 # 画布x，长
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yyy = 4 # 画布y，高
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nnncol = 1 # 图例列数
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fs = 'medium' ## xx--small;x-small;small;medium;large;x-large;xx-large
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# if title == '政务新媒体账号类型':
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if len(recipe) > 20:
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if len(recipe) > 40:
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xxx = 16
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nnncol = 4
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fs = 'x-small'
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else:
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xxx = 16
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nnncol = 2
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fs = 'x-small'
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fig, ax = plt.subplots(figsize=(xxx, yyy), subplot_kw=dict(aspect="equal"))
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"""
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设置圆环宽度，绘图方向，起始角度
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参数wedgeprops以字典形式传递，设置饼图边界的相关属性，例如圆环宽度0.5
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饼状图默认从x轴正向沿逆时针绘图，参数startangle可指定新的角（例如负40度）度起画
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"""
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wedges, texts = ax.pie(data, radius=1.1, wedgeprops=dict(width=0.4), startangle=0) # 画环,返回扇形列表和每个标注文本对象(坐标，文字，属性)
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if 1:
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x = 1.2
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if title == '政务新媒体监测结果':
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x = 1.0
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plt.legend(labels=recipe, loc="center left", bbox_to_anchor=(x, 0.5), borderaxespad=0., ncol=nnncol,
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fontsize=fs) # , ncol=3
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if len(title) > 0:
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ax.set_title(title, fontsize=16, fontweight='heavy') # , x=0.6
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plt.tight_layout()
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if len(fn) > 0:
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plt.savefig(fn)
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# plt.show()
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plt.cla()
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plt.clf()
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plt.close()
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# summaryCity(city, dfc, dfcw, dfcs, context, strfnTemplate, os.path.join(strPathVerified,'Reports', city+'.docx'), strPathVerified )
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# 汇总市州数据，
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# 市州名称， 监测数据， cbz数据， mgc数据， context(编号、名称)， word模板文件名称， 输出word文件名称， 临时文件目录
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# 需要传入模板文件，数据、错别字、敏感词，单位名称等
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def summaryCity(info, city, df, dfW, dfS, fnTemplate, fnReport, dirTemp):
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dCityClient = {
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'甘肃省': "甘肃省人民政府办公厅",
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'省直部门': "甘肃省人民政府办公厅",
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'白银市': "白银市人民政府办公室",
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'定西市': "定西市人民政府办公室",
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'临夏回族自治州': "临夏回族自治州人民政府办公室",
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'平凉市': "中共平凉市委网络安全和信息化委员会办公室",
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"庆阳市": "庆阳市电子政务与信息资源管理办公室",
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'庆阳市华池县': "华池县人民政府办公室",
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'庆阳市宁县': "宁县人民政府办公室",
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"庆阳市镇原县": "镇原县人民政府办公室",
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"酒泉市": "酒泉市人民政府办公室",
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"天水市": "天水市人民政府办公室",
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"武威市": "武威市人民政府办公室",
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"金昌市": "金昌市人民政府办公室",
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"嘉峪关市": "嘉峪关市人民政府办公室",
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"兰州新区": "兰州新区管委会办公室",
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"陇南市": "陇南市人民政府办公室",
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"张掖市": "张掖市政务服务中心",
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"甘南藏族自治州": "甘南藏族自治州政务服务中心",
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"兰州市": "兰州市政务服务中心",
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"陇南市": "陇南市政务服务中心",
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}
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print("----------------" + city + "----------------")
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# 报告编号、委托单位
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strID = "%02d" % (list(dCityClient).index(city))
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# print(strID)
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context = {
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"city": city,
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"client": dCityClient[city],
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"reportid": strID + info['num'],
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}
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context.update(info)
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subordinate = '区县/地方部门'
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subordinateName = '县区'
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# 区县数据筛选
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if "庆阳市" in city:
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if "华池县" in city:
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dfc = df.loc[(df['市/省局'] == '庆阳市')
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& (df['区县/地方部门'] == '华池县')].copy()
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elif "宁县" in city:
