def DrawRadar(city_name):
schema = [('最高气温', 45), ('气压', 1500), ('湿度', 100), ('最低气温', 45), ('PM2.5', 200)]
data = qr.get_data(city_name)
values = [[int(data['Htemperature']), int(data['QY']), int(data['SD'].strip('%')), int(data['Ltemperature']), int(data['pm25'])]]
radar = Radar()
radar.config(schema)
radar.add('%s天气信息' % city_name, values, is_splitline=True, is_axisline_show=True)
radar.render('weatherRadar.html')
def radar_demo():
radar = Radar()
# data1 = [[12, 0, 2, 18, 0, 1]]
radar_data1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]#降水量
# schema = [
# ("执行成功", 20), ("执行失败", 5), ("执行异常", 5),
# ("回滚成功", 20), ("回滚失败", 5), ("执行中断", 5)
# ]
schema = [
("Jan", 5), ("Feb", 10), ("Mar", 10),
("Apr", 50), ("May", 50), ("Jun", 200),
("Jul", 200), ("Aug", 200), ("Sep", 50),
("Oct", 50), ("Nov", 10), ("Dec", 5)
]
radar.add_schema(schema)
radar.add("执行状态", radar_data1)
radar.render("/aa.html")
def drawRadar(arr, value, valuemax, name):
radar = Radar(init_opts=opts.InitOpts(
width="1280px", height="720px", bg_color="#CCCCCC"))
v_max = [list(z) for z in zip(arr, valuemax)]
radar.add_schema(
schema=[opts.RadarIndicatorItem(name=k, max_=v) for k, v in v_max],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
radar.add(
series_name=name,
data=value,
linestyle_opts=opts.LineStyleOpts(color="#CD0000"),
)
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.set_global_opts(title_opts=opts.TitleOpts(title=name),
legend_opts=opts.LegendOpts())
radar.render("{0}.html".format(name))
def DrawRadar(title, data, savepath):
print('正在分析评分构成...')
radar = Radar()
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.set_global_opts(
title_opts=opts.TitleOpts(title="Radar-评分分布统计雷达图", pos_left='center'),
legend_opts=opts.LegendOpts(orient="vertical",
pos_top="middle",
pos_left="2%"),
)
values = [[data[i][1] for i in range(len(data))]] # 把整个列表当做一个要画的东西
attrs = [data[i][0] for i in range(len(data))] # 改名字
attrs = list(map(int, attrs))
attrs = [str(i) + '星' for i in attrs]
num = len(attrs) # 雷达图有几项
schema = [{"name": attrs[i], "max": 230} for i in (range(num))]
radar.add_schema(schema=schema)
radar.add('豆瓣评分构成', values)
radar.render(os.path.join(savepath, '%s.html' % title))
def plot_radar(series_radiant, series_dire):
c_schema = [{
"name": "控制",
"max": 15,
"min": 0
}, {
"name": "核心",
"max": 15,
"min": 0
}, {
"name": "辅助",
"max": 15,
"min": 0
}, {
"name": "逃生",
"max": 15,
"min": 0
}, {
"name": "爆发",
"max": 15,
"min": 0
}, {
"name": "先手",
"max": 15,
"min": 0
}, {
"name": "耐久",
"max": 15,
"min": 0
}, {
"name": "推进",
"max": 15,
"min": 0
}]
radar = Radar()
radar.add_schema(schema=c_schema)
radar.add("radiant", series_radiant, color="#f9713c")
radar.add("dire", series_dire, color="#b3e4a1")
radar.render("Rader.html")
def draw_radar_for_observation_result():
value = [[43, 58, 78, 65, 86, 63]]
min_val = 0
max_val = 100
# 用于调整雷达各维度的范围大小
c_schema = [{"name": "交往能力", "max": max_val, "min": min_val, },
{"name": "感知能力", "max": max_val, "min": min_val, "color": "#ff0000"},
{"name": "运动能力", "max": max_val, "min": min_val, "color": "#000000"},
{"name": "注意力", "max": max_val, "min": min_val, "color": "#00ffff"},
{"name": "语言能力", "max": max_val, "min": min_val, "color": "#ff00ff"},
{"name": "自我照顾能力", "max": max_val, "min": min_val, "color": "#ff00ff"}
]
# 画图
radar = Radar()
radar.add_schema(c_schema, textstyle_opts=opts.TextStyleOpts(color="#3344ff"))
radar.add(None, value, color="#f9713c",
symbol='circle', label_opts=opts.LabelOpts(is_show=True),
areastyle_opts=opts.AreaStyleOpts(opacity=0.5, color="#f9713c"))
radar.set_global_opts(title_opts=opts.TitleOpts(title="Radar-基本示例"))
radar.render("observation_result.html")
week = date_to_week(data['入网时间'])
week1 = [week]
radar = Radar()
schema = [{
