feat: 统计网站数据
This commit is contained in:
parent
3c8fc28113
commit
f80b196145
414
mycode/main.py
414
mycode/main.py
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@ -13,18 +13,21 @@ web_dir = os.path.join(BASE_DIR, 'web_dir')
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output_dir = os.path.join(BASE_DIR, 'summary')
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df_s = pd.read_excel(os.path.join(BASE_DIR, 'biao.xlsx'), sheet_name='筛查内容')
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def fix_url_scheme(url, default_scheme='http'):
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# 检查URL是否包含方案
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if not url.startswith('http://') and not url.startswith('https://'):
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# 如果没有方案，添加默认方案
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url = f'{default_scheme}://{url}'
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return url
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# 检查URL是否包含方案
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if not url.startswith('http://') and not url.startswith('https://'):
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# 如果没有方案，添加默认方案
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url = f'{default_scheme}://{url}'
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return url
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def trans_to_json():
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json_str = df_s.to_json(orient='records', force_ascii=False)
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with open('biao.json', 'w', encoding='utf-8') as f:
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f.write(json_str)
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def make_simple_csv_from_db(now: datetime):
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# 只查找当前月份更新的公众号数据
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now_month_str = now.strftime('%Y-%m-%d 00:00:00')
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@ -49,36 +52,38 @@ def make_simple_csv_from_db(now: datetime):
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# 将数据写入CSV文件
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df.to_csv(os.path.join(wechat_dir, 'articles.csv'), index=False)
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def float_to_int(value):
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try:
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return int(value)
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except:
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return value
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def get_cbma_info_from_db_and_ana(year: str = '2023'):
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# 全年统计数据
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zybiz = "MzIzMDU4Njg3MA=="
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df_fx = pd.DataFrame({"单位": [ "中国建材总院",
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"瑞泰科技", "国检集团", "中材高新", "哈玻院", "中国新材院", "秦皇岛院", "西安墙材院", "咸阳陶瓷院", "钟表所", "总院北分", "中岩科技", "水泥新材院", "中建材科创院", "科建苑", "办公室（董事会办公室）", "党委组织部/人力资源部", "财务部", "科技部", "投资部", "企业管理部、安全环保部", "党群部/宣传统战部",
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"党风办/巡察办、纪委综合室", "监督执纪室", "审计办公室"],
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"公众号Biz": [zybiz, "MzU0MzgwMzg1NA==", "MzI1MjYzNDQ3NA==", "MzA5MDkzNDA0NQ==", "Mzg2MDg0NjkwNw==", "MzI3MTY5NTExNA==", "MzI1MzY1Njg5MQ==", "MzIxOTQwNjE2MQ==",
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"Mzg3OTI0NTYzMA==", "MzA3NTU5NjM2MA==", "", "Mzg2NDgyMDM3OA==","","MzA5NTQ5MjY4Nw==", "", "", "", "", "", "", "", "", "", "", "", ],
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# "供总院稿数": [], "供总院专稿数": [], "供总院组稿数": [], "供总院阅读10000及以上数": [], "供总院阅读5000及以上数": [], "供总院阅读1000及以上数": [],
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# "1月发布数": [], "1月最高点击文章": [],
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# "2月发布数": [], "2月最高点击文章": [],
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# "3月发布数": [], "3月最高点击文章": [],
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# "4月发布数": [], "4月最高点击文章": [],
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# "5月发布数": [], "5月最高点击文章": [],
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# "6月发布数": [], "6月最高点击文章": [],
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# "7月发布数": [], "7月最高点击文章": [],
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# "8月发布数": [], "8月最高点击文章": [],
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# "9月发布数": [], "9月最高点击文章": [],
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# "10月发布数": [], "10月最高点击文章": [],
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# "11月发布数": [], "11月最高点击文章": [],
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# "12月发布数": [], "12月最高点击文章": [],
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# "半年发布数": [], "半年最高点击文章": [],
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# "全年发布数": [], "全年最高点击文章": []
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})
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df_fx = pd.DataFrame({"单位": ["中国建材总院",
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"瑞泰科技", "国检集团", "中材高新", "哈玻院", "中国新材院", "秦皇岛院", "西安墙材院", "咸阳陶瓷院", "钟表所", "总院北分", "中岩科技", "水泥新材院", "中建材科创院", "科建苑", "办公室（董事会办公室）", "党委组织部/人力资源部", "财务部", "科技部", "投资部", "企业管理部、安全环保部", "党群部/宣传统战部",
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"党风办/巡察办、纪委综合室", "监督执纪室", "审计办公室"],
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"公众号Biz": [zybiz, "MzU0MzgwMzg1NA==", "MzI1MjYzNDQ3NA==", "MzA5MDkzNDA0NQ==", "Mzg2MDg0NjkwNw==", "MzI3MTY5NTExNA==", "MzI1MzY1Njg5MQ==", "MzIxOTQwNjE2MQ==",
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"Mzg3OTI0NTYzMA==", "MzA3NTU5NjM2MA==", "", "Mzg2NDgyMDM3OA==", "", "MzA5NTQ5MjY4Nw==", "", "", "", "", "", "", "", "", "", "", "", ],
