def visual(self, array):
schema = [('得分', 35), ('篮板', 20), ('助攻', 15), ('抢断', 5), ('盖帽', 5)]
radar = Radar()
radar.config(schema)
for player, data in array.items():
radar.add(player, data, is_splitline=True, is_axisline_show=True)
radar.render('player.html')
def drawRadar(title, data, savepath='./results'):
radar = Radar(title)
values = [[data[i][1] for i in range(len(data))]]
schema = [(data[i][0], 500) for i in range(len(data))]
radar.config(schema)
radar.add('', values, is_splitline=True, is_axisline_show=True)
radar.render(os.path.join(savepath, '%s.html' % title))
def picture_radar(file_path):
"""
:param file_path:文件路径：str
"""
birth_hcs = pd.read_csv(file_path)
# 使用loc 进行切片， 行：全选 列：选择不连续的用列表包含列名
birth_nien_hcs = birth_hcs.loc[:, [
'INFANT_ALIVE_AT_REPORT', 'MOTHER_AGE_YEARS', 'CIG_1_TRI',
'MOTHER_PRE_WEIGHT', 'OBSTETRIC_GESTATION_WEEKS', 'INFANT_WEIGHT_GRAMS'
]]
birth_nien_hcs = nien_to_nan(birth_nien_hcs, [
'MOTHER_AGE_YEARS', 'CIG_1_TRI', 'MOTHER_PRE_WEIGHT',
'OBSTETRIC_GESTATION_WEEKS', 'INFANT_WEIGHT_GRAMS'
])
# birth_nien['INFANT_ALIVE_AT_REPORT'] == 'Y' 判断row的值：等于则为True 再使用DataFrame取为True的行
y_hcs = birth_nien_hcs[birth_nien_hcs['INFANT_ALIVE_AT_REPORT'] == 'Y']
n_hcs = birth_nien_hcs[birth_nien_hcs['INFANT_ALIVE_AT_REPORT'] == 'N']
# 按列求平均值，跳过nan，
# round(decimals=2)： 四舍五入取两位小数
y_mean_hcs = list(y_hcs.mean(skipna=True, axis=0).round(decimals=2))
n_mean_hcs = list(n_hcs.mean(skipna=True, axis=0).round(decimals=2))
print('存活婴儿数据雷达图:', y_mean_hcs, '\n', '死亡婴儿数据雷达图:', n_mean_hcs)
schema_hcs = [{
'name': "母亲平均年龄",
'max': 40
}, {
'name': "母亲平均吸烟数量",
'max': 1.2
}, {
'name': "母亲平均体重",
'max': 200
}, {
'name': "怀孕平均周数",
'max': 40
}, {
'name': "婴儿平均体重",
'max': 3500
}]
radar_hcs = Radar('存活婴儿与死亡婴儿数据对比——雷达图(黄彩思)', height=500)
radar_hcs.set_radar_component(c_schema=schema_hcs)
radar_hcs.add('存活婴儿', [y_mean_hcs],
item_color='#2525f5',
symbol=None,
area_color='#2525f5',
area_opacity=0.3,
legend_top='bottom',
line_width=3)
radar_hcs.add('死亡婴儿', [n_mean_hcs],
item_color="#f9713c",
symbol=None,
area_color="#ea3a2e",
area_opacity=0.3,
legend_top='bottom',
line_width=3,
legend_text_size=20)
radar_hcs.render('radar.html')
def leidatu():
'''雷达图'''
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000), ("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False)
radar.render('./info/雷达图.html')
def drawRadar(title, data, savepath='./results'):
if not os.path.exists(savepath):
os.mkdir(savepath)
radar = Radar(title, title_pos='center')
radar.use_theme('essos')
values = [j for i, j in data.items()]
sum_ = sum(values) / (len(values) // 2)
schema = [(i, sum_) for i, j in data.items()]
values = [values]
radar.config(schema)
radar.add("", values, is_splitline=True, is_axisline_show=True, radar_text_size=20)
radar.render(os.path.join(savepath, '%s.html' % title))
def test_radar_default_schema():
schema = [
("销售", 6500), ("管理", 16000), ("信息技术", 30000),
("客服", 38000), ("研发", 52000), ("市场", 25000)
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False,
legend_selectedmode='single')
radar.render()
def test_radar_default_schema():
schema = [
("销售", 6500), ("管理", 16000), ("信息技术", 30000),
("客服", 38000), ("研发", 52000), ("市场", 25000)
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False,
legend_selectedmode='single')
radar.render()
def __draw_radar_core__(city, data, color):
c_schema = [{
"name": "PM2.5\nmax=500μg/m3",
"max": 500,
"min": 0
}, {
"name": "PM10\nmax=1500μg/m3",
"max": 1500,
"min": 0
}, {
"name": "SO2\nmax=100μg/m3",
"max": 100
}, {
"name": "NO2\nmax=200μg/m3",
"max": 200
}, {
"name": "CO\nmax=5.0mg/m3",
"max": 5
}, {