"name": '星期天',
"max": 1000
}, {
"name": '星期一',
"max": 1000
}, {
"name": '星期二',
"max": 1000
}, {
"name": '星期三',
"max": 1000
}, {
"name": '星期四',
"max": 1000
}, {
"name": '星期五',
"max": 1000
}, {
"name": '星期六',
"max": 1000
}]
radar.add(data=week1, series_name='入网时间')
radar.add_schema(schema)
radar.render('render.html')
def score_analyse_radar(chengji, stu, subject):
score = pd.read_csv(chengji)
radar = Radar()
radar.render()
pass
[3300, 13000, 25000, 30000, 48000, 24000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.add_schema(schema=[
opts.RadarIndicatorItem(name='销售', max_=6500),
opts.RadarIndicatorItem(name='管理', max_=16000),
opts.RadarIndicatorItem(name='信息技术', max_=30000),
opts.RadarIndicatorItem(name='客服', max_=38000),
opts.RadarIndicatorItem(name='研发', max_=52000),
opts.RadarIndicatorItem(name='市场', max_=25000),
])
radar.add('预算分配', v1)
radar.add('实际开销', v2)
radar.set_global_opts(title_opts=opts.TitleOpts(title='Radar Sample'))
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
radar.render('pyecharts-radar.html')
# 词云图
words = [
('Sam S Club', 10000),
('Macys', 6181),
('Amy Schumer', 4386),
('Jurassic World', 4055),
('Charter Communications', 2467),
]
wordcloud = WordCloud()
wordcloud.add('', words, word_size_range=[20, 100])
wordcloud.set_global_opts(title_opts=opts.TitleOpts(title='WordCloud Sample'))
wordcloud.render('pyecharts-wordcloud.html')
# 更多图表 https://pyecharts.org/#/zh-cn/basic_charts http://gallery.pyecharts.org/#/Boxplot/boxplot_light_velocity
def Cartography():
#创建地图
map = Map( init_opts=opts.InitOpts(width="1900px", height="900px", bg_color="#d0effa", page_title="全球新冠疫情_1"))
map.add("确诊人数",[list(z) for z in zip(names_new, confirm)],is_map_symbol_show=False,
maptype="world",label_opts=opts.LabelOpts(is_show=False),itemstyle_opts=opts.ItemStyleOpts(color="rgb(98,121,146)"))#地图区域颜色
map.set_global_opts(title_opts = opts.TitleOpts(title='全球新冠疫情确诊人数'),legend_opts=opts.LegendOpts(is_show=False),
visualmap_opts=opts.VisualMapOpts(max_=2000000, is_piecewise=True,
pieces=[
{"max": 3000000,"min": 500001,"label":">500000","color":"#460303"},
{"max": 500000, "min": 100001, "label": "100001-500000", "color": "#8A0808"},
{"max": 100000, "min": 10001, "label": "10001-100000", "color": "#B40404"},
{"max": 10000, "min": 1001, "label": "1001-10000", "color": "#DF0101"},
{"max": 1000, "min": 101, "label": "101-1000", "color": "#F78181"},
{"max": 100, "min": 1, "label": "1-100", "color": "#F5A9A9"},
{"max": 0, "min": 0, "label": "0", "color": "#fababa"},
])
)
map.render('Global_new_crown_epidemic_map.html')
#创建饼图
pie = Pie(init_opts=opts.InitOpts(width='1900px', height='900px',page_title="全球新冠疫情_2",bg_color="#fee4e7"))
# 添加数据
pie.add("", [list(z) for z in zip(country_list, dead_list)],
radius=['20%', '100%'],#设置内径外径
center=['60%', '65%'],
rosetype='area')#圆心角相同，通过半径展现数据大小#rosetype='radius'圆心角展现数据百分比，半径展现数据大小
# 设置全局配置
pie.set_global_opts(title_opts=opts.TitleOpts(title='全球新冠疫情',subtitle='死亡人数超过\n5000的国家\n (除中国)',
title_textstyle_opts=opts.TextStyleOpts(font_size=15,color= '#f40909'),
subtitle_textstyle_opts= opts.TextStyleOpts(font_size=15,color= '#8a0b0b'),
pos_right= 'center',pos_left= '57%',pos_top= '60%',pos_bottom='center'),
legend_opts=opts.LegendOpts(is_show=False))
# 设置系列配置和颜色
pie.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='inside', font_size=13,
formatter='{b}：{c}', font_style='italic',
font_family='Microsoft YaHei'))
pie.set_colors(color_matching)
pie.render('Global_new_crown_epidemic_Rose.html')
#创建雷达图
radar = Radar(init_opts=opts.InitOpts(width='1900px',height='900px',page_title="全球新冠疫情_3",bg_color="#d1eff3"))
#由于雷达图传入的数据得为多维数据，所以这里需要做一下处理
radar_data1 = [list(dead_list)]
radar_data2 = [list(heal_list)]
radar.add_schema(
schema=[
