def bar_base(x, y, xline, yline): scatter = Scatter().add_xaxis(x).add_yaxis( '真实值', y).set_global_opts(title_opts=opts.TitleOpts(title="相关线")) xline = xline.reshape(-1) line = Line().add_xaxis(xaxis_data=xline.tolist()).add_yaxis( series_name="拟合线", y_axis=yline.tolist()) scatter.overlap(line) return scatter
def plt(key): """连接hbase""" connection = happybase.Connection(host="localhost", port=9090) """打开传输""" connection.open() """连接表""" basicFeaturesTable = happybase.Table('basicFeaturesTable', connection) socialAttributesTable = happybase.Table('socialAttributesTable', connection) consumptionFeaturesTable = happybase.Table('consumptionFeaturesTable', connection) internetFeaturesTable = happybase.Table('internetFeaturesTable', connection) consumptionCharacteristicsTable = happybase.Table( 'consumptionCharacteristicsTable', connection) internetBehaviorTable = happybase.Table('internetBehaviorTable', connection) """获取信息""" try: key_name = basicFeaturesTable.row(key, columns=["name"]) except: """连接hbase""" connection = happybase.Connection(host="localhost", port=9090) """打开传输""" connection.open() """连接表""" basicFeaturesTable = happybase.Table('basicFeaturesTable', connection) socialAttributesTable = happybase.Table('socialAttributesTable', connection) consumptionFeaturesTable = happybase.Table('consumptionFeaturesTable', connection) internetFeaturesTable = happybase.Table('internetFeaturesTable', connection) consumptionCharacteristicsTable = happybase.Table( 'consumptionCharacteristicsTable', connection) internetBehaviorTable = happybase.Table('internetBehaviorTable', connection) key_name = basicFeaturesTable.row(key, columns=["name"]) key_name = list(key_name.values()) key_name = key_name[0].decode() try: sInfo = socialAttributesTable.row(key, columns=["incLevel", "eduLevel"]) cInfo = consumptionFeaturesTable.row(key, columns=["CMLevel", "CTLevel"]) iInfo = internetFeaturesTable.row(key, columns=["intLevel"]) consumptionCharacteristicsInfo = consumptionCharacteristicsTable.row( key, columns=["commodity", "price", "date"]) internetBehaviorInfo = internetBehaviorTable.row \ (key, columns= ["date", "news", "communications", "entertainment", "domersticServices", "busApp", "toolUse"] ) except: """连接hbase""" connection = happybase.Connection(host="localhost", port=9090) """打开传输""" connection.open() """连接表""" basicFeaturesTable = happybase.Table('basicFeaturesTable', connection) socialAttributesTable = happybase.Table('socialAttributesTable', connection) consumptionFeaturesTable = happybase.Table('consumptionFeaturesTable', connection) internetFeaturesTable = happybase.Table('internetFeaturesTable', connection) consumptionCharacteristicsTable = happybase.Table( 'consumptionCharacteristicsTable', connection) internetBehaviorTable = happybase.Table('internetBehaviorTable', connection) sInfo = socialAttributesTable.row(key, columns=["incLevel", "eduLevel"]) cInfo = consumptionFeaturesTable.row(key, columns=["CMLevel", "CTLevel"]) iInfo = internetFeaturesTable.row(key, columns=["intLevel"]) consumptionCharacteristicsInfo = consumptionCharacteristicsTable.row( key, columns=["commodity", "price", "date"]) internetBehaviorInfo = internetBehaviorTable.row \ (key, columns= ["date", "news", "communications", "entertainment", "domersticServices", "busApp", "toolUse"] ) """消费情况图""" consumptionCharacteristicsValues = list( consumptionCharacteristicsInfo.values()) consumptionCharacteristicsValues = list( map(lambda x: x.decode(), consumptionCharacteristicsValues)) consumptionCharacteristicsValues[ int(len(consumptionCharacteristicsValues) / 3):-int(len(consumptionCharacteristicsValues) / 3)] \ = list(map(int, consumptionCharacteristicsValues[int(len(consumptionCharacteristicsValues) / 3): -int(len(consumptionCharacteristicsValues) / 3)])) consumptionCharacteristicsValues[-int(len(consumptionCharacteristicsValues) / 3):] \ = list(map(int, consumptionCharacteristicsValues[-int(len(consumptionCharacteristicsValues) / 3):])) consumptionCharacteristicsData = [] consumptionCharacteristicsData.append( consumptionCharacteristicsValues[:int( len(consumptionCharacteristicsValues) / 