def ksh_scatter(*data):
n = len(data)
if "FG" in data:
m = 4
lamble = 0 #数据是2D为0,3D为1
if data[m] == "FG":
scatter = pyecharts.Scatter(data[0])
scatter.add(data[m - 3], data[m - 2], data[m - 1])
else:
scatter = pyecharts.Scatter3D(data[0], width=1200, height=600)
scatter.add(data[m - 3],
data[m - 2],
is_visualmap=True,
visual_range_color=range_color)
lamble = 1
while m <= n:
if lamble == 0:
scatter.add(data[m - 3], data[m - 2], data[m - 1])
m = m + 4
else:
print(m)
scatter.add(data[m],
data[m + 1],
is_visualmap=True,
visual_range_color=range_color)
m = m + 3
if n == 4:
scatter = pyecharts.Scatter(data[0])
scatter.add(data[1], data[2], data[3])
if n == 3:
scatter = pyecharts.Scatter3D(data[0], width=1200, height=600)
scatter.add(data[1],
data[2],
data[3],
data[4],
is_visualmap=True,
visual_range_color=range_color)
return scatter
def func():
page = pyecharts.Page() # step 1
# bar
attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
v1 = [5, 20, 36, 10, 75, 90]
v2 = [10, 25, 8, 60, 20, 80]
bar = pyecharts.Bar("柱状图数据堆叠示例")
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True)
page.add(bar) # step 2
# scatter3D
import random
data = [[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)
] for _ in range(80)]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
scatter3D = pyecharts.Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
page.add(scatter3D) # step 2
page.render() # step 3
# scatter_geo
attr = ['11', '22']
data = ['1', '2']
scatter_geo = pyecharts.Geo("地理位置视图", width=1200, height=600)
scatter_geo.add("", attr, data)
page.add(scatter_geo) # step 2
page.render() # step 3
data = [["广州", "北京"], ["广州", "上海"]]
scatter_geo = pyecharts.GeoLines("地理位置视图", width=1200, height=600)
scatter_geo.add("上午航班",
data,
geo_normal_color='#ff00ff',
geo_effect_period=9,
geo_effect_color='#ff0000',
geo_effect_symbol='arrow')
data = [["广州", "南昌"], ["广州", "成都"]]
scatter_geo.add("下午航班",
data,
geo_effect_traillength=1,
geo_emphasis_color='#0000ff')
page.add(scatter_geo) # step 2
page.render() # step 3
def creat_Scatter3D():
data = [[
random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)
] for _ in range(80)]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
scatter3D = pyecharts.Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
page.add(scatter3D)
def t12(pa):
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026'
]
scatter3D = p.Scatter3D("经验需求薪水")
scatter3D.add("3D",
next(pa),
is_visualmap=True,
visual_range_color=range_color,
is_grid3d_rotate=True,
visual_range=[7000, 35000],
xaxis3d_name='经验',
yaxis3d_name='需求',
zaxis3d_name='薪水',
is_toolbox_show=False,
visual_top=9999)
return scatter3D
bar.add("商家A", attr, v1, is_stack=True)
bar.add("商家B", attr, v2, is_stack=True)
page.add(bar) # step 2
# scatter3D
import random
data = [
[random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)] for _ in range(80)
]
range_color = [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
scatter3D = echarts.Scatter3D("3D 散点图示例", width=1200, height=600)
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
page.add(scatter3D) # step 2
page.render(path='tmp/page.html')
# 当然，更多图也是可以的
if __name__ == '__main__':
page = echarts.Page()
# line
attr = ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
line = echarts.Line("折线图示例")
line.add("最高气温", attr, [11, 11, 15, 13, 12, 13, 10],
mark_point=["max", "min"], mark_line=["average"])
line.add("最低气温", attr, [1, -2, 2, 5, 3, 2, 0],
mark_point=["max", "min"], mark_line=["average"])
def draw(self):
mat = Cluster.pivot.replace(np.nan, 0).as_matrix()
cluster = KMeans(n_clusters=6).fit(mat)
# clustering result
Cluster.clusters = pd.DataFrame({
Cluster.sampleCol: Cluster.pivot.index,
'Cluster': cluster.labels_
})
# Cluster-Sample-Feature table: explore the meaning/constitution of a cluster
ClSpFt = pd.merge(Cluster.SampFeat, Cluster.clusters,
how='left').groupby('Cluster')
ForEachCluster = lambda x: x.pivot(
index=Cluster.sampleCol, columns=Cluster.featureCol, values='Freq'
).apply(percent).replace(np.nan, 0)
Cluster.ClSpFt = ClSpFt.apply(ForEachCluster)
pca = skl.PCA(n_components=3).fit(mat)
xyz = pca.transform(mat)
# xl,yl,zl=pca.explained_variance_ratio_
# 3D scatter plot
scatter = pch.Scatter3D('Clustering ' + Cluster.sampleCol + ' by ' +
Cluster.featureCol,
is_grid=True,
width=1200,
height=600)
for i, group in pd.concat(
[Cluster.clusters, pd.DataFrame(xyz)], axis=1).groupby('Cluster'):
scatter.add('Cluster ' + str(i),
xyz[group.index].tolist(),
grid_right="75%",
legend_top="10%",
legend_pos="0%",
is_grid3D_rotate=True,
grid3D_rotate_speed=40)
# Radar chart
radar = pch.Radar('Radar Chart of ' + Cluster.sampleCol + ' on ' +
Cluster.featureCol,
is_grid=True)
schema = [(feat, 1) for feat in Cluster.switch[Cluster.featureCol]]
radar.config(schema)
for i in np.arange(6):
data = Cluster.ClSpFt.loc[i].as_matrix().tolist()
c = [
'#50a3ba', '#eac763', '#d94e5d', '#4e79a7', '#f9713c',
'#b3e4a1'
]
radar.add('Cluster ' + str(i),
data,
item_color=c[i],
legend_pos='0%',
legend_top="10%",
is_area_show=True,
area_color=c[i],
area_opacity=0.5)
radar.render('Graph/Cluster/' + Cluster.sampleCol +
Cluster.featureCol + '_radar.html')
scatter.render('Graph/Cluster/' + Cluster.sampleCol +
Cluster.featureCol + '_scatter.html')