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# "供总院稿数": [], "供总院专稿数": [], "供总院组稿数": [], "供总院阅读10000及以上数": [], "供总院阅读5000及以上数": [], "供总院阅读1000及以上数": [],
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# "1月发布数": [], "1月最高点击文章": [],
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# "2月发布数": [], "2月最高点击文章": [],
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# "3月发布数": [], "3月最高点击文章": [],
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# "4月发布数": [], "4月最高点击文章": [],
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# "5月发布数": [], "5月最高点击文章": [],
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# "6月发布数": [], "6月最高点击文章": [],
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# "7月发布数": [], "7月最高点击文章": [],
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# "8月发布数": [], "8月最高点击文章": [],
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# "9月发布数": [], "9月最高点击文章": [],
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# "10月发布数": [], "10月最高点击文章": [],
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# "11月发布数": [], "11月最高点击文章": [],
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# "12月发布数": [], "12月最高点击文章": [],
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# "半年发布数": [], "半年最高点击文章": [],
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# "全年发布数": [], "全年最高点击文章": []
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})
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# 查询所有指定公众号的文章并按年/月排序
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conn = sqlite3.connect(os.path.join(BASE_DIR, 'db_folder/test.db'))
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query_gzhs = f'''
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@ -104,13 +109,53 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
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pub_year, pub_month, pub_day;
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'''
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df = pd.read_sql_query(query_gzhs, conn)
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conn.close
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conn.close()
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# 尝试连接官网库进行查询
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import psycopg2
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conn_web = None
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df_web = None
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try:
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conn_web = psycopg2.connect(
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"dbname={} user={} password={} host={} port={}".format('edn_cms', 'auditor', 'Lde78B3_cbma', '10.65.253.10', '54321'))
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cur_web = conn.cursor()
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query_web = f"""
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SELECT
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a_outer.id,
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TO_CHAR(a_outer.ctime, 'YYYY') AS pub_year,
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TO_CHAR(a_outer.ctime, 'MM') AS pub_month,
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TO_CHAR(a_outer.ctime, 'DD') AS pub_day,
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a_outer.title,
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a_outer.source,
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a_outer.hits,
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t.title as bankuai,
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a_outer.src
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FROM
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"a_article" a_outer
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left join (
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select id, title, father, path
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from a_article
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where father in (20110528, 19080024)
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) t on a_outer.father = t.id
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WHERE
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a_outer.TYPE = 3
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and a_outer.deleted is NULL
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and EXTRACT ( YEAR FROM a_outer.ctime ) = {year}
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and bankuai is not NULL
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ORDER BY
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a_outer.ctime;
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"""
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df_web = pd.read_sql_query(query_web, conn_web)
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cur_web.close()
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conn_web.close()
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except Exception as e:
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pass
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# 追加总院数据来源
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for ind, row in df.iterrows():
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if row['gbiz'] == zybiz:
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full_path = os.path.join(wechat_dir, row['nickname'], row['id'] + '.md')
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full_path = os.path.join(
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wechat_dir, row['nickname'], row['id'] + '.md')
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try:
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with open(full_path, encoding='utf-8') as f:
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content = f.read()
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@ -136,7 +181,7 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
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for ind, row in df_fx.iterrows():