"name": "O3\nmax=200μg/m3",
"max": 200,
"min": 0
}]
radar = Radar(background_color='#333')
radar.config(
c_schema=c_schema,
shape='polygon',
radar_text_color="#FFF",
)
# radar.use_theme("dark")
radar.add(
city,
data,
is_area_show=True,
area_color=color, #'#F0891F',
area_opacity=0.3,
line_curve=0.1,
line_opacity=0,
symbol=None,
legend_text_color='#FFF',
# legend_orient='vertical',
legend_top='4%',
legend_pos='25%',
legend_text_size=15,
legend_selectedmode=False,
label_color=[color] #,'#3F4A97','#F05F8D','#6A9EA3']
)
radar.render()
radar.render(city + '雷达图.jpeg')
def scores_of_bread(data):
star = data['Star'].mean()
taste = data['Taste'].mean()
environ = data['Environment'].mean()
service = data['Service'].mean()
ave = (taste + environ + service) / 3
result_show = [star, taste, environ, service, ave]
config_info = [('星级', 50), ('口味', 10), ('环境', 10), ('服务', 10), ('综合', 10)]
scores_fig1 = Radar('综合评分情况')
scores_fig1.config(config_info)
scores_fig1.add('', [result_show])
scores_fig1.render(os.path.join(data_out, 'scores1_of_bread.html'))
result = pd.DataFrame(columns=data.columns)
for index, item in enumerate(data['Taste']):
if item == data['Taste'].max():
# print('口味最好：\n', data.loc[index])
# print(data.loc[index].info())
result = result.append(data.loc[index])
for index, item in enumerate(data['Environment']):
if item == data['Environment'].max():
# print('环境最佳：\n', data.loc[index])
result = result.append(data.loc[index])
for index, item in enumerate(data['Service']):
if item == data['Service'].max():
# print('服务最棒：\n', data.loc[index])
result = result.append(data.loc[index])
result.to_csv(os.path.join(data_out, 'result_of_best.csv'),
encoding='utf-8-sig')
print(
'最昂贵的菜系：%s' %
data.groupby('Cuisine')['Per_Consumption'].mean().idxmax(),
'人均：%.2f元' % data.groupby('Cuisine')['Per_Consumption'].mean().max())
print('最受欢迎的菜系：%s' % data.groupby('Cuisine')['Comments'].mean().idxmax(),
'平均有：%d条评论' % data.groupby('Cuisine')['Comments'].mean().max())
data['ave'] = round(
(data['Taste'] + data['Environment'] + data['Service']) / 3, 2)
scores_fig2 = Scatter('综合评分与餐厅星级关系')
scores_fig2.add('综合评分', data['Star'].values, data['ave'].values)
scores_fig2.render(os.path.join(data_out, 'scores2_of_bread.html'))
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(self):
# 雷达图
from pyecharts import Radar
radar = Radar('雷达图', subtitle='一年的降水量与蒸发量')
# 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 = [[
2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3
]]
radar_data2 = [[
2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3
]]
# 设置column的最大值，为了雷达图更为直观，这里的月份最大值设置有所不同
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)]
# 传入坐标
radar.config(schema)
radar.add("降水量", radar_data1)
# 一般默认为同一种颜色，这里为了便于区分，需要设置item的颜色
radar.add("蒸发量", radar_data2, item_color="#1C86EE")
radar.render('radar.html')
from pyecharts import Radar
schema = [ ("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000), ("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配",v1,is_splitline_show=True,is_axisline_show=True)
radar.add("实际开销",v2,label_color=["#4e79a7"],is_area_show=False)
radar.show_config()
radar.render(r"Radar（雷达图-技能图）.html")
#-----------------------------------------------------------------------------
# radar plot
from pyecharts import Radar
schema = [
("销售", 6500), ("管理", 16000), ("信息技术", 30000),
("客服", 38000), ("研发", 52000), ("市场", 25000)
]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True, symbol=None,
area_color=RGB(255, 120, 0), item_color=RGB(255, 120, 0), is_area_show=True, area_opacity=0.5)
radar.add("实际开销", v2, symbol=None,