opts.RadarIndicatorItem(name='巴西', max_=8000),
opts.RadarIndicatorItem(name='荷兰', max_=8000),
opts.RadarIndicatorItem(name='伊朗', max_=20000),
opts.RadarIndicatorItem(name='德国', max_=40000),
opts.RadarIndicatorItem(name='比利时', max_=70000),
opts.RadarIndicatorItem(name='英国', max_=80000),
opts.RadarIndicatorItem(name='法国 ', max_=110000),
opts.RadarIndicatorItem(name='西班牙', max_=150000),
opts.RadarIndicatorItem(name='意大利', max_=170000),
opts.RadarIndicatorItem(name='美国', max_=220000),
]
)
radar.add("死亡人数",radar_data1,color='blue',areastyle_opts = opts.AreaStyleOpts(opacity = 0.2,color='blue'))
radar.add("治愈人数",radar_data2,color='red',areastyle_opts=opts.AreaStyleOpts(opacity=0.3,color='red'))
radar.set_series_opts(label_opts=opts.LabelOpts(is_show=True))
radar.set_global_opts(title_opts=opts.TitleOpts(title="死亡人数与治愈人数对比"))
radar.render("Death_Versus_Heal.html")
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")
def draw_radar(season_x, season_name, filechart, seasonareacolor, seasonlinecolor):
# 建立schema的json文件 season_x.columns = [season_name]
season_index = season_x.index.to_list()
# print(season_index)
# n_max, n_min = max(season_x), min(season_x)
n_max = max(enumerate(season_x[0]), key=operator.itemgetter(1))[1]
n_min = min(enumerate(season_x[0]), key=operator.itemgetter(1))[1]
# 用于获取对象的哪些维的数据
# print(n_max, n_min)
df_season = pd.DataFrame({'name': season_index})
df_season['max'], df_season['min'] = n_max, n_min
# season_js = df_season.to_json(orient='records')
# print(season_js)
# 设置16方位, 逆时针排序
season_js = [
{"name": "N", "max": n_max, "min": n_min},
{"name": "NNW", "max": n_max, "min": n_min},
{"name": "NW", "max": n_max, "min": n_min},
{"name": "WNW", "max": n_max, "min": n_min},
{"name": "W", "max": n_max, "min": n_min},
{"name": "WSW", "max": n_max, "min": n_min},
{"name": "SW", "max": n_max, "min": n_min},
{"name": "SSW", "max": n_max, "min": n_min},
{"name": "S", "max": n_max, "min": n_min},
{"name": "SSE", "max": n_max, "min": n_min},
{"name": "SE", "max": n_max, "min": n_min},
{"name": "ESE", "max": n_max, "min": n_min},
{"name": "E", "max": n_max, "min": n_min},
{"name": "ENE", "max": n_max, "min": n_min},
{"name": "NE", "max": n_max, "min": n_min},
{"name": "NNE", "max": n_max, "min": n_min},
]
# 设置数据
fengsus = season_x[0].to_list()
data_fengsu = [{'value': fengsus, 'name': '风频数'}]
# print(data_fengsu)
charts = Radar()
charts.set_colors(['#4587E7']) # 设置颜色
charts.add_schema(schema=season_js,
shape='circle',
center=['50%', '50%'],
radius='80%',
angleaxis_opts=opts.AngleAxisOpts(
min_=0, # 坐标轴刻度最小值
max_=360, # 坐标轴刻度最大值
is_clockwise=True,
interval=22.5, # 强制设置坐标轴分割间隔
axistick_opts=opts.AxisTickOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False,), # 坐标轴线标签配置项
axisline_opts=opts.AxisLineOpts(is_show=True), # 坐标轴线风格配置项
splitline_opts=opts.SplitLineOpts(is_show=True) # 分割线配置项
),
radiusaxis_opts=opts.RadiusAxisOpts(
min_=n_min, # 坐标轴刻度最小值
max_=n_max, # 坐标轴刻度最大值
interval=2, # 强制设置坐标轴分割间隔
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=0.2,
)
),
splitline_opts=opts.SplitLineOpts(is_show=True,
linestyle_opts=opts.LineStyleOpts(is_show=True,
width=0.5,
color='grey')),
axislabel_opts=opts.LabelOpts(is_show=True,
font_size=12,
color='grey'), # 坐标轴线标签配置项
),
polar_opts=opts.PolarOpts(),
splitarea_opt=opts.SplitAreaOpts(is_show=True),
splitline_opt=opts.SplitLineOpts(is_show=False), # 分割线配置项
textstyle_opts=opts.TextStyleOpts(color='black',
font_size=14)
)
charts.add(series_name='%s玫瑰图' % season_name, # 系列名称
data=data_fengsu, # 系列数据
areastyle_opts=opts.AreaStyleOpts(opacity=0.5, # 系列面样式设置
color=seasonareacolor),
linestyle_opts=opts.LineStyleOpts(width=2, # 系列线样式设置
color=seasonlinecolor),
label_opts=opts.LabelOpts(is_show=False),
) # 系列标签设置
charts.render(filechart)