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dw = row['单位']
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gbiz = row['公众号Biz']
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# 全年对总院供给统计
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# if '、' in dw: # 针对这种同一部门的
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# cons = (df['gbiz']==zybiz)
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@ -146,37 +191,63 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
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# df_fx.at[ind, '供总院全年稿数'] = ((cons_dw_1)&(cons)).sum()
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# else:
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# df_fx.at[ind, '供总院全年稿数'] = ((df['source'].str.contains(dw))&(df['gbiz']==zybiz)).sum()
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df_fx.at[ind, '供总院全年专稿数'] = ((df['source'] == dw)&(df['gbiz']==zybiz)).sum()
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df_fx.at[ind, '供总院全年组稿数'] = ((df['source'].str.contains(dw)&(df['source']!=dw))&(df['gbiz']==zybiz)).sum()
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df_fx.at[ind, '供总院全年阅读10000及以上数'] = ((df['read_num']>=10000)&(df['source'].str.contains(dw))&(df['gbiz']==zybiz)).sum()
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df_fx.at[ind, '供总院全年阅读5000及以上数'] = ((df['read_num']>=5000)&(df['read_num']<10000)&(df['source'].str.contains(dw))&(df['gbiz']==zybiz)).sum()
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df_fx.at[ind, '供总院全年阅读1000及以上数'] = ((df['read_num']>=1000)&(df['read_num']<5000)&(df['source'].str.contains(dw))&(df['gbiz']==zybiz)).sum()
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df_fx.at[ind, '供总院全年专稿数'] = (
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(df['source'] == dw) & (df['gbiz'] == zybiz)).sum()
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df_fx.at[ind, '供总院全年网站专稿数'] = (
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(df_web['source'] == dw)).sum()
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df_fx.at[ind, '供总院全年组稿数'] = ((df['source'].str.contains(
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dw) & (df['source'] != dw)) & (df['gbiz'] == zybiz)).sum()
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df_fx.at[ind, '供总院全年网站组稿数'] = ((df_web['source'].str.contains(
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dw) & (df_web['source'] != dw))).sum()
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df_fx.at[ind, '供总院全年阅读10000及以上数'] = ((df['read_num'] >= 10000) & (
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df['source'].str.contains(dw)) & (df['gbiz'] == zybiz)).sum()
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df_fx.at[ind, '供总院全年阅读5000及以上数'] = ((df['read_num'] >= 5000) & (
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df['read_num'] < 10000) & (df['source'].str.contains(dw)) & (df['gbiz'] == zybiz)).sum()
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df_fx.at[ind, '供总院全年阅读1000及以上数'] = ((df['read_num'] >= 1000) & (
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df['read_num'] < 5000) & (df['source'].str.contains(dw)) & (df['gbiz'] == zybiz)).sum()
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for i in ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月', '上半年', '下半年', '全年']:
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if '月' in i:
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i_str = i.replace('月', '').zfill(2)
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cons_y_m = (df['pub_month']==str(i_str))
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cons_y_m = (df['pub_month'] == str(i_str))
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cons_y_m_web = (df_web['pub_month'] == str(i_str))
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elif i == '上半年':
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cons_y_m = (df['pub_month'] =='01')|(df['pub_month'] =='02')|(df['pub_month'] =='03')|(df['pub_month'] =='04')|(df['pub_month'] =='05')|(df['pub_month'] =='06')
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cons_y_m = (df['pub_month'] == '01') | (df['pub_month'] == '02') | (df['pub_month'] == '03') | (
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df['pub_month'] == '04') | (df['pub_month'] == '05') | (df['pub_month'] == '06')
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cons_y_m_web = (df_web['pub_month'] == '01') | (df_web['pub_month'] == '02') | (df_web['pub_month'] == '03') | (
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df_web['pub_month'] == '04') | (df_web['pub_month'] == '05') | (df_web['pub_month'] == '06')
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elif i == '下半年':
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cons_y_m = (df['pub_month'] =='07')|(df['pub_month'] =='08')|(df['pub_month'] =='09')|(df['pub_month'] =='10')|(df['pub_month'] =='11')|(df['pub_month'] =='12')
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cons_y_m = (df['pub_month'] == '07') | (df['pub_month'] == '08') | (df['pub_month'] == '09') | (
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df['pub_month'] == '10') | (df['pub_month'] == '11') | (df['pub_month'] == '12')
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cons_y_m_web = (df_web['pub_month'] == '07') | (df_web['pub_month'] == '08') | (df_web['pub_month'] == '09') | (
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df_web['pub_month'] == '10') | (df_web['pub_month'] == '11') | (df_web['pub_month'] == '12')
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elif i == '全年':
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cons_y_m = pd.Series(True, index=df.index)
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cons_y_m_web = pd.Series(True, index=df_web.index)
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if '、' in dw: # 针对这种同一部门的
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cons_dw_1 = pd.Series(False, index=df.index)