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dfc = df.loc[(df['市/省局'] == '庆阳市')
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& (df['区县/地方部门'] == '宁县')].copy()
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elif "镇原县" in city:
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dfc = df.loc[(df['市/省局'] == '庆阳市')
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& (df['区县/地方部门'] == '镇原县')].copy()
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else:
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dfc = df.loc[(df['市/省局'] == '庆阳市')].copy()
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# & (df['区县/地方部门']!='华池县')
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# & (df['区县/地方部门']!='宁县')
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# & (df['区县/地方部门']!='镇原县') ].copy()
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dfcw = dfW.loc[dfW['市州'] == '庆阳市'].copy()
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dfcs = dfS.loc[dfS['市州'] == '庆阳市'].copy()
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elif "甘肃" in city :
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dfc = df.copy()
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dfcw = dfW.copy()
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dfcs = dfS.copy()
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subordinate = '市/省局'
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subordinateName = '市州'
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elif "省直部门" in city :
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dfc = df.loc[df['市/省局'] == city].copy()
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dfcw = dfW.loc[dfW['市州'] == dictSC[city]].copy()
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dfcs = dfS.loc[dfS['市州'] == dictSC[city]].copy()
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else:
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dfc = df.loc[(df['市/省局'] == city)].copy()
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dfcw = dfW.loc[dfW['市州'] == city].copy()
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dfcs = dfS.loc[dfS['市州'] == city].copy()
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# -----------------------
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# 统计结果分析
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dCity = {'1': '2'}
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#
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# 县区-监测结果 统计
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#
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# 透视表， 按县区统计各个监测结果账号数量
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dfCountyAccount = pd.pivot_table(dfc, index=[subordinate], columns=['监测结果'], values=['账号名称'], aggfunc='count',
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fill_value='', margins=True)
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dfCountyAccount.columns = dfCountyAccount.columns.droplevel(0)
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# 准备模板中的表格
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tt3_list = []
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for index, row in dfCountyAccount.iterrows():
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county = ''
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if index == 'All':
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county = '总 计'
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else:
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county = index
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hg = ''
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u2w = ''
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un = ''
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count = ''
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if '合格' in dfCountyAccount.columns.values.tolist():
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if not isinstance(row['合格'], str):
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hg = int(row['合格'])
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if '监测期间未更新' in dfCountyAccount.columns.values.tolist():
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if not isinstance(row['监测期间未更新'], str):
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un = int(row['监测期间未更新'])
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if '超过两周未更新' in dfCountyAccount.columns.values.tolist():
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if not isinstance(row['超过两周未更新'], str):
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u2w = int(row['超过两周未更新'])
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if 'All' in dfCountyAccount.columns.values.tolist():
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if not isinstance(row['All'], str):
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count = int(row['All'])
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tt3_a = {'county': county, 'hg': hg, 'u2w': u2w, 'un': un, 'count': count}
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tt3_list.append(tt3_a)
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context['tt3_contents'] = tt3_list
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# dfCountyAccount.to_excel(dirTask+strPathCity+'县区监测结果.xlsx')
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# -----------------------
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#
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# 按媒体类型统计
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#
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# 透视表， 按账号类型统计账号数量
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dfMedia = pd.pivot_table(dfc, index=['账号类型'], values=['账号名称'], aggfunc='count', fill_value='', margins=True)
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# 提取该市账号数量
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dCity['nmCount'] = dfMedia.loc['All', '账号名称']
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print(' 监测账号数：', dCity['nmCount'])
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# 提取 账号类型-数量 ， 拼成文本串
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dfMedia = dfMedia.sort_values(by='账号名称', ascending=False)
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lTableCs1 = []
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strMedia = ''
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i = 0
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tt1_list = []