area_color=RGB(50, 50, 0), item_color=RGB(50, 50, 0), is_area_show=True, area_opacity=0.5)
radar.render()
#------------------------------------------------------------------------------
# Funnel
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
value = [1, 11, 15, 30, 45, 101]
funnel = Funnel("漏斗图示例")
funnel.add("商品", attr, value, is_label_show=True,
label_pos="inside", label_text_color="#fff")
funnel.render('funnel.html')
# ----------------------------------------------------------------------------
# Gauge
from pyecharts import Radar
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000),
("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销",
v2,
label_color=["#4e79a7"],
is_area_show=False,
legend_selectedmode='single')
radar.render("./img/雷达图.html")
def draw_age_radar(data):
radar = Radar("妹子年龄分布雷达图")
radar.config(age_interval)
radar.add("年龄段", data, is_area_show=False, legend_selectedmode='single')
radar.render()
def test_radar():
# radar_0
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000),
("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False)
radar.show_config()
radar.render()
# radar_1
value_bj = [[55, 9, 56, 0.46, 18, 6, 1], [25, 11, 21, 0.65, 34, 9, 2],
[56, 7, 63, 0.3, 14, 5, 3], [33, 7, 29, 0.33, 16, 6, 4],
[42, 24, 44, 0.76, 40, 16, 5], [82, 58, 90, 1.77, 68, 33, 6],
[74, 49, 77, 1.46, 48, 27, 7], [78, 55, 80, 1.29, 59, 29, 8],
[267, 216, 280, 4.8, 108, 64, 9],
[185, 127, 216, 2.52, 61, 27, 10],
[39, 19, 38, 0.57, 31, 15, 11], [41, 11, 40, 0.43, 21, 7, 12],
[64, 38, 74, 1.04, 46, 22, 13],
[108, 79, 120, 1.7, 75, 41, 14],
[108, 63, 116, 1.48, 44, 26, 15], [33, 6, 29, 0.34, 13, 5, 16],
[94, 66, 110, 1.54, 62, 31, 17],
[186, 142, 192, 3.88, 93, 79, 18],
[57, 31, 54, 0.96, 32, 14, 19], [22, 8, 17, 0.48, 23, 10, 20],
[39, 15, 36, 0.61, 29, 13,
21], [94, 69, 114, 2.08, 73, 39, 22],
[99, 73, 110, 2.43, 76, 48, 23], [31, 12, 30, 0.5, 32, 16, 24],
[42, 27, 43, 1, 53, 22, 25], [154, 117, 157, 3.05, 92, 58, 26],
[234, 185, 230, 4.09, 123, 69, 27],
[160, 120, 186, 2.77, 91, 50, 28],
[134, 96, 165, 2.76, 83, 41,
29], [52, 24, 60, 1.03, 50, 21, 30],
[46, 5, 49, 0.28, 10, 6, 31]]
value_sh = [[91, 45, 125, 0.82, 34, 23, 1], [65, 27, 78, 0.86, 45, 29, 2],
[83, 60, 84, 1.09, 73, 27, 3], [109, 81, 121, 1.28, 68, 51, 4],
[106, 77, 114, 1.07, 55, 51,
5], [109, 81, 121, 1.28, 68, 51, 6],
[106, 77, 114, 1.07, 55, 51, 7], [89, 65, 78, 0.86, 51, 26, 8],
[53, 33, 47, 0.64, 50, 17, 9], [80, 55, 80, 1.01, 75, 24, 10],
[117, 81, 124, 1.03, 45, 24,
11], [99, 71, 142, 1.1, 62, 42, 12],
[95, 69, 130, 1.28, 74, 50, 13],
[116, 87, 131, 1.47, 84, 40, 14],
[108, 80, 121, 1.3, 85, 37, 15],
[134, 83, 167, 1.16, 57, 43, 16],
[79, 43, 107, 1.05, 59, 37,
17], [71, 46, 89, 0.86, 64, 25, 18],
[97, 71, 113, 1.17, 88, 31, 19],
[84, 57, 91, 0.85, 55, 31, 20], [87, 63, 101, 0.9, 56, 41, 21],
[104, 77, 119, 1.09, 73, 48, 22], [87, 62, 100, 1, 72, 28, 23],
[168, 128, 172, 1.49, 97, 56, 24],
[65, 45, 51, 0.74, 39, 17, 25], [39, 24, 38, 0.61, 47, 17, 26],
[39, 24, 39, 0.59, 50, 19, 27], [93, 68, 96, 1.05, 79, 29, 28],
[188, 143, 197, 1.66, 99, 51, 29],
[174, 131, 174, 1.55, 108, 50, 30],
[187, 143, 201, 1.39, 89, 53, 31]]
c_schema = [{
"name": "AQI",
"max": 300,
"min": 5
}, {
"name": "PM2.5",
"max": 250,
"min": 20
}, {
"name": "PM10",
"max": 300,
"min": 5
}, {
"name": "CO",
"max": 5
}, {
"name": "NO2",
"max": 200
}, {
"name": "SO2",
"max": 100
}]
radar = Radar("雷达图示例")
radar.config(c_schema=c_schema, shape='circle')
radar.add("北京", value_bj, item_color="#f9713c", symbol=None)
radar.add("上海", value_sh, item_color="#b3e4a1", symbol=None)