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cons_dw_1_web = pd.Series(False, index=df_web.index)
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for item in dw.split('、'):
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cons_dw_1 = (df['source'].str.contains(item))|cons_dw_1
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df_fx.at[ind, f'供总院{i}稿数'] = ((cons_dw_1)&(cons_y_m)&(df['gbiz']==zybiz)).sum()
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cons_dw_1 = (df['source'].str.contains(item)) | cons_dw_1
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cons_dw_1_web = (df_web['source'].str.contains(item)) | cons_dw_1_web
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df_fx.at[ind, f'供总院{i}稿数'] = ((cons_dw_1) & (
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cons_y_m) & (df['gbiz'] == zybiz)).sum()
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df_fx.at[ind, f'供总院网站{i}稿数'] = ((cons_dw_1_web) & (
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cons_y_m_web)).sum()
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else:
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df_fx.at[ind, f'供总院{i}稿数'] = (df['source'].str.contains(dw)&(cons_y_m)&(df['gbiz']==zybiz)).sum()
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df_fx.at[ind, f'供总院{i}稿数'] = (df['source'].str.contains(
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dw) & (cons_y_m) & (df['gbiz'] == zybiz)).sum()
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df_fx.at[ind, f'供总院网站{i}稿数'] = (df_web['source'].str.contains(
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dw) & (cons_y_m_web)).sum()
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df_fx[f'供总院{i}稿数'] = df_fx[f'供总院{i}稿数'].fillna(0)
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df_fx[f'供总院{i}稿数'] = df_fx[f'供总院{i}稿数'].astype(int)
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df_fx[f'供总院网站{i}稿数'] = df_fx[f'供总院网站{i}稿数'].fillna(0)
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df_fx[f'供总院网站{i}稿数'] = df_fx[f'供总院网站{i}稿数'].astype(int)
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if gbiz:
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# 进行查询
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# 条件
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cons = (cons_y_m)&(df['gbiz']==gbiz)
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cons = (cons_y_m) & (df['gbiz'] == gbiz)
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cons_sum = (cons).sum()
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df_fx.at[ind, f'{i}发布数'] = cons_sum
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df_fx[f'{i}发布数'] = df_fx[f'{i}发布数'].fillna(0)
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@ -184,9 +255,14 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
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df_fx.at[ind, f'{i}最高点击文章'] = ''
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if cons_sum:
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max_read_row = df[cons].loc[df[cons]['read_num'].idxmax()]
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max_read_row_list = [max_read_row['id'], max_read_row['title'], str(max_read_row['read_num']), f'{max_read_row["pub_year"]}-{max_read_row["pub_month"]}-{max_read_row["pub_day"]}', max_read_row['source']]
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max_read_row_list = [max_read_row['id'], max_read_row['title'], str(
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max_read_row['read_num']), f'{max_read_row["pub_year"]}-{max_read_row["pub_month"]}-{max_read_row["pub_day"]}', max_read_row['source']]
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df_fx.at[ind, f'{i}最高点击文章'] = '***'.join(max_read_row_list)
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df_fx.at[ind, f'总院网站{i}发布数'] = cons_y_m_web.sum()
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df_fx[f'总院网站{i}发布数'] = df_fx[f'总院网站{i}发布数'].fillna(0)
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df_fx[f'总院网站{i}发布数'] = df_fx[f'总院网站{i}发布数'].astype(int)
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# 矫正数据类型
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df_fx = df_fx.applymap(float_to_int)
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# 先输出原始统计数据
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@ -202,29 +278,49 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
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for ind, row in df.iterrows():
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if row['gbiz'] == zybiz:
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sheet.cell(row=ind_zy+3, column=1, value=str(ind_zy+1))
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sheet.cell(row=ind_zy+3, column=2, value=f'{row["pub_year"]}-{row["pub_month"]}-{row["pub_day"]}')
|
||||
sheet.cell(row=ind_zy+3, column=2,
|
||||
value=f'{row["pub_year"]}-{row["pub_month"]}-{row["pub_day"]}')
|
||||
sheet.cell(row=ind_zy+3, column=3, value=row['title'])
|
||||
sheet.cell(row=ind_zy+3, column=4, value=row['source'])
|
||||
sheet.cell(row=ind_zy+3, column=6, value=row['read_num'])
|
||||
sheet.cell(row=ind_zy+3, column=7, value=row['id'])
|
||||
sheet.cell(row=ind_zy+3, column=8, value=row['content_url'])
|
||||
ind_zy = ind_zy + 1
|
||||
sheet_web = workbook['官方网站更新数']
|
||||
sheet_web.cell(row=1, column=1, value=f'关于{year}年度中国建材总院新媒体更新情况明细表\n（网站）')
|
||||
ind_zyweb = 0
|
||||
for ind, row in df_web.iterrows():
|
||||
sheet.cell(row=ind_zy+3, column=1, value=str(ind_zyweb+1))
|
||||
sheet.cell(row=ind_zy+3, column=2, value=f'{row["pub_year"]}-{row["pub_month"]}-{row["pub_day"]}')
|
||||
sheet.cell(row=ind_zy+3, column=3, value=row['title'])
|
||||
sheet.cell(row=ind_zy+3, column=4, value=row['source'])
|
||||
sheet.cell(row=ind_zy+3, column=5, value=row['bankuai'])
|
||||
ind_zyweb = ind_zyweb + 1
|
||||
cbma_path = os.path.join(BASE_DIR, f'summary/{year}年_总院文章.xlsx')
|
||||
workbook.save(cbma_path)
|
||||
print(f'总院{year}年文章表生成完毕!')
