python可视化数据分析pyecharts初步尝试

bar = Bar()

bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])

bar.set_global_opts(title_opts=opts.TitleOpts(title="主标题", subtitle="副标题"))

import random
from pyecharts import options as opts
from pyecharts.charts import Boxplot
import numpy as np
# 离线资源，有网络下可以不管
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = "http://127.0.0.1:8889/assets/"
# 长度为1的str list
x_label = ['随机数'] 
data = np.random.randint(1000, size=100)
# 这里data应为2维数组，长度和x_label相同的 list list
data = [data.tolist()]  
boxplot = Boxplot()
boxplot.add_xaxis(x)
# 调用自带的函数，计算箱型图需要的数据
y_value = boxplot.prepare_data(y_value)
boxplot.add_yaxis('', y_value)
boxplot.set_global_opts(title_opts=opts.TitleOpts(title='box plot demo'))
boxplot.render()

# -*- coding: utf-8 -*-
from pyecharts.charts import Bar
from pyecharts import options as opts
import numpy as np
# 离线资源，有网络下可以不管
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = "http://127.0.0.1:8889/assets/"
# 随机数组，0~255的数字，10000个
x = np.random.randint(255, size=1000) 
# 统计直方图
sum = np.zeros(256, dtype=np.int32)
for cur_x in x:
    sum[cur_x] += 1
# 绘图
bar = Bar()
# x轴 0~255
x_label = [str(label) for label in list(range(256))]
bar.add_xaxis(x_label)
# y轴 频数, 这里的list一定要是标准int,不能为 np.int，所有 y_axis=list(sum)的话是不可以的
bar.add_yaxis(series_name='频数', y_axis=sum.tolist())
# 设置标题
bar.set_global_opts(title_opts=opts.TitleOpts(title='直方图统计'))
# 生成网页，会在当前目录下生成一个render.html
bar.render()

import random
from pyecharts import options as opts
from pyecharts.charts import HeatMap
from pyecharts.faker import Faker
# 离线资源，有网络下可以不管
from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = "http://127.0.0.1:8889/assets/"
value = [[i, j, random.randint(0, 50)] for i in range(24) for j in range(7)]
heatmap = (
    HeatMap()
    .add_xaxis(Faker.clock)
    .add_yaxis(
        "",
        Faker.week,
        value,
        label_opts=opts.LabelOpts(is_show=True, position="inside"),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="基础热力图"),
        visualmap_opts=opts.VisualMapOpts(),
    )
)
heatmap.render()