pyecharts在数据可视化中的应用 （二）（pyecharts绘制树图、矩形树图、地理热力图、词云图、相关性矩阵等图）

1.使用以下JSON数据绘制树图、矩形树图。

from pyecharts import options as opts
from pyecharts.charts import Tree
data = [{
    "name": "flare",
    "children": [
        {
            "name": "flex",
            "children": [{"name": "FlareVis", "value": 4116}]
        },
        {
            "name": "scale",
            "children": [{"name": "IScaleMap", "value": 2105},
                         {"name": "LinearScale", "value": 1316},
                         {"name": "LogScale", "value": 3151},
                         {"name": "OrdinalScale", "value": 3770},
                         {"name": "QuantileScale", "value": 2435},
                         {"name": "QuantitativeScale", "value": 4839},
                         {"name": "RootScale", "value": 1756},
                         {"name": "Scale", "value": 4268},
                         {"name": "ScaleType", "value": 1821},
                         {"name": "TimeScale", "value": 5833}]
        },
        {
            "name": "display",
            "children": [{"name": "DirtySprite", "value": 8833}]
        }
    ]
}]
c = (
    Tree()
    .add("", data)
    .set_global_opts(title_opts=opts.TitleOpts(title="Tree-基本示例"))
    .render("树图.html")
)
# c = (
#     TreeMap()
#     .add("演示数据", data)
#     .set_global_opts(title_opts=opts.TitleOpts(title="TreeMap-基本示例"))
#     .render("矩形树图.html")
# )

2.绘制鸢尾花数据的相关性矩阵（数据：iris.csv）。

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

data = pd.read_csv('iris.csv')
# print(data)
sns.pairplot(data=data, hue='Species', vars=['Unnamed: 0', 'Sepal.Length', 'Petal.Length', 'Petal.Width'])
plt.show()

3.在地图上用圆点标出各省的销售额数据。

from pyecharts import options as opts
from pyecharts.charts import Map
import random
# 导入数据
province = ['广东', '湖北', '湖南', '四川', '重庆', '黑龙江', '浙江', '山西', '河北', '安徽', '河南', '山东', '西藏']
data = [(i, random.randint(50, 150)) for i in province]
c = (
    Map()
    .add("省份",  data, "china")
    # 设置坐标属性
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    # 设置全局属性
    .set_global_opts(
        title_opts=opts.TitleOpts(title="各省的销售额数据"),
        visualmap_opts=opts.VisualMapOpts(max_=150, is_piecewise=True),
    )
    .render("geo_base3.html")
)

4.绘制地理热点图展示某连锁企业在湖北省各城市的门店数。

from pyecharts import options as opts
from pyecharts.charts import Geo
import random
from pyecharts.globals import ChartType
province = ['武汉', '十堰', '鄂州', '宜昌', '荆州', '孝感', '黄石', '咸宁', '仙桃']
data = [(i, random.randint(50, 150)) for i in province]
c = (
    Geo()
    # 加载图表模型中的湖北地图
    .add_schema(maptype="湖北")
    # 在地图中加入点的属性
    .add(
        "门店数",
        [list(z) for z in zip(province, data)],
        type_=ChartType.HEATMAP,
    )
    # 设置坐标属性
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
    # 设置全局属性
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(max_=150, min_=50, is_piecewise=True),
        legend_opts=opts.LegendOpts(is_show=False),
        title_opts=opts.TitleOpts(title="Geo-湖北地图")
    )
    .render("geo_湖北.html")
)

5.绘制词云图（数据：word_data.csv）。

import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import WordCloud

data = pd.read_csv('word_data.csv')
words = list(data['category'])
views = list(data['views'])
data_pair = [(i, j)for i, j in zip(words, views)]
c = (
    WordCloud()
    .add(
        "词云图",
        data_pair=data_pair,
        word_size_range=[10, 66],
    )
    .set_global_opts(title_opts=opts.TitleOpts(
        title="WordCloud",
        title_textstyle_opts=opts.TextStyleOpts(font_size=23)),
        tooltip_opts=opts.TooltipOpts(is_show=True)
    )
    .render("词云图.html")
)