|
||||
|
||||
template_cal_path = os.path.join(BASE_DIR, 'summary/template_cbma_cal.xlsx')
|
||||
template_cal_path = os.path.join(
|
||||
BASE_DIR, 'summary/template_cbma_cal.xlsx')
|
||||
workbook2 = load_workbook(template_cal_path)
|
||||
need_df_list = [ "瑞泰科技", "国检集团", "中材高新", "哈玻院", "中国新材院", "秦皇岛院", "西安墙材院", "咸阳陶瓷院", "钟表所", "总院北分", "中岩科技", "水泥新材院", "中建材科创院", "科建苑"]
|
||||
sheet2= workbook2['打分表']
|
||||
need_df_list = ["瑞泰科技", "国检集团", "中材高新", "哈玻院", "中国新材院", "秦皇岛院",
|
||||
"西安墙材院", "咸阳陶瓷院", "钟表所", "总院北分", "中岩科技", "水泥新材院", "中建材科创院", "科建苑"]
|
||||
sheet2 = workbook2['打分表']
|
||||
sheet2.cell(row=1, column=1, value=f'中国建材总院宣传工作计分表（{year}年度）')
|
||||
for ind, val in enumerate(need_df_list):
|
||||
row_ind_df_fx = df_fx['单位'].to_list().index(val)
|
||||
sheet2.cell(row=6, column=5+2*ind, value=df_fx.at[row_ind_df_fx, '供总院全年专稿数'])
|
||||
sheet2.cell(row=10, column=5+2*ind, value=df_fx.at[row_ind_df_fx, '供总院全年组稿数'])
|
||||
sheet2.cell(row=12, column=5+2*ind, value=df_fx.at[row_ind_df_fx, '供总院全年阅读10000及以上数'])
|
||||
sheet2.cell(row=13, column=5+2*ind, value=df_fx.at[row_ind_df_fx, '供总院全年阅读5000及以上数'])
|
||||
sheet2.cell(row=14, column=5+2*ind, value=df_fx.at[row_ind_df_fx, '供总院全年阅读1000及以上数'])
|
||||
sheet2.cell(row=6, column=5+2*ind,
|
||||
value=df_fx.at[row_ind_df_fx, '供总院全年专稿数'])
|
||||
sheet2.cell(row=7, column=5+2*ind,
|
||||
value=df_fx.at[row_ind_df_fx, '供总院网站全年专稿数'])
|
||||
sheet2.cell(row=10, column=5+2*ind,
|
||||
value=df_fx.at[row_ind_df_fx, '供总院全年组稿数'])
|
||||
sheet2.cell(row=12, column=5+2*ind,
|
||||
value=df_fx.at[row_ind_df_fx, '供总院全年阅读10000及以上数'])
|
||||
sheet2.cell(row=13, column=5+2*ind,
|
||||
value=df_fx.at[row_ind_df_fx, '供总院全年阅读5000及以上数'])
|
||||
sheet2.cell(row=14, column=5+2*ind,
|
||||
value=df_fx.at[row_ind_df_fx, '供总院全年阅读1000及以上数'])
|
||||
cbma_cal_path = os.path.join(BASE_DIR, f'summary/{year}年_总院打分.xlsx')
|
||||
workbook2.save(cbma_cal_path)
|
||||
print(f'总院{year}年打分表生成完毕!')
|
||||
|
|
@ -235,13 +331,15 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
|
|||
workbook3 = load_workbook(template_month_path)
|
||||
for i in ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月', '上半年', '下半年', '全年']:
|
||||
try:
|
||||
sheet= workbook3[i]
|
||||
sheet = workbook3[i]
|
||||
except KeyError:
|
||||
sheet = workbook3.copy_worksheet(workbook3['1月'])
|
||||
sheet.title = i
|
||||
sheet.cell(row=1, column=1, value=f'关于{year}年度中国建材总院各企业新媒体更新情况统计表\n（{i}）')
|
||||
sheet.cell(row=1, column=1,
|
||||
value=f'关于{year}年度中国建材总院各企业新媒体更新情况统计表\n（{i}）')
|
||||
# 开始总院填充数据
|
||||
sheet.cell(row=4, column=3, value=df_fx.at[0, f'{i}发布数'])
|
||||
sheet.cell(row=4, column=2, value=df_fx.at[0, f'总院网站{i}发布数'])
|
||||
max_read_row = df_fx.at[dw_list.index('中国建材总院'), f'{i}最高点击文章']
|
||||
if max_read_row:
|
||||
_, title, read_num, pub_date, source = max_read_row.split('***')
|
||||
|
|
@ -250,59 +348,121 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
|
|||
sheet.cell(row=7, column=5, value=pub_date)
|
||||
sheet.cell(row=7, column=6, value=source)
|
||||
# 开始填充各单位数据
|
||||
sheet.cell(row=14, column=3, value=df_fx.at[dw_list.index('瑞泰科技'), f'{i}发布数'])
|
||||
sheet.cell(row=14, column=6, value=df_fx.at[dw_list.index('瑞泰科技'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=14, column=3,
|
||||
value=df_fx.at[dw_list.index('瑞泰科技'), f'{i}发布数'])
|
||||
sheet.cell(row=14, column=6,
|
||||
value=df_fx.at[dw_list.index('瑞泰科技'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=14, column=5,
|
||||
value=df_fx.at[dw_list.index('瑞泰科技'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=15, column=3, value=df_fx.at[dw_list.index('国检集团'), f'{i}发布数'])
|
||||
sheet.cell(row=15, column=6, value=df_fx.at[dw_list.index('国检集团'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=16, column=3, value=df_fx.at[dw_list.index('中材高新'), f'{i}发布数'])
|
||||
sheet.cell(row=16, column=6, value=df_fx.at[dw_list.index('中材高新'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=17, column=3, value=df_fx.at[dw_list.index('哈玻院'), f'{i}发布数'])