radar.show_config()
radar.render()
from pyecharts import Radar
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000),
("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema, shape="circle")
radar.add("预算分配",
v1,
is_splitline_show=True,
is_axisline_show=True,
is_stack=True)
radar.add("实际开销", v2, label_color=["#23e"], is_area_show=False, is_stack=True)
# radar.add("实际开销",v2,label_color=["#23e"],is_area_show=False,
# legend_selectedmode='single',is_stack=True)
radar.render(path="Radar.html")
"#4e79a7",
"#c23531",
"#2f4554",
"#61a0a8",
"#d48265",
"#749f83",
"#ca8622",
"#bda29a",
"#6e7074",
"#546570",
"#c4ccd3",
"#f05b72",
"#ef5b9c",
"#f47920",
"#905a3d",
"#fab27b",
"#2a5caa",
"#444693",
"#726930",
"#b2d235",
"#6d8346",
"#ac6767",
"#1d953f",
"#6950a1",
"#918597",
"#f6f5ec"
]
}
>>> radar.render()
>>>
#encoding:utf-8
# 作者：孙亚楠
# 日期：2020/3/21 0021 18:39
# 工具：PyCharm
# Python版本：3.7.3
#此python文件完成功能：
from pyecharts import Radar
radar = Radar("雷达图", "一年的降水量与蒸发量")
# //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 = [[
2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3
]]
radar_data2 = [[
2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3
]]
# //设置column的最大值，为了雷达图更为直观，这里的月份最大值设置有所不同
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)]
# //传入坐标
radar.config(schema)
radar.add("降水量", radar_data1)
# //一般默认为同一种颜色，这里为了便于区分，需要设置item的颜色
radar.add("蒸发量", radar_data2, item_color="#1C86EE")
radar.render('雷达图.html')
type='barRadius',
is_stack=True)
polar.show_config()
polar.render(path='./data/03-04极坐标.html')
# 雷达图
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000),
("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False)
radar.show_config()
radar.render(path='./data/03-05雷达图.html')
"""
#支持保持成各种格式,但是会有问题
bar = Bar("我的第一个图表", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], [5, 20, 36, 10, 75, 90])
# bar.print_echarts_options()
bar.render(path='snapshot.html')
bar.render(path='snapshot.png')
bar.render(path='snapshot.pdf')
"""
name = [
'Sam S Club', 'Macys', 'Amy Schumer', 'Jurassic World',
'Charter Communications', 'Chick Fil A', 'Planet Fitness', 'Pitch Perfect',
'Express', 'Home', 'Johnny Depp', 'Lena Dunham', 'Lewis Hamilton', 'KXAN',
'Mary Ellen Mark', 'Farrah Abraham', 'Rita Ora', 'Serena Williams',
def test_radar():
# radar_0
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000), ("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar("雷达图示例")
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False, legend_selectedmode='single')
radar.show_config()
radar.render()
# radar_1
value_bj = [
[55, 9, 56, 0.46, 18, 6, 1],
[25, 11, 21, 0.65, 34, 9, 2],
[56, 7, 63, 0.3, 14, 5, 3],
[33, 7, 29, 0.33, 16, 6, 4],
[42, 24, 44, 0.76, 40, 16, 5],
[82, 58, 90, 1.77, 68, 33, 6],
[74, 49, 77, 1.46, 48, 27, 7],
[78, 55, 80, 1.29, 59, 29, 8],
[267, 216, 280, 4.8, 108, 64, 9],
[185, 127, 216, 2.52, 61, 27, 10],
[39, 19, 38, 0.57, 31, 15, 11],
[41, 11, 40, 0.43, 21, 7, 12],
[64, 38, 74, 1.04, 46, 22, 13],
[108, 79, 120, 1.7, 75, 41, 14],
[108, 63, 116, 1.48, 44, 26, 15],
[33, 6, 29, 0.34, 13, 5, 16],
[94, 66, 110, 1.54, 62, 31, 17],
[186, 142, 192, 3.88, 93, 79, 18],
[57, 31, 54, 0.96, 32, 14, 19],
[22, 8, 17, 0.48, 23, 10, 20],
[39, 15, 36, 0.61, 29, 13, 21],
[94, 69, 114, 2.08, 73, 39, 22],
[99, 73, 110, 2.43, 76, 48, 23],
[31, 12, 30, 0.5, 32, 16, 24],
[42, 27, 43, 1, 53, 22, 25],
[154, 117, 157, 3.05, 92, 58, 26],
[234, 185, 230, 4.09, 123, 69, 27],
[160, 120, 186, 2.77, 91, 50, 28],
[134, 96, 165, 2.76, 83, 41, 29],
[52, 24, 60, 1.03, 50, 21, 30],
[46, 5, 49, 0.28, 10, 6, 31]
]
value_sh = [
[91, 45, 125, 0.82, 34, 23, 1],