6.绘制主题河流图。

import pyecharts.options as opts
from pyecharts.charts import ThemeRiver

datax = ['分支1', '分支2', '分支3', '分支4', '分支5', '分支6']
datay = [
    ['2015/11/08', 10, '分支1'], ['2015/11/09', 15, '分支1'], ['2015/11/10', 35, '分支1'],
    ['2015/11/14', 7, '分支1'], ['2015/11/15', 2, '分支1'], ['2015/11/16', 17, '分支1'],
    ['2015/11/17', 33, '分支1'], ['2015/11/18', 40, '分支1'], ['2015/11/19', 32, '分支1'],
    ['2015/11/20', 26, '分支1'], ['2015/11/21', 35, '分支1'], ['2015/11/22', 40, '分支1'],
    ['2015/11/23', 32, '分支1'], ['2015/11/24', 26, '分支1'], ['2015/11/25', 22, '分支1'],
    ['2015/11/08', 35, '分支2'], ['2015/11/09', 36, '分支2'], ['2015/11/10', 37, '分支2'],
    ['2015/11/11', 22, '分支2'], ['2015/11/12', 24, '分支2'], ['2015/11/13', 26, '分支2'],
    ['2015/11/14', 34, '分支2'], ['2015/11/15', 21, '分支2'], ['2015/11/16', 18, '分支2'],
    ['2015/11/17', 45, '分支2'], ['2015/11/18', 32, '分支2'], ['2015/11/19', 35, '分支2'],
    ['2015/11/20', 30, '分支2'], ['2015/11/21', 28, '分支2'], ['2015/11/22', 27, '分支2'],
    ['2015/11/23', 26, '分支2'], ['2015/11/24', 15, '分支2'], ['2015/11/25', 30, '分支2'],
    ['2015/11/26', 35, '分支2'], ['2015/11/27', 42, '分支2'], ['2015/11/28', 42, '分支2'],
    ['2015/11/08', 21, '分支3'], ['2015/11/09', 25, '分支3'], ['2015/11/10', 27, '分支3'],
    ['2015/11/11', 23, '分支3'], ['2015/11/12', 24, '分支3'], ['2015/11/13', 21, '分支3'],
    ['2015/11/14', 35, '分支3'], ['2015/11/15', 39, '分支3'], ['2015/11/16', 40, '分支3'],
    ['2015/11/17', 36, '分支3'], ['2015/11/18', 33, '分支3'], ['2015/11/19', 43, '分支3'],
    ['2015/11/20', 40, '分支3'], ['2015/11/21', 34, '分支3'], ['2015/11/22', 28, '分支3'],
    ['2015/11/14', 7, '分支4'], ['2015/11/15', 2, '分支4'], ['2015/11/16', 17, '分支4'],
    ['2015/11/17', 33, '分支4'], ['2015/11/18', 40, '分支4'], ['2015/11/19', 32, '分支4'],
    ['2015/11/20', 26, '分支4'], ['2015/11/21', 35, '分支4'], ['2015/11/22', 40, '分支4'],
    ['2015/11/23', 32, '分支4'], ['2015/11/24', 26, '分支4'], ['2015/11/25', 22, '分支4'],
    ['2015/11/26', 16, '分支4'], ['2015/11/27', 22, '分支4'], ['2015/11/28', 10, '分支4'],
    ['2015/11/08', 10, '分支5'], ['2015/11/09', 15, '分支5'], ['2015/11/10', 35, '分支5'],
    ['2015/11/11', 38, '分支5'], ['2015/11/12', 22, '分支5'], ['2015/11/13', 16, '分支5'],
    ['2015/11/14', 7, '分支5'], ['2015/11/15', 2, '分支5'], ['2015/11/16', 17, '分支5'],
    ['2015/11/17', 33, '分支5'], ['2015/11/18', 40, '分支5'], ['2015/11/19', 32, '分支5'],
    ['2015/11/20', 26, '分支5'], ['2015/11/21', 35, '分支5'], ['2015/11/22', 4, '分支5'],
    ['2015/11/23', 32, '分支5'], ['2015/11/24', 26, '分支5'], ['2015/11/25', 22, '分支5'],
    ['2015/11/26', 16, '分支5'], ['2015/11/27', 22, '分支5'], ['2015/11/28', 10, '分支5'],
    ['2015/11/08', 10, '分支6'], ['2015/11/09', 15, '分支6'], ['2015/11/10', 35, '分支6'],
    ['2015/11/11', 38, '分支6'], ['2015/11/12', 22, '分支6'], ['2015/11/13', 16, '分支6'],
    ['2015/11/14', 7, '分支6'], ['2015/11/15', 2, '分支6'], ['2015/11/16', 17, '分支6'],
    ['2015/11/17', 33, '分支6'], ['2015/11/18', 4, '分支6'], ['2015/11/19', 32, '分支6'],
    ['2015/11/20', 26, '分支6'], ['2015/11/21', 35, '分支6'], ['2015/11/22', 40, '分支6'],
    ['2015/11/23', 32, '分支6'], ['2015/11/24', 26, '分支6'], ['2015/11/25', 22, '分支6']
]
(
    ThemeRiver(init_opts=opts.InitOpts(width="1000px", height="600px"))
    .add(
        series_name=datax,
        data=datay,
        singleaxis_opts=opts.SingleAxisOpts(
            pos_top="50", pos_bottom="50", type_="time"
        ),
    )
    .set_global_opts(
        tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line")
    )
    .render("主题河流图.html")
)

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