|
||||
sheet.cell(row=17, column=6, value=df_fx.at[dw_list.index('哈玻院'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=18, column=3, value=df_fx.at[dw_list.index('中国新材院'), f'{i}发布数'])
|
||||
sheet.cell(row=18, column=6, value=df_fx.at[dw_list.index('中国新材院'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=19, column=3, value=df_fx.at[dw_list.index('秦皇岛院'), f'{i}发布数'])
|
||||
sheet.cell(row=19, column=6, value=df_fx.at[dw_list.index('秦皇岛院'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=20, column=3, value=df_fx.at[dw_list.index('西安墙材院'), f'{i}发布数'])
|
||||
sheet.cell(row=20, column=6, value=df_fx.at[dw_list.index('西安墙材院'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=21, column=3, value=df_fx.at[dw_list.index('咸阳陶瓷院'), f'{i}发布数'])
|
||||
sheet.cell(row=21, column=6, value=df_fx.at[dw_list.index('咸阳陶瓷院'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=22, column=3, value=df_fx.at[dw_list.index('钟表所'), f'{i}发布数'])
|
||||
sheet.cell(row=22, column=6, value=df_fx.at[dw_list.index('钟表所'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=15, column=3,
|
||||
value=df_fx.at[dw_list.index('国检集团'), f'{i}发布数'])
|
||||
sheet.cell(row=15, column=6,
|
||||
value=df_fx.at[dw_list.index('国检集团'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=15, column=5,
|
||||
value=df_fx.at[dw_list.index('国检集团'), f'供总院网站{i}稿数'])
|
||||
|
||||
# sheet.cell(row=23, column=3, value=df_fx.at[dw_list.index('总院北分'), f'{i}发布数'])
|
||||
sheet.cell(row=23, column=6, value=df_fx.at[dw_list.index('总院北分'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=16, column=3,
|
||||
value=df_fx.at[dw_list.index('中材高新'), f'{i}发布数'])
|
||||
sheet.cell(row=16, column=6,
|
||||
value=df_fx.at[dw_list.index('中材高新'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=16, column=5,
|
||||
value=df_fx.at[dw_list.index('中材高新'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=17, column=3,
|
||||
value=df_fx.at[dw_list.index('哈玻院'), f'{i}发布数'])
|
||||
sheet.cell(row=17, column=6,
|
||||
value=df_fx.at[dw_list.index('哈玻院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=17, column=5,
|
||||
value=df_fx.at[dw_list.index('哈玻院'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=18, column=3,
|
||||
value=df_fx.at[dw_list.index('中国新材院'), f'{i}发布数'])
|
||||
sheet.cell(row=18, column=6,
|
||||
value=df_fx.at[dw_list.index('中国新材院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=18, column=5,
|
||||
value=df_fx.at[dw_list.index('中国新材院'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=24, column=3, value=df_fx.at[dw_list.index('中岩科技'), f'{i}发布数'])
|
||||
sheet.cell(row=24, column=6, value=df_fx.at[dw_list.index('中岩科技'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=19, column=3,
|
||||
value=df_fx.at[dw_list.index('秦皇岛院'), f'{i}发布数'])
|
||||
sheet.cell(row=19, column=6,
|
||||
value=df_fx.at[dw_list.index('秦皇岛院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=19, column=5,
|
||||
value=df_fx.at[dw_list.index('秦皇岛院'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=20, column=3,
|
||||
value=df_fx.at[dw_list.index('西安墙材院'), f'{i}发布数'])
|
||||
sheet.cell(row=20, column=6,
|
||||
value=df_fx.at[dw_list.index('西安墙材院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=20, column=5,
|
||||
value=df_fx.at[dw_list.index('西安墙材院'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=21, column=3,
|
||||
value=df_fx.at[dw_list.index('咸阳陶瓷院'), f'{i}发布数'])
|
||||
sheet.cell(row=21, column=6,
|
||||
value=df_fx.at[dw_list.index('咸阳陶瓷院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=21, column=5,
|
||||
value=df_fx.at[dw_list.index('咸阳陶瓷院'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=22, column=3,
|
||||
value=df_fx.at[dw_list.index('钟表所'), f'{i}发布数'])
|
||||
sheet.cell(row=22, column=6,
|
||||
value=df_fx.at[dw_list.index('钟表所'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=22, column=5,
|
||||
value=df_fx.at[dw_list.index('钟表所'), f'供总院网站{i}稿数'])
|
||||
|
||||
# sheet.cell(row=23, column=3, value=df_fx.at[dw_list.index('总院北分'), f'{i}发布数'])
|