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for m in dfMedia.index.tolist()[1:]: # 第一个是总数，不用取
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strNum = str(dfMedia.iloc[:, 0].tolist()[1:][i])
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strMedia = strMedia + m + strNum + '个，'
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tt1_a = {'type': m, 'count': strNum}
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tt1_list.append(tt1_a)
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i = i + 1
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dCity['sMediaCount'] = strMedia[:-1].rstrip('，')
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context.update({'tt1_contents': tt1_list})
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# -----------------------
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#
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# 按县区-更新次数 统计
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#
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dfCountyArticle = pd.pivot_table(dfc, index=[subordinate], values=['更新次数'], aggfunc='sum', fill_value='',
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margins=True)
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dfCountyArticle = dfCountyArticle.sort_values(by='更新次数', ascending=False).copy()
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dCity['cityArticleCount'] = "%d" % dfCountyArticle.iloc[0, 0]
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dCity['countyMostArticle'] = dfCountyArticle.index.tolist()[1]
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dCity['countyMostArticleCount'] = "%d" % dfCountyArticle.iloc[1, 0]
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strCountyArticle = ''
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iiii = 1
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for cccc in dfCountyArticle.index.tolist()[1:]:
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strCountyArticle = strCountyArticle + cccc + "%d" % dfCountyArticle.iloc[iiii, 0] + "次，"
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iiii = iiii + 1
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dCity['sCountyArticles'] = strCountyArticle.rstrip('，')
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# 市各县区监测结果按总数排序，
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dfCountyAccount.loc[:, '合格'] = dfCountyAccount['合格'].astype('int')
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dfCountyAccount = dfCountyAccount.sort_values(by='All', ascending=False).copy()
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# 计算合格率
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dfCountyAccount.eval('rate = 合格 / All ', inplace=True)
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dfResult = dfCountyAccount.copy()
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# 提取city合格率
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dCity['cityRatio'] = "{:.1%}".format(dfCountyAccount.loc['All', 'rate'])
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print(' 合格率：', dCity['cityRatio'])
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# 导出文件
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# dfCountyAccount.to_excel(dirIntermediate+sFileBase+'县区合格率.xlsx')
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# dfMedia = dfMedia.drop(['All'])
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# 提取县区名称，县区账号数, 县区合格率，转成字符串
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dfCountyAccount = dfCountyAccount.drop(['All']) # 删除"All"行
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counties = dfCountyAccount.index.tolist()
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countyCounts = dfCountyAccount['All'].values.tolist()
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countyHeges = dfCountyAccount['合格'].values.tolist()
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print(countyCounts)
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print(counties)
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# 按县区账号数量排序
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strCountyCount = ''
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strCounties = ''
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i = 0
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for c in counties:
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strCounties = strCounties + c + '，'
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strCountyCount = strCountyCount + c + str(countyCounts[i]) + '个，'
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i = i + 1
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dCity['countyCount'] = "%d" % i
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dCity['sCounties'] = strCounties.rstrip('，')
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dCity['sCountyCount'] = strCountyCount.rstrip('，')
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# 按合格率排序
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dfCountyAccount = dfCountyAccount.sort_values(by='rate', ascending=False)
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countieshege = dfCountyAccount.index.tolist()
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countyRates = dfCountyAccount['rate']
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strCountyRatio = ''
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i = 0
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tt2_list = []
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for c in countieshege:
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strRatio = "%.1f" % (100.0 * countyRates[i])
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strCountyRatio = strCountyRatio + c + strRatio + '%，'
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tt2_a = {'county': c, 'ratio': strRatio + '%'}
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tt2_list.append(tt2_a)
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i = i + 1
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dCity['sCountyRatio'] = strCountyRatio.rstrip('，')
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dCity['tt2_contents'] = tt2_list
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# -----------------------
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#
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# 绘图
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#
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print(' 生成图片...')