3)]) consumptionCharacteristicsData.append(consumptionCharacteristicsValues[ int(len(consumptionCharacteristicsValues) / 3):-int(len(consumptionCharacteristicsValues) / 3)]) consumptionCharacteristicsData.append(consumptionCharacteristicsValues[ -int(len(consumptionCharacteristicsValues) / 3):]) consumptionCharacteristicsData = pd.DataFrame( consumptionCharacteristicsData).T consumptionCharacteristicsData.columns = ["commodity", "date", "price"] consumptionCharacteristicsData.sort_values(by="date", inplace=True) consumptionCharacteristicsData_y = consumptionCharacteristicsData.loc[:, "date"].tolist( ) consumptionCharacteristicsData_y = list( map(str, consumptionCharacteristicsData_y)) consumptionCharacteristicsData_x = consumptionCharacteristicsData.loc[:, "price"].tolist( ) consumptionCharacteristicsData_commodity = consumptionCharacteristicsData.loc[:, "commodity"].tolist( ) quzhong_consumptionCharacteristicsData_y = list( set(consumptionCharacteristicsData_y)) if len(quzhong_consumptionCharacteristicsData_y) != len( consumptionCharacteristicsData_y): for i in range(len(quzhong_consumptionCharacteristicsData_y)): count_consumptionCharacteristicsData_y = consumptionCharacteristicsData_y. \ count(quzhong_consumptionCharacteristicsData_y[i]) if count_consumptionCharacteristicsData_y != 1: index = consumptionCharacteristicsData_y.index( quzhong_consumptionCharacteristicsData_y[i]) consumptionCharacteristicsData_y = consumptionCharacteristicsData_y[:index + 1] + \ consumptionCharacteristicsData_y[ index + count_consumptionCharacteristicsData_y:] consumptionCharacteristicsData_x[index] = sum( consumptionCharacteristicsData_x[ index:index + count_consumptionCharacteristicsData_y]) consumptionCharacteristicsData_x = consumptionCharacteristicsData_x[:index + 1] + \ consumptionCharacteristicsData_x[ index + count_consumptionCharacteristicsData_y:] consumptionCharacteristicsData_commodity[index] = \ ",".join( consumptionCharacteristicsData_commodity[index:index + count_consumptionCharacteristicsData_y]) consumptionCharacteristicsData_commodity \ = consumptionCharacteristicsData_commodity[:index + 1] + \ consumptionCharacteristicsData_commodity[index + count_consumptionCharacteristicsData_y:] consumptionCharacteristicsData_xx = \ [list(z) for z in zip(consumptionCharacteristicsData_x, consumptionCharacteristicsData_commodity)] scatter = Scatter(init_opts=opts.InitOpts(width="850px", height="380px")) scatter.add_xaxis(consumptionCharacteristicsData_y) scatter.add_yaxis("", consumptionCharacteristicsData_xx, color="red") scatter.set_global_opts( tooltip_opts=opts.TooltipOpts( trigger="item", axis_pointer_type="cross", formatter=JsCode( "function (params) {return '消费日期:' + params.name + ' <br/>消费金额:' + params.value[1] + '元 <br/>消费产品:' + params.value[2];}" )), yaxis_opts=opts.AxisOpts( name="消费金额", type_="value", name_textstyle_opts=opts.TextStyleOpts(color="white"), ########### axislabel_opts=opts.LabelOpts(formatter="{value} 元", border_color="white", color="white"), ######## axistick_opts=opts.AxisTickOpts(is_show=True), splitline_opts=opts.SplitLineOpts(is_show=True), ), xaxis_opts=opts.AxisOpts( name="消费日期", type_="category", name_textstyle_opts=opts.TextStyleOpts(color="white"), ########### axislabel_opts=opts.LabelOpts(border_color="white", color="white"), ############## axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), ), legend_opts=opts.LegendOpts(is_show=False)) scatter.