[65, 27, 78, 0.86, 45, 29, 2],
[83, 60, 84, 1.09, 73, 27, 3],
[109, 81, 121, 1.28, 68, 51, 4],
[106, 77, 114, 1.07, 55, 51, 5],
[109, 81, 121, 1.28, 68, 51, 6],
[106, 77, 114, 1.07, 55, 51, 7],
[89, 65, 78, 0.86, 51, 26, 8],
[53, 33, 47, 0.64, 50, 17, 9],
[80, 55, 80, 1.01, 75, 24, 10],
[117, 81, 124, 1.03, 45, 24, 11],
[99, 71, 142, 1.1, 62, 42, 12],
[95, 69, 130, 1.28, 74, 50, 13],
[116, 87, 131, 1.47, 84, 40, 14],
[108, 80, 121, 1.3, 85, 37, 15],
[134, 83, 167, 1.16, 57, 43, 16],
[79, 43, 107, 1.05, 59, 37, 17],
[71, 46, 89, 0.86, 64, 25, 18],
[97, 71, 113, 1.17, 88, 31, 19],
[84, 57, 91, 0.85, 55, 31, 20],
[87, 63, 101, 0.9, 56, 41, 21],
[104, 77, 119, 1.09, 73, 48, 22],
[87, 62, 100, 1, 72, 28, 23],
[168, 128, 172, 1.49, 97, 56, 24],
[65, 45, 51, 0.74, 39, 17, 25],
[39, 24, 38, 0.61, 47, 17, 26],
[39, 24, 39, 0.59, 50, 19, 27],
[93, 68, 96, 1.05, 79, 29, 28],
[188, 143, 197, 1.66, 99, 51, 29],
[174, 131, 174, 1.55, 108, 50, 30],
[187, 143, 201, 1.39, 89, 53, 31]
]
c_schema = [{"name":"AQI", "max": 300, "min": 5},
{"name":"PM2.5", "max": 250, "min": 20},
{"name":"PM10", "max":300, "min": 5},
{"name":"CO", "max":5},
{"name":"NO2", "max":200},
{"name":"SO2", "max":100}]
radar = Radar("雷达图示例")
radar.config(c_schema=c_schema, shape='circle')
radar.add("北京", value_bj, item_color="#f9713c", symbol=None)
radar.add("上海", value_sh, item_color="#b3e4a1", symbol=None, legend_selectedmode='signle')
radar.show_config()
radar.render()
# radar_2
radar = Radar("雷达图示例")
radar.config(c_schema=c_schema, shape='circle')
radar.add("北京", value_bj, item_color="#f9713c", symbol=None)
radar.add("上海", value_sh, item_color="#b3e4a1", symbol=None)
radar.show_config()
radar.render()
def test_radar_user_define_schema():
value_bj = [
[55, 9, 56, 0.46, 18, 6, 1],
[25, 11, 21, 0.65, 34, 9, 2],
[56, 7, 63, 0.3, 14, 5, 3],
[33, 7, 29, 0.33, 16, 6, 4],
[42, 24, 44, 0.76, 40, 16, 5],
[82, 58, 90, 1.77, 68, 33, 6],
[74, 49, 77, 1.46, 48, 27, 7],
[78, 55, 80, 1.29, 59, 29, 8],
[267, 216, 280, 4.8, 108, 64, 9],
[185, 127, 216, 2.52, 61, 27, 10],
[39, 19, 38, 0.57, 31, 15, 11],
[41, 11, 40, 0.43, 21, 7, 12],
[64, 38, 74, 1.04, 46, 22, 13],
[108, 79, 120, 1.7, 75, 41, 14],
[108, 63, 116, 1.48, 44, 26, 15],
[33, 6, 29, 0.34, 13, 5, 16],
[94, 66, 110, 1.54, 62, 31, 17],
[186, 142, 192, 3.88, 93, 79, 18],
[57, 31, 54, 0.96, 32, 14, 19],
[22, 8, 17, 0.48, 23, 10, 20],
[39, 15, 36, 0.61, 29, 13, 21],
[94, 69, 114, 2.08, 73, 39, 22],
[99, 73, 110, 2.43, 76, 48, 23],
[31, 12, 30, 0.5, 32, 16, 24],
[42, 27, 43, 1, 53, 22, 25],
[154, 117, 157, 3.05, 92, 58, 26],
[234, 185, 230, 4.09, 123, 69, 27],
[160, 120, 186, 2.77, 91, 50, 28],
[134, 96, 165, 2.76, 83, 41, 29],
[52, 24, 60, 1.03, 50, 21, 30],
[46, 5, 49, 0.28, 10, 6, 31]
]
value_sh = [
[91, 45, 125, 0.82, 34, 23, 1],
[65, 27, 78, 0.86, 45, 29, 2],
[83, 60, 84, 1.09, 73, 27, 3],
[109, 81, 121, 1.28, 68, 51, 4],
[106, 77, 114, 1.07, 55, 51, 5],
[109, 81, 121, 1.28, 68, 51, 6],
[106, 77, 114, 1.07, 55, 51, 7],
[89, 65, 78, 0.86, 51, 26, 8],
[53, 33, 47, 0.64, 50, 17, 9],
[80, 55, 80, 1.01, 75, 24, 10],
[117, 81, 124, 1.03, 45, 24, 11],
[99, 71, 142, 1.1, 62, 42, 12],
[95, 69, 130, 1.28, 74, 50, 13],
[116, 87, 131, 1.47, 84, 40, 14],
[108, 80, 121, 1.3, 85, 37, 15],
[134, 83, 167, 1.16, 57, 43, 16],
[79, 43, 107, 1.05, 59, 37, 17],
[71, 46, 89, 0.86, 64, 25, 18],
[97, 71, 113, 1.17, 88, 31, 19],
[84, 57, 91, 0.85, 55, 31, 20],
[87, 63, 101, 0.9, 56, 41, 21],
[104, 77, 119, 1.09, 73, 48, 22],
[87, 62, 100, 1, 72, 28, 23],
[168, 128, 172, 1.49, 97, 56, 24],
[65, 45, 51, 0.74, 39, 17, 25],
[39, 24, 38, 0.61, 47, 17, 26],
