疫情数据简析，可视化呈现

提示：文章写完后，目录可以自动生成，如何生成可参考右边的帮助文档

---

前言

提示：这里可以添加本文要记录的大概内容：

最近疫情出现新状况，作为一个中国人民怎么能不关注，作此数据处理时才发现已经这么严重了，不过我们可以永远相信自己的国家，风云终会消散。

---

提示：以下是本篇文章正文内容，下面案例可供参考

1.引入库

代码如下（示例）：

from pyecharts import options as opts
from pyecharts.charts import Map, Timeline
import pandas as pd
import numpy as np

2.读入数据

data = pd.read_excel('新型冠状病毒肺炎疫情地图.xlsx', index_col=None)
print(data.head())

3.提取需要数据

这里我将每一列疫情数据与地区结合为一个个列表

# 然后转化为list形式
xy_data_list = np.array(data[['地区', '现有']]).tolist()
xz_data_list = np.array(data[['地区', '新增']]).tolist()
lj_data_list = np.array(data[['地区', '累计']]).tolist()
zy_data_list = np.array(data[['地区', '治愈']]).tolist()

4.绘制中国地图，添加数据

使用pyecharts库绘制中国地图，然后调节对应参数，这里就步详讲了，调参就要一步一步慢慢试。

def map_china() -> Map:
    map1 = (
        Map()
        .add(series_name="现有病例", data_pair=xy_data_list, maptype="china", zoom=1, center=[105, 38])
        .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：现有病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
            pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                    {"max": 99, "min": 10, "label": "10-99","color": "#FF7F50"},
                    {"max": 499, "min": 100, "label": "100-499","color":"#F08080"},
                                    {"max": 999, "min": 500, "label": "500-999","color":"#CD5C5C"},
                                    {"max": 9999, "min": 1000, "label": ">=1000", "color":"#8B0000"}]
                                             )
        )
    )
    map2 = (
        Map()
            .add(series_name="新增病例", data_pair=xz_data_list, maptype="china", zoom=1, center=[105, 38])
            .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：新增病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
                                              pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                                                      {"max": 99, "min": 10, "label": "10-99", "color": "#FF7F50"},
                                                      {"max": 499, "min": 100, "label": "100-499", "color": "#F08080"},
                                                      {"max": 999, "min": 500, "label": "500-999", "color": "#CD5C5C"},
                                                      {"max": 9999, "min": 1000, "label": ">=1000", "color": "#8B0000"}]
                                              )
        )
    )
    map3 = (
        Map()
            .add(series_name="累计病例", data_pair=lj_data_list, maptype="china", zoom=1, center=[105, 38])
            .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：累计病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
                                              pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                                                      {"max": 99, "min": 10, "label": "10-99", "color": "#FF7F50"},
                                                      {"max": 499, "min": 100, "label": "100-499", "color": "#F08080"},
                                                      {"max": 999, "min": 500, "label": "500-999", "color": "#CD5C5C"},
                                                      {"max": 9999, "min": 1000, "label": ">=1000", "color": "#8B0000"}]
                                              )
        )
    )
    map4 = (
        Map()
            .add(series_name="治愈病例", data_pair=zy_data_list, maptype="china", zoom=1, center=[105, 38])
            .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：治愈病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
                                              pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                                                      {"max": 99, "min": 10, "label": "10-99", "color": "#FF7F50"},
                                                      {"max": 499, "min": 100, "label": "100-499", "color": "#F08080"},
                                                      {"max": 999, "min": 500, "label": "500-999", "color": "#CD5C5C"},
                                                      {"max": 9999, "min": 1000, "label": ">=1000", "color": "#8B0000"}]
                                              )
        )
    )

每个列表做一个图，然后为了方便，渲染成了轮播图如下步

5.渲染轮播图

如下：设置需要参数，最终呈现：

#  创建组合类对象
    timeline = Timeline(init_opts=opts.InitOpts(width='1028px', height='600px'))
    # 在组合对象中添加需要组合的图表对象

    timeline.add(chart=map1, time_point="现有病例疫情图")
    timeline.add(chart=map2, time_point="新增病例疫情图")
    timeline.add(chart=map3, time_point="累计病例疫情图")
    timeline.add(chart=map4, time_point="治愈病例疫情图")
    timeline.add_schema(is_auto_play=True, play_interval=2000)
    # 渲染数据
    timeline.render('疫情轮播图.html')