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) line = Line(init_opts=opts.InitOpts(width="850px", height="380px")) line.add_xaxis(consumptionCharacteristicsData_y) line.add_yaxis(series_name="", y_axis=consumptionCharacteristicsData_x, color="red") scatter.overlap(line) scatter.render("./static/html/pictureConsumptionCharacteristics.html") """互联网行为图""" internetBehaviorInfoKeys = list(internetBehaviorInfo.keys()) internetBehaviorInfoKeys = list( map(lambda x: x.decode(), internetBehaviorInfoKeys)) internetBehaviorInfoKeys = list( map(lambda x: x[:x.index(':')], internetBehaviorInfoKeys)) internetBehaviorInfoValues = list(internetBehaviorInfo.values()) internetBehaviorInfoValues = list( map(lambda x: x.decode(), internetBehaviorInfoValues)) internetBehaviorInfoValues = list(map(int, internetBehaviorInfoValues)) internetBehaviorData = [] splitNum = int(len(internetBehaviorInfoValues) / 7) internetBehaviorData.append(internetBehaviorInfoValues[:splitNum]) internetBehaviorData.append(internetBehaviorInfoValues[splitNum:2 * splitNum]) internetBehaviorData.append(internetBehaviorInfoValues[2 * splitNum:3 * splitNum]) internetBehaviorData.append(internetBehaviorInfoValues[3 * splitNum:4 * splitNum]) internetBehaviorData.append(internetBehaviorInfoValues[4 * splitNum:5 * splitNum]) internetBehaviorData.append(internetBehaviorInfoValues[5 * splitNum:6 * splitNum]) internetBehaviorData.append(internetBehaviorInfoValues[6 * splitNum:7 * splitNum]) internetBehaviorData = pd.DataFrame(internetBehaviorData).T internetBehaviorData.columns = [ internetBehaviorInfoKeys[0], internetBehaviorInfoKeys[splitNum], internetBehaviorInfoKeys[2 * splitNum], internetBehaviorInfoKeys[3 * splitNum], internetBehaviorInfoKeys[4 * splitNum], internetBehaviorInfoKeys[5 * splitNum], internetBehaviorInfoKeys[6 * splitNum] ] internetBehaviorData.sort_values(by="date", inplace=True) internetBehaviorData_y = internetBehaviorData.loc[:, "date"].tolist() internetBehaviorData_y = list(map(str, internetBehaviorData_y)) bar = Bar(init_opts=opts.InitOpts(width="800px", height="380px")) bar.add_xaxis(internetBehaviorData_y) bar.add_yaxis(series_name="新闻资讯", yaxis_data=internetBehaviorData.loc[:, "news"].tolist(), stack="stack") bar.add_yaxis( series_name="通信交流", yaxis_data=internetBehaviorData.loc[:, "communications"].tolist(), stack="stack") bar.add_yaxis( series_name="娱乐休闲", yaxis_data=internetBehaviorData.loc[:, "entertainment"].tolist(), stack="stack") bar.add_yaxis( series_name="生活服务", yaxis_data=internetBehaviorData.loc[:, "domersticServices"].tolist(), stack="stack") bar.add_yaxis(series_name="商务应用", yaxis_data=internetBehaviorData.loc[:, "busApp"].tolist(), stack="stack") bar.add_yaxis(series_name="工具使用", yaxis_data=internetBehaviorData.loc[:, "toolUse"].tolist(), stack="stack") bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) bar.set_global_opts( tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"), xaxis_opts=opts.AxisOpts( name="日期", name_textstyle_opts=opts.TextStyleOpts(color="white"), ########### type_="category", axislabel_opts=opts.LabelOpts(border_color="white", color="white"), ############# axispointer_opts=opts.AxisPointerOpts(is_show=True), ), yaxis_opts=opts.AxisOpts( name="时间", name_textstyle_opts=opts.TextStyleOpts(color="white"), type_="value", min_=0, max_=24, interval=4, axislabel_opts=opts.LabelOpts(formatter="{value} 小时", border_color="white", color="white"), ########### axistick_opts=opts.AxisTickOpts(is_show=True), ), datazoom_opts=opts.DataZoomOpts(type_="inside"), legend_opts=opts.LegendOpts(is_show=True, textstyle_opts=opts.TextStyleOpts( border_color="white", color="wihte"), orient='horizontal')) bar.render("./static/html/pictureInternetBehavior.html") """个人特征图""" sValue = list(sInfo.values()) sValue = list(map(lambda x: x.decode(), sValue)) cValue = list(cInfo.values()) cValue = list(map(lambda x: x.decode(), cValue)) iValue = list(iInfo.values()) iValue = list(map(lambda x: x.decode(), iValue)) value = sValue + cValue + iValue value = list(map(int, value)) value = [value[1], value[2], value[3], value[0], value[4]] value = [value] rader = Radar(init_opts=opts.InitOpts(width="350px", height="350px")) rader.add_schema(schema=[ opts.RadarIndicatorItem(name="收入指数", max_=5), opts.RadarIndicatorItem(name="消费金\n额指数", max_=5), opts.RadarIndicatorItem(name="消费次数指数", max_=5), opts.RadarIndicatorItem(name="学历指数", max_=5), opts.RadarIndicatorItem(name="网络依\n赖指数", max_=5), ], shape='polygon') rader.add(series_name=key_name, data=value) rader.set_global_opts(legend_opts=opts.LegendOpts(is_show=False)) rader.render("./static/html/pictureCompositiveInfo.html")