[39, 24, 39, 0.59, 50, 19, 27],
[93, 68, 96, 1.05, 79, 29, 28],
[188, 143, 197, 1.66, 99, 51, 29],
[174, 131, 174, 1.55, 108, 50, 30],
[187, 143, 201, 1.39, 89, 53, 31]
]
c_schema = [
{"name": "AQI", "max": 300, "min": 5},
{"name": "PM2.5", "max": 250, "min": 20},
{"name": "PM10", "max": 300, "min": 5},
{"name": "CO", "max": 5},
{"name": "NO2", "max": 200},
{"name": "SO2", "max": 100}
]
# legend selected mode 'single'
radar = Radar("雷达图示例")
radar.config(c_schema=c_schema, shape='circle')
radar.add("北京", value_bj, item_color="#f9713c", symbol=None)
radar.add("上海", value_sh, item_color="#b3e4a1", symbol=None,
legend_selectedmode='single')
assert 'single' in radar._repr_html_()
assert 'multiple' not in radar._repr_html_()
radar.render()
# legend selected mode 'single'
radar = Radar("雷达图示例")
radar.config(c_schema=c_schema, shape='circle')
radar.add("北京", value_bj, item_color="#f9713c", symbol=None)
radar.add("上海", value_sh, item_color="#b3e4a1", symbol=None)
assert 'multiple' in radar._repr_html_()
assert 'single' not in radar._repr_html_()
radar.render()
"min": 0
}, {
"name": "价格",
"max": 20000,
"min": 0
}]
radar = Radar()
radar.config(c_schema=indicator)
radar.add("HuaWei mate 40 pro",
value_mate40,
item_color="#f9713c",
symbol=None,
area_color="#ea3a2e",
area_opacity=0.3,
legend_top='bottom',
line_width=3)
radar.add("Apple iphone 12 pro",
value_iphone,
item_color='#2525f5',
symbol=None,
area_color='#2525f5',
area_opacity=0.3,
legend_top='bottom',
legend_text_size=20,
line_width=3)
radar.render("F:\桌面文件\实训数据\Rader.html")
{"name": "三月", "max": 120, "min": 0},
{"name": "四月", "max": 120, "min": 0},
{"name": "五月", "max": 120, "min": 0},
{"name": "六月", "max": 120, "min": 0},
{"name": "七月", "max": 120, "min": 0},
{"name": "八月", "max": 120, "min": 0},
{"name": "九月", "max": 120, "min": 0},
{"name": "十月", "max": 120, "min": 0},
{"name": "十一月", "max": 120, "min": 0},
{"name": "十二月", "max": 120, "min": 0}]
radar = Radar("ABCDE的雷达图",title_pos='left')
radar.config(c_schema=c_schema,radar_text_size=20)
radar.add(" ", value1, item_color='#2525f5',
symbol=None,aarea_opacity=0.3,
legend_top='bottom',line_width=3)
radar.render("Rader.html")
#f9713c
attr=['生产过程火灾爆炸事故','生产过程中毒伤害事故','储存过程中重特大事故',
'运输过程中火灾爆炸事故','运输过程中毒伤害事故','使用、销售过程中造成的重特大伤亡事故',
'储罐、管道重特大泄漏事故的应急救援','运输车辆、船舶重特大泄漏事故的应急救援','危险化学品其他重特大事故']
v1=[94,55,27,19,15,15,7,8,19]
#v2=[85,24,39,80,27]
pie=Pie(" ",title_pos='left',width=1000,height=300)
pie.add('1',attr,v1, center=[50,60],radius=[30,75],is_label_show=True)
#pie.add('2',attr,v2, center=[75,50],radius=[30,75],is_label_show=True)
pie.render('kongqi5.html')
#'生产过程火灾爆炸事故','生产过程中毒伤害事故','储存过程中重特大事故',
#'运输过程中火灾爆炸事故','运输过程中毒伤害事故','使用、销售过程中造成的重特大伤亡事故',
#'储罐、管道重特大泄漏事故的应急救援','运输车辆、船舶重特大泄漏事故的应急救援','危险化学品其他重特大事故'
# 用于调整雷达各维度的范围大小
c_schema = [{
"name": "篮板",
"max": 10,
"min": -1
}, {
"name": "助攻",
"max": 10,
"min": -1
}, {
"name": "抢断",
"max": 10,
"min": -1
}, {
"name": "盖帽",
"max": 10,
"min": -1
}, {
"name": "失误",
"max": 10,
"min": -1
}]
#画图
rader_name = name_obj['name'].replace('清洗后的数据.csv', '雷达图') + '.html'
radar = Radar()
radar.config(c_schema=c_schema)
radar.add("雷达图", value1)
radar.render(rader_name)
# polar.add("", [1, 2, 3, 4, 1, 2, 5], radius_data=radius, type='barAngle', is_stack=True)
# polar.show_config()
# polar.render('./文件/案例3_极坐标系2.html')
#13.1雷达图
from pyecharts import Radar
schema = [("销售", 6500), ("管理", 16000), ("信息技术", 30000), ("客服", 38000),
("研发", 52000), ("市场", 25000)]
v1 = [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 = [[5000, 14000, 28000, 31000, 42000, 21000]]
radar = Radar()
radar.config(schema)
