import pandas as pd
import numpy as np

df = pd.read_csv("midu.csv")

x = df['2019']

y = df['country']

人口密度 = zip(list(y),list(x))

人口密度 = list(zip(list(y),list(x)))

from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.globals import ChartType, SymbolType
from pyecharts.globals import ThemeType

def map_world_people() -> Map:
    c = (
        Map()
        .add("世界人口密度",人口密度 ,"world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="世界人口密度"),
            visualmap_opts=opts.VisualMapOpts(max_=700),
        )
    )
    return c
map_world_people().render_notebook()

import pandas as pd
import numpy as np

df = pd.read_csv("income.csv")
df

a = df['2019']
aa = list(a)

b = df['World Bank income groups']
bb = list(b)

cc = list=['高收入国家','中收入国家','中偏上收入国家','中偏下收入国家','低收入国家','无可用收入国家']

from pyecharts.charts import Bar

def bar_with_brush() -> Bar:
    c = (
        Bar()
        .add_xaxis(cc)
        .add_yaxis("不同收入水平国家的人口密度", aa,category_gap="50%")
        .set_global_opts(
            title_opts=opts.TitleOpts(title="世界人口密度以收入水平分"),
            brush_opts=opts.BrushOpts(),
        )
    )
    return c
bar_with_brush().render_notebook()

	World Bank income groups	2019	2020
0	High-income countries	33.206186	33.339466
1	Middle-income countries	73.986109	74.718406
2	Upper-middle-income countries	47.515831	47.801361
3	Lower-middle-income countries	142.497814	144.386458
4	Low-income countries	50.919486	52.257457
5	No income group available	7.906779	8.004407