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drawAnnulus(dfMedia.iloc[:, 0].tolist()[1:], dfMedia.index.tolist()[1:],
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'政务新媒体账号类型', os.path.join(dirTemp, city + 'annulusMediaCount.png'))
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print(countyCounts)
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print(counties)
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drawAnnulus(countyCounts, counties,
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subordinateName + '政务新媒体账号数量', os.path.join(dirTemp, city + 'annulusCountyCount.png'))
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drawAnnulus(dfCountyArticle.iloc[:, 0].tolist()[1:], dfCountyArticle.index.tolist()[1:],
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subordinateName + '政务新媒体累计更新次数', os.path.join(dirTemp, city + 'annulusCountyArticle.png'))
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# ；{{resultNoUpdated}}个政务新媒体监测期间未更新，占监测总数的{{resultNoUpdatedRatio}}
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# ；{{resultNoUpdated2W}}个政务新媒体连续未更新时间超过两周，占监测总数的{{resultNoUpdated2WRatio}}
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# 政务新媒体监测结果
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dfResult = dfResult.drop('All', axis=1)
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dfResult = dfResult.drop('rate', axis=1)
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# 合格数，合格率，不合格数
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dCity['resultQualified'] = "%d" % (dfResult.loc['All', '合格'])
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dCity['resultQualifiedRatio'] = "%.1f%%" % (dfResult.loc['All', '合格'] / dCity['nmCount'] * 100.0)
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dCity['resultUnqualified'] = "%d" % (dCity['nmCount'] - dfResult.loc['All', '合格'])
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#
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# numNoupdated = 0
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if '监测期间未更新' in dfResult.columns.values.tolist():
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numNoupdated = dfResult.loc['All', '监测期间未更新']
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dCity['stringResultNoUpdated'] = "；%d个政务新媒体监测期间未更新，占监测总数的%.1f%%" % (
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numNoupdated, numNoupdated / dCity['nmCount'] * 100.0)
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dCity['stringNoUpdated'] = "%d个政务新媒体监测期间未更新。" % (numNoupdated)
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else:
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dCity['stringResultNoUpdated'] = ''
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dCity['stringNoUpdated'] = ""
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# dCity['resultNoUpdated'] = "%d"%(numNoupdated)
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# dCity['resultNoUpdatedRatio'] = "%.1f%%"%(numNoupdated/dCity['nmCount']*100.0)
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# numNoupdated2W = 0
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if '超过两周未更新' in dfResult.columns.values.tolist():
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numNoupdated2W = dfResult.loc['All', '超过两周未更新']
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dCity['stringResultNoUpdated2W'] = "；%d个政务新媒体连续未更新时间超过两周，占监测总数的%.1f%%" % (
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numNoupdated2W, numNoupdated2W / dCity['nmCount'] * 100.0)
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dCity['stringNoUpdated2W'] = "%d个政务新媒体连续未更新时间超过两周。" % (numNoupdated2W)
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else:
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dCity['stringResultNoUpdated2W'] = ''
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dCity['stringNoUpdated2W'] = ''
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# dCity['resultNoUpdated2W'] = "%d"%(numNoupdated2W)
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# dCity['resultNoUpdated2WRatio'] = "%.1f%%"%(numNoupdated2W/dCity['nmCount']*100.0)
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resultLabels = dfResult.columns.values.tolist()
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resultCounts = dfResult.loc['All'].values.tolist()
|
||
drawAnnulus(resultCounts, resultLabels,
|
||
'政务新媒体监测结果', os.path.join(dirTemp, city + 'annulusResult.png'))
|
||
|
||
drawBar(countyRates, countieshege,
|
||
'政务新媒体管理矩阵发布时效性合格率榜单', os.path.join(dirTemp, city + 'barCountyRatio.png'))
|
||
|
||
# -----------------------
|
||
#
|
||
# 准备报告需要的数据
|
||
#
|
||
print(' 生成报告...')