||||
sheet.cell(row=23, column=6,
|
||||
value=df_fx.at[dw_list.index('总院北分'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=23, column=5,
|
||||
value=df_fx.at[dw_list.index('总院北分'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=24, column=3,
|
||||
value=df_fx.at[dw_list.index('中岩科技'), f'{i}发布数'])
|
||||
sheet.cell(row=24, column=6,
|
||||
value=df_fx.at[dw_list.index('中岩科技'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=24, column=5,
|
||||
value=df_fx.at[dw_list.index('中岩科技'), f'供总院网站{i}稿数'])
|
||||
|
||||
# sheet.cell(row=25, column=3, value=df_fx.at[dw_list.index('水泥新材院'), f'{i}发布数'])
|
||||
sheet.cell(row=25, column=6, value=df_fx.at[dw_list.index('水泥新材院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=25, column=6,
|
||||
value=df_fx.at[dw_list.index('水泥新材院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=25, column=5,
|
||||
value=df_fx.at[dw_list.index('水泥新材院'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=26, column=3, value=df_fx.at[dw_list.index('中建材科创院'), f'{i}发布数'])
|
||||
sheet.cell(row=26, column=6, value=df_fx.at[dw_list.index('中建材科创院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=26, column=3,
|
||||
value=df_fx.at[dw_list.index('中建材科创院'), f'{i}发布数'])
|
||||
sheet.cell(row=26, column=6,
|
||||
value=df_fx.at[dw_list.index('中建材科创院'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=26, column=5,
|
||||
value=df_fx.at[dw_list.index('中建材科创院'), f'供总院网站{i}稿数'])
|
||||
|
||||
# sheet.cell(row=27, column=3, value=df_fx.at[dw_list.index('科建苑'), f'{i}发布数'])
|
||||
sheet.cell(row=27, column=6, value=df_fx.at[dw_list.index('科建苑'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=27, column=6,
|
||||
value=df_fx.at[dw_list.index('科建苑'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=27, column=5,
|
||||
value=df_fx.at[dw_list.index('科建苑'), f'供总院网站{i}稿数'])
|
||||
|
||||
sheet.cell(row=29, column=2, value=df_fx.at[dw_list.index('办公室（董事会办公室）'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=30, column=2, value=df_fx.at[dw_list.index('党委组织部/人力资源部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=31, column=2, value=df_fx.at[dw_list.index('财务部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=32, column=2, value=df_fx.at[dw_list.index('科技部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=33, column=2, value=df_fx.at[dw_list.index('投资部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=29, column=7, value=df_fx.at[dw_list.index('企业管理部、安全环保部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=30, column=7, value=df_fx.at[dw_list.index('党群部/宣传统战部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=31, column=7, value=df_fx.at[dw_list.index('党风办/巡察办、纪委综合室'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=32, column=7, value=df_fx.at[dw_list.index('监督执纪室'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=33, column=7, value=df_fx.at[dw_list.index('审计办公室'), f'供总院{i}稿数'])
|
||||
|
||||
sheet.cell(row=29, column=2,
|
||||
value=df_fx.at[dw_list.index('办公室（董事会办公室）'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=30, column=2,
|
||||
value=df_fx.at[dw_list.index('党委组织部/人力资源部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=31, column=2,
|
||||
value=df_fx.at[dw_list.index('财务部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=32, column=2,
|
||||
value=df_fx.at[dw_list.index('科技部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=33, column=2,
|
||||
value=df_fx.at[dw_list.index('投资部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=29, column=7,
|
||||
value=df_fx.at[dw_list.index('企业管理部、安全环保部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=30, column=7,
|
||||
value=df_fx.at[dw_list.index('党群部/宣传统战部'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=31, column=7,
|
||||
value=df_fx.at[dw_list.index('党风办/巡察办、纪委综合室'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=32, column=7,
|
||||
value=df_fx.at[dw_list.index('监督执纪室'), f'供总院{i}稿数'])
|
||||
sheet.cell(row=33, column=7,
|
||||