radar.add("预算分配", v1, is_splitline=True, is_axisline_show=True)
radar.add("实际开销", v2, label_color=["#4e79a7"], is_area_show=False)
radar.show_config()
radar.render('./文件/案例3_雷达图1.html')
#13.2
from pyecharts import Radar
value_bj = [[55, 9, 56, 0.46, 18, 6, 1], [25, 11, 21, 0.65, 34, 9, 2],
[56, 7, 63, 0.3, 14, 5, 3], [33, 7, 29, 0.33, 16, 6, 4]]
value_sh = [[91, 45, 125, 0.82, 34, 23, 1], [65, 27, 78, 0.86, 45, 29, 2],
[83, 60, 84, 1.09, 73, 27, 3], [109, 81, 121, 1.28, 68, 51, 4]]
c_schema = [{
"name": "AQI",
"max": 300,
"min": 5
}, {
"name": "PM2.5",
"max": 250,
"min": 20
app.screen_updating = False
book = app.books.open(filepath)
sheet = book.sheets['sheet1']
data1 = sheet.range('G2:P2').value # 第一串数据
data2 = sheet.range('G3:P3').value # 第二串数据
_list1 = []
_list2 = []
i = 0
j = 0
print(data1)
radar = Radar("雷达图", "球员能力值")
k = 0
for itemm in data1:
print("%d : %s" % (k, itemm))
k = k + 1
for item in data1:
print(item)
_list1.append(item)
radar_data1 = [_list1]
radar_data2 = [_list2]
schema = [("出场时间(minutes)", 3500), ("进球", 30), ("助攻", 25), ("传球成功率", 100),
("创造机会", 22), ("争顶成功", 1), ("全场最佳", 25), ("综合得分", 10)]
radar.config(schema)
radar.add("阿扎尔", radar_data1)
radar.add("斯特林", radar_data2, item_color="#1C86EE")
radar.render("C:\\Users\\mushr\\Desktop\\433\\创3\\DVFiles\\radar.html")
def echart1(request):
template = loader.get_template('ewpyecharts.html')
sql_s = "select typen, count(*) f1, sum(order_m) as f2, sum(status) f3, max(order_m) f4, avg(order_m) f5 from myechart_ewsales group by typen"
columns_s = ["typen", "f1", "f2", "f3", "f4", "f5"]
# index_col_s = "f1"
df = pd.read_sql(sql_s, connection, columns=columns_s)
df = df.sort_values(by='f1', ascending=False)
# f1 数量, f2 金额, f2 成功率, f4 最大金额, F5 平均金额
# old_width = pd.get_option('display.max_colwidth')
# pd.set_option('display.max_colwidth', -1)
# df.to_html('files.html', escape=False, index=False, sparsify=True, border=0, index_names=False, header=False)
# pd.set_option('display.max_colwidth', old_width
df1 = list(df['typen'])
df2 = df[['f1', 'f2', 'f3', 'f4', 'f5']]
# 用于调整雷达各维度的范围大小
c_schema = [{
"name": "数量201910",
"max": df['f1'].max(),
"min": -1
}, {
"name": "金额2019",
"max": df['f2'].max(),
"min": -1
}, {
"name": "成功率",
"max": df['f3'].max(),
"min": -1
}, {
"name": "最大金额",
"max": df['f4'].max(),
"min": -1
}, {
"name": "平均金额",
"max": df['f5'].max(),
"min": -1
}]
#
# 画图
radar = Radar()
radar.config(c_schema=c_schema)
radar.add(df1[0],
df2[0:1].values.tolist(),
item_color="#f9713c",
symbol=None,
area_color="#ea3a2e",
area_opacity=0.1,
legend_top='bottom',
line_width=3)
radar.add(df1[1],
df2[1:2].values.tolist(),
item_color='#2525f5',
symbol=None,
area_color='#2525f5',
area_opacity=0.1,
legend_top='bottom',
legend_text_size=20,
line_width=3)
radar.add(df1[2],
df2[2:3].values.tolist(),
item_color='#112535',
line_width=3)
radar.add(df1[3],
df2[3:4].values.tolist(),
item_color='#991125',
line_width=3)
radar.add(df1[4],
df2[4:5].values.tolist(),
item_color='#1125b9',
line_width=3)
radar.render("Rader.html")
context = dict(myechart=radar.render_embed(),
host=REMOTE_HOST,
script_list=radar.get_js_dependencies())
html_out = template.render(context, request)
# attr = ["衬衫", "羊毛衫", "始祖鸟", "裤子", "高跟鞋", "袜子"]
# v1 = [5, 20, 36, 10, 75, 90]
# v2 = [10, 25, 8, 60, 20, 80]
# bar1 = Bar("柱状图数据堆叠示例")
# bar1.add("商家A", attr, v1, is_stack=True)
# bar1.add("商家B", attr, v2, is_stack=True)
#
# context = dict(
# bar=bar1.render_embed(),
# host=REMOTE_HOST,
# script_list=bar.get_js_dependencies()
# )
# return HttpResponse(template.render(context, request))
return HttpResponse(html_out)