效果如下：
动图效果无法展示

总结

以上就是今天要讲的内容，本文仅仅简单制作了今天疫情数据简析用，而pandas，pyechart提供了大量能使我们快速便捷地处理数据的函数和方法，还有更多更好的需要学习呢。

完整代码

from pyecharts import options as opts
from pyecharts.charts import Map, Timeline
import pandas as pd
import numpy as np


data = pd.read_excel('新型冠状病毒肺炎疫情地图.xlsx', index_col=None)
print(data.head())
# 然后转化为list形式
xy_data_list = np.array(data[['地区', '现有']]).tolist()
xz_data_list = np.array(data[['地区', '新增']]).tolist()
lj_data_list = np.array(data[['地区', '累计']]).tolist()
zy_data_list = np.array(data[['地区', '治愈']]).tolist()
print(xy_data_list)


def map_china() -> Map:
    map1 = (
        Map()
        .add(series_name="现有病例", data_pair=xy_data_list, maptype="china", zoom=1, center=[105, 38])
        .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：现有病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
            pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                    {"max": 99, "min": 10, "label": "10-99","color": "#FF7F50"},
                    {"max": 499, "min": 100, "label": "100-499","color":"#F08080"},
                                    {"max": 999, "min": 500, "label": "500-999","color":"#CD5C5C"},
                                    {"max": 9999, "min": 1000, "label": ">=1000", "color":"#8B0000"}]
                                             )
        )
    )
    map2 = (
        Map()
            .add(series_name="新增病例", data_pair=xz_data_list, maptype="china", zoom=1, center=[105, 38])
            .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：新增病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
                                              pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                                                      {"max": 99, "min": 10, "label": "10-99", "color": "#FF7F50"},
                                                      {"max": 499, "min": 100, "label": "100-499", "color": "#F08080"},
                                                      {"max": 999, "min": 500, "label": "500-999", "color": "#CD5C5C"},
                                                      {"max": 9999, "min": 1000, "label": ">=1000", "color": "#8B0000"}]
                                              )
        )
    )
    map3 = (
        Map()
            .add(series_name="累计病例", data_pair=lj_data_list, maptype="china", zoom=1, center=[105, 38])
            .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：累计病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
                                              pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                                                      {"max": 99, "min": 10, "label": "10-99", "color": "#FF7F50"},
                                                      {"max": 499, "min": 100, "label": "100-499", "color": "#F08080"},
                                                      {"max": 999, "min": 500, "label": "500-999", "color": "#CD5C5C"},
                                                      {"max": 9999, "min": 1000, "label": ">=1000", "color": "#8B0000"}]
                                              )
        )
    )
    map4 = (
        Map()
            .add(series_name="治愈病例", data_pair=zy_data_list, maptype="china", zoom=1, center=[105, 38])
            .set_global_opts(
            title_opts=opts.TitleOpts(title="疫情地图：治愈病例"),
            visualmap_opts=opts.VisualMapOpts(max_=9999, is_piecewise=True,
                                              pieces=[{"max": 9, "min": 0, "label": "0-9", "color": "#FFE4E1"},
                                                      {"max": 99, "min": 10, "label": "10-99", "color": "#FF7F50"},
                                                      {"max": 499, "min": 100, "label": "100-499", "color": "#F08080"},
                                                      {"max": 999, "min": 500, "label": "500-999", "color": "#CD5C5C"},
                                                      {"max": 9999, "min": 1000, "label": ">=1000", "color": "#8B0000"}]
                                              )
        )
    )
    #  创建组合类对象
    timeline = Timeline(init_opts=opts.InitOpts(width='1028px', height='600px'))
    # 在组合对象中添加需要组合的图表对象

    timeline.add(chart=map1, time_point="现有病例疫情图")
    timeline.add(chart=map2, time_point="新增病例疫情图")
    timeline.add(chart=map3, time_point="累计病例疫情图")
    timeline.add(chart=map4, time_point="治愈病例疫情图")
    timeline.add_schema(is_auto_play=True, play_interval=2000)
    # 渲染数据
    timeline.render('疫情轮播图.html')


if __name__ == '__main__':
    d_map = map_china()