value=df_fx.at[dw_list.index('审计办公室'), f'供总院{i}稿数'])
|
||||
|
||||
cbma_month_path = os.path.join(BASE_DIR, f'summary/{year}年_单位月度.xlsx')
|
||||
workbook3.save(cbma_month_path)
|
||||
|
|
@ -312,10 +472,11 @@ def get_cbma_info_from_db_and_ana(year: str = '2023'):
|
|||
|
||||
|
||||
def make_wechat_articles_full():
|
||||
df = pd.read_csv(os.path.join(wechat_dir, 'articles.csv'))
|
||||
df = pd.read_csv(os.path.join(wechat_dir, 'articles.csv'))
|
||||
df['content'] = ''
|
||||
for ind, row in df.iterrows():
|
||||
full_path = os.path.join(wechat_dir, row['nickname'], row['id'] + '.md')
|
||||
full_path = os.path.join(
|
||||
wechat_dir, row['nickname'], row['id'] + '.md')
|
||||
try:
|
||||
with open(full_path, encoding='utf-8') as f:
|
||||
df.at[ind, 'content'] = f.read()
|
||||
|
|
@ -324,6 +485,7 @@ def make_wechat_articles_full():
|
|||
output_path = os.path.join(wechat_dir, 'articles_full.csv')
|
||||
df.to_csv(output_path)
|
||||
|
||||
|
||||
def ana_wechat():
|
||||
articles_full_path = os.path.join(wechat_dir, 'articles_full.csv')
|
||||
if not os.path.exists(articles_full_path):
|
||||
|
|
@ -341,7 +503,7 @@ def ana_wechat():
|
|||
|
||||
if not result.empty:
|
||||
for ind2, row2 in result.iterrows():
|
||||
if row['错误表述'] == '“两学一做”学习' and '“两学一做”学习教育' in row2['content']:
|
||||
if row['错误表述'] == '“两学一做”学习' and '“两学一做”学习教育' in row2['content']:
|
||||
continue
|
||||
if row['错误表述'] == '20大':
|
||||
continue
|
||||
|
|
@ -361,12 +523,14 @@ def ana_wechat():
|
|||
|
||||
return output_data
|
||||
|
||||
|
||||
def find_title(text):
|
||||
match = re.search(r'#\s*.*', text, re.MULTILINE)
|
||||
if match:
|
||||
return match.group(0).strip() # 去除两边的空白字符
|
||||
return "/"
|
||||
|
||||
|
||||
def ana_web():
|
||||
output_data = []
|
||||
index = 1
|
||||
|
|
@ -382,15 +546,15 @@ def ana_web():
|
|||
name = row['主办']
|
||||
url = fix_url_scheme(row['地址'].strip())
|
||||
domain = urlparse(url).netloc.replace('www.', '')
|
||||
full_path = os.path.join(BASE_DIR, f'web_dir/{name}_{domain}.xlsx')
|
||||
if os.path.exists(full_path) and os.path.getsize(full_path) > 0:
|
||||
full_path = os.path.join(BASE_DIR, f'web_dir/{name}_{domain}.xlsx')
|
||||
if os.path.exists(full_path) and os.path.getsize(full_path) > 0:
|
||||
df = pd.read_excel(os.path.join(full_path), engine='openpyxl')
|
||||
for ind, row in df_s.iterrows():
|
||||
mask = df['text'].str.contains(row['错误表述'], na=False)
|
||||
result = df[mask]
|
||||
if not result.empty:
|
||||
for ind2, row2 in result.iterrows():
|
||||
if row['错误表述'] == '“两学一做”学习' and '“两学一做”学习教育' in row2['text']:
|
||||
if row['错误表述'] == '“两学一做”学习' and '“两学一做”学习教育' in row2['text']:
|
||||
continue
|
||||
if row['错误表述'] == '20大':
|
||||
continue
|
||||
|
|
@ -410,6 +574,42 @@ def ana_web():
|
|||
|
||||
return output_data
|
||||
|
||||
if __name__ == "__main__":
|
||||
get_cbma_info_from_db_and_ana()
|
||||
|
||||
if __name__ == "__main__":
|
||||
# get_cbma_info_from_db_and_ana()
|
||||
import psycopg2
|
||||
conn = None
|
||||
try:
|
||||
conn = psycopg2.connect(
|
||||
"dbname={} user={} password={} host={} port={}".format('edn_cms', 'auditor', 'Lde78B3_cbma', '10.65.253.10', '54321'))
|
||||
cur = conn.cursor()
|
||||
year = 2023
|
||||
query = f"""
|
||||
SELECT
|
||||
a_outer.id,
|
||||
TO_CHAR(a_outer.ctime, 'YYYY-MM-DD') AS ctime,
|
||||
a_outer.title,
|
||||
a_outer.source,
|
||||
a_outer.hits,
|
||||
t.title as bankuai,
|
||||
a_outer.src
|
||||
FROM
|
||||
"a_article" a_outer
|
||||
left join (
|
||||
select id, title, father, path
|
||||
from a_article
|
||||
where father in (20110528, 19080024)
|
||||
) t on a_outer.father = t.id
|
||||
WHERE
|
||||
a_outer.TYPE = 3
|
||||
and a_outer.deleted is NULL
|
||||
and EXTRACT ( YEAR FROM a_outer.ctime ) = {year}
|
||||
and bankuai is not NULL
|
||||
ORDER BY
|
||||
a_outer.ctime desc;
|
||||
"""
|
||||
df = pd.read_sql_query(query, conn)
|
||||
print(df)
|
||||
cur.close()
|
||||
except Exception as e:
|
||||
pass
|
||||
Loading…
Reference in New Issue