def mysqldataShow():
conn = MySQLdb.Connect(host='127.0.0.1',
port=3306,
user='******',
passwd='',
db='bishe',
charset='utf8')
cursor1 = conn.cursor()
#通过计算得分以后的分数进行查询并且排序
sql_select = "select(@i:=@i+1)as rank,UserName , readnumsum,likenumsum,readnumavg,readnummax,bangindex " \
"from newdata1 a,(select@i:=0)b order by Bangindex desc;"
cursor1.execute(sql_select)
print '选取的总数为:', cursor1.rowcount #获取返回数据总条数
UserName = []
ReadNumSum = []
LikeNumSum = []
ReadNumMax=[]
ReadNumAvg=[]
BangIndex=[]
for i in range(cursor1.rowcount):
rs = cursor1.fetchone()
UserName.append(rs[1])
ReadNumSum.append(rs[2])
LikeNumSum.append(rs[3])
ReadNumAvg.append(rs[4])
ReadNumMax.append(rs[5])
BangIndex.append(rs[6])
print UserName
# 生成柱状图,包括得分
bar1=Bar("公众号排名",width=1500,height=720,page_title='rank',background_color='#F5F5F5')
bar1.add("综合排名", UserName, BangIndex,is_datazoom_show=True,datazoom_type='both',is_toolbox_show=True)
bar1.show_config()
bar1.render(r"C:\Users\lss\Desktop\rank.html")
# 生成柱状图,包括阅读最高数,阅读平均数,总阅读数,点赞数
bar2 = Bar("公众号详细分析", width=1500, height=720,page_title='analysis',background_color='#F5F5F5')
bar2.add("阅读最高数", UserName, ReadNumMax, is_convert=True, mark_line=["average"], mark_point=["max", "min"],
is_toolbox_show=True)
bar2.add("阅读平均数", UserName, ReadNumAvg, is_datazoom_show=True, datazoom_type='both',
datazoom_range=[10, 25], is_toolbox_show=True, mark_line=["average"], mark_point=["max", "min"])
bar2.add("文章点赞数", UserName, LikeNumSum, mark_line=["average"], mark_point=["max", "min"],
is_toolbox_show=True)
bar2.add("文章阅读总数", UserName, ReadNumSum, mark_line=["average"], mark_point=["max", "min"],
is_toolbox_show=True)
bar2.show_config()
bar2.render(r"C:\Users\lss\Desktop\fenxi.html")
# 生成云图
wordcloud = WordCloud(width=1500,height=720,page_title='cloud',background_color='#F5F5F5')
wordcloud.add("公众号",UserName,BangIndex,word_gap=30,word_size_range=[9,100],shape="circle")
wordcloud.render(r"C:\Users\lss\Desktop\cloud.html")
# 对最近一个月排名前10的微信公众号进行排名趋势分析
sql_Tenselect = "select newdata1.username,newdata1.rank as a,newdata2.rank as b," \
"newdata3.rank as c,newdata4.rank as d,newdata5.rank as e " \
"from newdata1,newdata2,newdata3, newdata4,newdata5 " \
"where newdata1.username=newdata2.username " \
"and newdata2.username=newdata3.username " \
"and newdata3.username=newdata4.username " \
"and newdata4.username=newdata5.username " \
"and newdata5.rank in (1,2,3,4,5,6,7,8,9,10)"
cursor2 = conn.cursor()
cursor2.execute(sql_Tenselect)
riqi = ["五月前","四月前","三月前","两月前","一月前"]
line = Line('公众号排名趋势',width=1500,height=720,page_title='line',background_color='#F5F5F5')
for i in range(cursor2.rowcount):
rs2 = cursor2.fetchone()
rank = [rs2[1],rs2[2],rs2[3],rs2[4],rs2[5]]
line.add(rs2[0],riqi,rank,is_smooth=True,is_label_show=True,is_xaxis_boundarygap=True,mark_line=['max','min'],is_yaxis_inverse=True,xaxis_name='时间',yaxis_name='名次',xaxis_name_gap='end')
line.render(r"C:\Users\lss\Desktop\everyone.html")
# 通过雷达图对某个公众号分析
sql_oneselect = "select username, readnumsum,likenumsum,readnumavg,readnummax from newdata5 where newdata5.rank<10"
cursor3 = conn.cursor()
cursor3.execute(sql_oneselect)
rader = Radar(width=1500, height=720,page_title='leida',background_color='#F5F5F5')
schema = [
("文章阅读总数", 1000000),("文章点赞数",31000),("阅读平均数",30000),("阅读最高数",110000)
]
rader.config(schema,radar_text_color='#4682B4')
for i in range(cursor3.rowcount):
rs3 = cursor3.fetchone()
rader.add(rs3[0],[[rs3[1],rs3[2],rs3[3],rs3[4]]],area_color="#4682B4",label_emphasis_textcolor="#4682B4")
rader.render(r"C:\Users\lss\Desktop\one.html")
cursor2.close()
cursor1.close()
conn.close()
