【作业】多图表

　　为了表示出多图表，数据采用了老师在51Job上爬取的数据

　　以下是涉及到的代码段

　　数据库Dao层

#定义一个职位数据操作的数据库访问类
class JobPositionDao(BaseDao):

    def __init__(self):
        super().__init__()

    #   查询各个语言的平均薪资
    def findPositionClassify(self):
        sql = "select avg(job_meansalary),job_taskid,task_title from job_position,job_collect_task where job_position.job_taskid = job_collect_task.task_id group by job_taskid,task_title;"
        result = self.execute(sql,params=None)
        self.commit()
        return self.fetch()
    #   查询各个语言在不同城市的平均薪资
    def findCityPositionClassify(self):
        sql = "select avg(t1.job_meansalary) as m,t1.job_taskid,t2.task_title,t1.job_city from job_position t1 left join job_collect_task t2 on t1.job_taskid = t2.task_id group by job_taskid,job_city,t2.task_title order by t1.job_taskid asc,m desc;"
        result = self.execute(sql,params=None)
        self.commit()
        return self.fetch()
        pass
    #   查询python在不同日期上的岗位新发布数
    def findPythonNewCountByDate(self):
        sql = "SELECT COUNT(1) as count,jc.task_title,jp.FBTime FROM `job_position` as jp,`job_collect_task` as jc WHERE jp.job_taskid=5 GROUP BY jp.job_taskid,jp.FBTime ORDER BY jp.FBTime; "
        result = self.execute(sql, params=None)
        self.commit()
        return self.fetch()
        pass

　　图表绘制

from day022.spiderproject.spiderproject.dao.jobpositiondao import JobPositionDao
import numpy as np
import matplotlib.pyplot as plt


class Chart():
    def __init__(self,shape:tuple=None):
        plt.rcParams['font.sans-serif'] = ['SimHei']
        plt.rcParams['axes.unicode_minus'] = False
        self.__x,self.__y=shape or (None,None)
        plt.figure()
        pass

    def LineChart(self,dataList,colorList,labelList,xyMsg:tuple = None,title = None,count = 1):
        #   分块
        if self.__x and self.__y:
            axLine = plt.subplot(self.__x,self.__y,count)
            plt.sca(axLine)#    选择当前块
        for itData,c,l in zip(dataList,colorList,labelList):
            plt.plot(*itData,c=c,label=l)

        if xyMsg:
            plt.xlabel(xyMsg[0])
            plt.ylabel(xyMsg[1])
        if title:
            plt.title(title)
        plt.xticks(rotation=45) #横坐标标签旋转45度
        plt.legend(loc='best')
        pass

    def pieChart(self,dataList,labelList = None,title=None,count = 1):
        if self.__x and self.__y:
            axPie = plt.subplot(self.__x,self.__y,count)
            plt.sca(axPie)
        plt.pie(dataList,labels=labelList,autopct="%1.2f")# autopct是比率精度
        if title:
            plt.title(title)
        plt.axis('equal')
        pass

    def barChart(self,xData,yData,count = 1,title = None):
        if self.__x and self.__y:
            axBar = plt.subplot(self.__x,self.__y,count)
            plt.sca(axBar)
        plt.bar(x=xData,height=yData)
        for x,v in zip(xData,yData):
            plt.text(x,v+10,"{0}".format(v),ha="center")
        if title:
            plt.title(title)
        pass

    def show(self):
        plt.show()



    pass
#   折线图数据处理和绘制函数
def lineChart(jpDao:JobPositionDao,chartView:Chart):
    avgList = jpDao.findCityPositionClassify()
    lineList = {}
    #   分成{"语言标题":[(城市，薪资)],……}的格式，薪资保留两位小数
    for m, i, t, c in avgList:
        if not lineList.get(t):
            lineList[t] = []
        lineList[t].append((c, round(m, 2)))
    #   按城市排序
    for k in lineList.keys():
        lineList[k].sort(key=lambda x: x[0])
    minCityList = None
    #   因为采样数有限，所以取四个语言的样本中都有的城市
    for k in lineList.keys():
        if minCityList == None:
            minCityList = set(np.array(lineList[k]).T[0])
            continue
        minCityList &= set(np.array(lineList[k]).T[0])
    #   城市排序
    minCityList = sorted(list(minCityList))
    #   去除已有城市数据
    for k in lineList.keys():
        i = 0
        while i < len(minCityList):
            if lineList[k][i][0] != minCityList[i]:
                lineList[k].pop(i)
            else:
                i += 1
    dataList = [np.array(v).T for v in lineList.values()]
    dataList = [[it[0],it[1].astype(np.float)] for it in dataList]
    chartView.LineChart(dataList, ['red', 'green', 'blue', 'black'],
                        [k for k in lineList.keys()], xyMsg=("城市", "工资平均值"), title="四种语言在各个城市中的平均工资")
    pass


def pieChart(jpDao:JobPositionDao,chartView:Chart):
    avgList = np.array(jpDao.findPositionClassify()).T
    labelList = [avgList[2][i]+":"+str(avgList[0][i].astype(np.float).round(2)) for i in range(avgList.__len__() + 1)]
    chartView.pieChart(avgList[0].astype(np.float).round(2),labelList=labelList,title="各个语言的总平均工资",count=2)
    pass

def barChart(jpDao:JobPositionDao,chartView:Chart):
    avgList =np.array(jpDao.findPythonNewCountByDate()).T
    chartView.barChart(avgList[2],avgList[0].astype(np.int),count=3,title="Python职位在不同日期的新需求岗位数")


jp = JobPositionDao()
chartView = Chart(shape=(2,2))
lineChart(jp,chartView)
pieChart(jp,chartView)
barChart(jp,chartView)
chartView.show()

 　　结果