zabbix获取一周内各个等级告警的次数

 # encoding:UTF-8
 import xlsxwriter
 import datetime
 import pymysql
 import numpy as np
 import pandas

 __author__ = 'sanjing'
 __data__ = '2019/06/14'

 averagesql = """
     select from_unixtime(clock,'%Y-%m-%d') as time,
         count(DISTINCT eventid) as id
         from alerts
         WHERE mediatypeid = 5
         AND message LIKE '%告警等级: Average%'
         AND SUBJECT  LIKE '%PROBLEM%'
         AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
         group by time
         order by time asc

 """

 highsql = """
     select from_unixtime(clock,'%Y-%m-%d') as time,
         count(DISTINCT eventid) as id
         from alerts
         WHERE mediatypeid = 5
         AND message LIKE '%告警等级: High%'
         AND SUBJECT  LIKE '%PROBLEM%'
         AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
         group by time
         order by time asc
 """

 warningsql = """
     select from_unixtime(clock,'%Y-%m-%d') as time,
         count(DISTINCT eventid) as id
         from alerts
         WHERE mediatypeid = 5
         AND message LIKE '%告警等级: Warning%'
         AND SUBJECT  LIKE '%PROBLEM%'
         AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
         group by time
         order by time asc
 """

 informationsql = """
     select from_unixtime(clock,'%Y-%m-%d') as time,
         count(DISTINCT eventid) as id
         from alerts
         WHERE mediatypeid = 5
         AND message LIKE '%告警等级: Information%'
         AND SUBJECT  LIKE '%PROBLEM%'
         AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
         group by time
         order by time asc
 """

 disastersql = """
     select from_unixtime(clock,'%Y-%m-%d') as time,
         count(DISTINCT eventid) as id
         from alerts
         WHERE mediatypeid = 5
         AND message LIKE '%告警等级: Diasater%'
         AND SUBJECT  LIKE '%PROBLEM%'
         AND clock BETWEEN UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 7 DAY)) AND UNIX_TIMESTAMP(DATE_SUB(NOW(), INTERVAL 1 DAY))
         group by time
         order by time asc
 """

 #连接MySQL数据库
 def get_count(sql):
     conn=pymysql.connect("xx.xx.xx.xx", "readonly", "xxxx", "zabbix", charset='utf8')
     cursor = conn.cursor()
     cursor.execute(sql)
     count = cursor.fetchall()
     # 将rows转化为数组
     rows = np.array(count)
     conn.close()
     return count

 def coloum(data, weekendtime):
 #创建一个excel文件
     workbook = xlsxwriter.Workbook(weekendtime +".xlsx")
 #创建一个工作表，默认sheet1
     worksheet = workbook.add_worksheet()
     bold = workbook.add_format({'bold': 1})
 #表头

     title = ['告警级别', '星期一','星期二','星期三','星期四','星期五','星期六','星期日']
 #列名
     buname = ['information', 'warning','average', 'high', 'disaster']
 # 定义数据formatter格式对象，设置边框加粗1像素
     formatter = workbook.add_format()
     formatter.set_border(1)
 #定义格式：# 定义标题栏格式对象：边框加粗1像素，背景色为灰色，单元格内容居中、加粗
     title_formatter = workbook.add_format()
     title_formatter.set_border(1)
     title_formatter.set_bg_color('#cccccc')
     title_formatter.set_align('center')
     title_formatter.set_bold()
     chart_col = workbook.add_chart({'type': 'column'})
     def chart_series(row):
         chart_col.add_series(
             {
                 'categories': '=Sheet1!$B$1:$H$1',
                 'values': '=Sheet1!$B${}:$H${}'.format(row, row),
                 'line': {'color': 'black'},
 #                'name': '=Sheet1!$A${}'.format(row)
                 'name': '=Sheet1!$A$' + row
             }
         )
 # 下面分别以行和列的方式将标题栏、业务名称、流量数据写入单元格，并引用不同的格式对象
     worksheet.write_row('A1',title,title_formatter)
     worksheet.write_column('A2',buname,formatter)
     for i in range (2,7):
         worksheet.write_row('B{}'.format(i),data[i-2],formatter)
         print (i)
         chart_series(str(i))

     # 设置图表的title 和 x，y轴信息
     chart_col.set_title({'name': '告警统计/周'})
     chart_col.set_x_axis({'name': '告警级别'})
     chart_col.set_y_axis({'name': '告警次数'})
     # 设置图表的风格
 #    chart_col.set_style(37)

     # 把图表插入到worksheet以及偏移
     worksheet.insert_chart('A10', chart_col, {'x_offset': 25, 'y_offset': 10})
     workbook.close()
 #判断二维元组是否为空，长度是否满足要求，不满足则补0.
 #输入为一个字典，判断是否为空，空则添加数据
 def covertdata(jsondata,weektime):

     listkey = list(jsondata.keys())
     for i in weektime:
         j = i.strftime("%Y-%m-%d")
         if listkey:
             if j not in listkey:
                 jsondata[j] = ""
         else:
             jsondata[j] = ""
 #    print (jsondata)
 #按照时间对字典进行排序
     sort = sorted(jsondata.items(), key=lambda d: d[0])
 #将第二列取出来并转为列表
     array = np.array(sort)
     array2 = array[:, 1]
     list2 = array2.tolist()
     list3 = list(map(lambda x: float(x), list2))
     return list3
 #    print (list2)

 if __name__ == '__main__':
     yesterday = (datetime.date.today() + datetime.timedelta(days=-1)).strftime("%Y-%m-%d")
     weeklist = pandas.date_range(end=yesterday, periods=7)
     informationdata = get_count(informationsql)
     informationlist = covertdata(dict(informationdata),weeklist)
     warningdata = get_count(warningsql)
     warninglist = covertdata(dict(warningdata),weeklist)
     averagedata = get_count(averagesql)
     averagelist = covertdata(dict(averagedata),weeklist)
     highdata = get_count(highsql)
     highlist = covertdata(dict(highdata),weeklist)
     disasterdata = get_count(disastersql)
     disasterlist = covertdata(dict(disasterdata),weeklist)
 #    print (informationlist)
 #    print (warninglist)
 #    print (averagelist)
 #    print (highlist)
 #    print (disasterlist)
     data1 = [informationlist, warninglist, averagelist, highlist, disasterlist]
     coloum(data1,yesterday)

结果如图：