|
||
|
||
dfCityUnqulified = dfc[dfc['监测结果'] != '合格']
|
||
dfCityUnqulified = dfCityUnqulified.sort_values(by="监测结果", ascending=True) # by指定按哪列排序。ascending表示是否升序=False
|
||
|
||
#################################################
|
||
|
||
dfCityQulified = dfc[dfc['监测结果'] == '合格']
|
||
dfCityQulified = dfCityQulified.sort_values(by=subordinate, ascending=True) # by指定按哪列排序。ascending表示是否升序=False
|
||
|
||
# 方法一
|
||
|
||
tt4_list = []
|
||
for index, row in dfCityUnqulified.iterrows():
|
||
count = ''
|
||
if row['更新次数']:
|
||
count = "%d" % row['更新次数']
|
||
days = ''
|
||
if row['最大静默日数']:
|
||
days = "%d" % row['最大静默日数']
|
||
sD1 = ''
|
||
sD2 = ''
|
||
if row['静默开始日期']:
|
||
sD1 = toDate(str(row['静默开始日期']))
|
||
if row['静默结束日期']:
|
||
sD2 = toDate(str(row['静默结束日期']))
|
||
|
||
tt4_a = {'name': row['账号名称'], 'type': row['账号类型'], 'unit': row['开设主体'],
|
||
'county': row[subordinate], 'result': row['监测结果'], 'num': count,
|
||
'days': days, 'start': sD1, 'end': sD2, }
|
||
tt4_list.append(tt4_a)
|
||
tt4_results = {'tt4_contents': tt4_list}
|
||
context.update(tt4_results)
|
||
|
||
tt5_list = []
|
||
for index, row in dfCityQulified.iterrows():
|
||
count = ''
|
||
if row['更新次数']:
|
||
count = "%d" % row['更新次数']
|
||
days = ''
|
||
if row['最大静默日数']:
|
||
days = "%d" % row['最大静默日数']
|
||
sD1 = ''
|
||
sD2 = ''
|
||
if row['静默开始日期']:
|
||
sD1 = toDate(str(row['静默开始日期']))
|
||
if row['静默结束日期']:
|
||
sD2 = toDate(str(row['静默结束日期']))
|
||
|
||
tt5_a = {'name': row['账号名称'], 'type': row['账号类型'], 'unit': row['开设主体'],
|
||
'county': row[subordinate], 'result': row['监测结果'], 'num': count,
|
||
'days': days, 'start': sD1, 'end': sD2, }
|
||
tt5_list.append(tt5_a)
|
||
tt5_results = {'tt5_contents': tt5_list}
|
||
context.update(tt5_results)
|
||
|
||
# 读取添加错别字表格
|
||
tCbz_list = []
|
||
dfcw.fillna('')
|
||
for index, row in dfcw.iterrows():
|
||
sTitle = ''
|
||
sDate = toDate(str(row['发文时间']))
|
||
if '标题' in dfcw.columns:
|
||
sTitle = row['标题']
|
||
|
||
# 去除引号等干扰表格模板输出的字符
|
||
r = "[——,$%^，。？、~@#￥%……&*《》<>「」{}【】()/\\\[\]'\"]"
|
||
if pd.isna(row['错误出现位置']):
|
||
s = ''
|
||
else:
|
||
s = re.sub(r, '', row['错误出现位置'])
|
||
a = {'error': row['错误'], 'tips': row['建议'], 'sentence': s, 'type': row['账号类型'], 'name': row['账号名称'],
|
||
'date': sDate, 'title': sTitle, }
|
||
tCbz_list.append(a)
|
||
if dfcw.shape[0] > 0:
|
||
dCity['stringCbzCount'] = '本次检测发现错别字%d处，详细情况见附表政务新媒体发布内容错别字统计表。' % (dfcw.shape[0])
|
||
else:
|
||
dCity['stringCbzCount'] = '本次检测未发现错别字。'
|
||
tCbz_results = {'tCbz_contents': tCbz_list}
|
||
context.update(tCbz_results)
|
||
|
||
# 读取添加敏感词表格
|
||
tMgc_list = []
|
||
dfcs.fillna('')
|
||
for index, row in dfcs.iterrows():
|
||
sTitle = ''
|
||
sDate = toDate(str(row['发文时间']))
|
||
if '标题' in dfcs.columns:
|
||
sTitle = row['标题']
|
||
a = {'error': row['错误'], 'tips': row['建议'], 'sentence': row['错误出现位置'], 'type': row['账号类型'], 'name': row['账号名称'],
|
||
'date': sDate, 'title': sTitle, }
|
||
tMgc_list.append(a)
|
||
if dfcs.shape[0] > 0:
|
||
dCity['stringMgcCount'] = '本次检测发现敏感信息%d处，详细情况见附表政务新媒体发布内容敏感信息统计表。' % (dfcs.shape[0])
|
||
else:
|
||
dCity['stringMgcCount'] = '本次检测未发现涉敏内容。'
|
||
tMgc_results = {'tMgc_contents': tMgc_list}
|
||
context.update(tMgc_results)
|
||
|
||
# table1
|
||
context.update(dCity)
|
||
|
||
# -----------------------
|
||
#
|
||
# 按模板生成报告
|
||
#
|
||
temp_word(fnTemplate,
|
||
fnReport,
|
||
context, dirTemp, city)
|
||
|
||
def createDir(dirP, dirS):
|
||
dirN = dirP
|
||
if os.path.isdir(dirP):
|
||
dirN = os.path.join(dirP, dirS)
|
||
if not (os.path.exists(dirN)):
|
||
os.mkdir(dirN)
|
||
if os.path.isdir(dirN):
|
||
pass
|
||
else:
|
||
dirN = dirP
|
||
print('Directory ' + dirN + ' cannot be created.')
|
||
return dirN
|
||
# def createDir(dirP, dirS):
|
||
|
||
def summary(info, strFnData, strFnW, strFnS, strfnTemplate, strPathOutput):
|
||
# 打开监测数据、错别字、敏感词
|
||
df = pd.read_excel(strFnData)
|
||
dfW = pd.read_excel(strFnW)
|
||
dfS = pd.read_excel(strFnS)
|
||
|
||
# df.loc[df['账号类型'] == '微信服务号', '账号类型'] = '微信'
|
||
# df.loc[df['账号类型'] == '微信订阅号', '账号类型'] = '微信'
|
||
|
||
# 统一监测结果表述
|
||
df.loc[df['监测结果'] == '连续两周未更新', '监测结果'] = '超过两周未更新'
|
||
|
||
# 过长名称替换为简称，便于绘图
|
||
df.loc[df['区县/地方部门'] == '积石山保安族东乡族撒拉族自治县', '区县/地方部门'] = '积石山县'
|
||
df.loc[df['区县/地方部门'] == '阿克塞哈萨克族自治县', '区县/地方部门'] = '阿克塞自治县'
|
||
|
||
# 省直、 市直、 州直
|
||
df['市/省局'] = df['市/省局'].fillna('省直部门')
|
||
df['区县/地方部门'] = df['区县/地方部门'].fillna('市直部门')
|
||
df.loc[(df['市/省局'] == '临夏回族自治州') & (df['区县/地方部门'] == '市直部门'), '区县/地方部门'] = '州直部门'
|
||
|
||
|
||
# 数据整理
|
||
df.replace(r'\s+', '', regex=True, inplace=True) # 去除账号、单位名称中的空格、换行、tab等
|
||
df.replace(r'^其他\+', '', regex=True, inplace=True) # 去除账号类型中的 "其它" 字样
|
||
df['更新次数'] = df['更新次数'].fillna(0)
|
||
df = df.fillna(value='')
|
||
|
||
|
||
#########################################################
|
||
#
|
||
# 统计市州范围
|
||
cities = {'甘肃省', '白银市', '武威市', '庆阳市', '酒泉市', '天水市', '临夏回族自治州', '平凉市', '定西市', '兰州新区',
|
||
'嘉峪关市', '庆阳市华池县', '庆阳市镇原县', '庆阳市宁县'}
|
||
#cities = cities | {'甘南藏族自治州', '金昌市', '兰州市', '陇南市', '张掖市', '甘肃省', '省直部门'}
|
||
#
|
||
cities = {'甘肃省','庆阳市','武威市','临夏回族自治州', '酒泉市'} # 只统计特定市州
|
||
|
||
cities = {'张掖市'}
|
||
|
||
# strPathOutput目录下生成报告目录和临时文件目录：Reports 和 Intermediate
|
||
dirP = os.path.abspath(os.path.dirname(strPathOutput))
|
||
dirReports = createDir(dirP, 'Reports')
|
||
dirIntermediate = createDir(dirP, 'Intermediate')
|
||
for city in cities:
|
||
summaryCity(info, city, df, dfW, dfS, strfnTemplate, os.path.join(dirReports, city + '.docx'), dirIntermediate)
|
||
|
||
# 合并错别字文件
|
||
def mergeCMC(keyword, strPathCBZ, strFnCbz):
|
||
# cityShorten
|
||
cityShorten = {'白银': '白银市', '定西': '定西市', '酒泉': '酒泉市', '嘉峪关': '嘉峪关市', '陇南': '陇南市',
|
||
'临夏': '临夏回族自治州', '平凉': '平凉市', '庆阳': '庆阳市', '天水': '天水市', '武威': '武威市', '新区': '兰州新区',
|
||
'兰州新区': '兰州新区', '兰州': '兰州市', '张掖': '张掖市', '甘南': '甘南藏族自治州', '省直': '省直部门', '金昌': '金昌市',
|
||
'BY': '白银市', 'DX': '定西市', 'JQ': '酒泉市', 'JYG': '嘉峪关市', 'LN': '陇南市',
|
||
'LX': '临夏回族自治州', 'PL': '平凉市', 'QY': '庆阳市', 'TS': '天水市', 'WW': '武威市', 'XQ': '兰州新区',
|
||
'LZXQ': '兰州新区', 'LZ': '兰州市', 'ZY': '张掖市', 'GN': '甘南藏族自治州', 'SZ': '省直部门', 'JC': '金昌市', }
|
||
df = pd.DataFrame()
|
||
for fn in glob.glob(os.path.join(strPathCBZ, '*'+keyword+'*.xlsx')):
|
||
p, f = os.path.split(fn)
|
||
city=''
|
||
for c in cityShorten.keys():
|
||
if c in f:
|
||
city = cityShorten[c]
|
||
break
|
||
if len(city)<1:
|
||
print("!!!!! City Name not matched ( ", f, " )")
|
||
dfn = pd.read_excel(fn)
|
||
dfn['市州'] = city
|
||
df = df.append(dfn, ignore_index=True)
|
||
print(city, f, dfn.shape[0], '/', df.shape[0])
|
||
df.to_excel(strFnCbz)
|
||
#def mergeCMC
|
||
|
||
if __name__ == "__main__":
|
||
|
||
# 运行之前先转换excel文件的日期列
|
||
|
||
info = {
|
||
"year": "2023",
|
||
"quarter": "一",
|
||
"dateCN": "二〇二三年四月",
|
||
"dateStart": "2023年1月1日",
|
||
"dateEnd": "2023年3月20日",
|
||
"days": "79",
|
||
"num": "4",
|
||
}
|
||
# 数据根目录，
|
||
strPath = 'D:/Projects/POM/DATA/2023年S1/'
|
||
# 监测数据
|
||
strFnMonitoring = strPath + '汇总/第一季度汇总数据_2023.3.xlsx'
|
||
# word模板文件
|
||
strPathTemplate = strPath + 'POM_ReportTemplate0.docx'
|
||
# 错别字
|
||
strFnCbz = strPath + '汇总/CBZ.xlsx'
|
||
if not os.path.exists(strFnCbz):# 汇总错别字
|
||
strPathCBZ = strPath + '监测/'
|
||
mergeCMC("错别", strPathCBZ, strFnCbz)
|
||
# 敏感词
|
||
strFnMgc = strPath + '汇总/MGC.xlsx'
|
||
if not os.path.exists(strFnMgc):#汇总敏感词
|
||
strPathMGC = strPath + '监测/'
|
||
mergeCMC("敏感", strPathMGC, strFnMgc)
|
||
# 数据目录
|
||
strPathOutput = strPath + '统计/'
|
||
|
||
summary(info, strFnMonitoring, strFnCbz, strFnMgc, strPathTemplate, strPathOutput)
|