数据：全球每平方公里的人口密度。截取了1990年与2019年的数据。¶

概述：当今人口急剧增长，地球人口达到了前所未有的数量，通过观察分析相隔29年-30年的时间，世界各国的人口变化。分析人口增长的原因以及未来对人类发展的影响。¶

故事：从两张地图的颜色对比可以明显看出，世界人口密度最高的在亚洲，尤其是印度、中国、日本、菲律宾等国家，这30年的时间里，欧洲、南北美洲、澳洲、非洲的人口增长都相对比较缓慢，人口密度不高。随着人口的高度增长以及人类消费水平的不断提高，人类必然向环境索取越来越多的资源，人口的大量增长，必然会造成对自然资源的不合理的过度开发、地球的资源紧张，人们要对可再生、不可再生资源的利用更加严谨，而且要有效利用资源，提高资源利用率、土地使用率。¶

import pandas as pd
import csv, os
df = pd.read_csv("Desktop\popular.csv")      
df

print(list(df.country))

['Burundi', 'Comoros', 'Djibouti', 'Eritrea', 'Ethiopia', 'Kenya', 'Madagascar', 'Malawi', 'Mauritius', 'Mayotte', 'Mozambique', 'Réunion', 'Rwanda', 'Seychelles', 'Somalia', 'South Sudan', 'Uganda', 'United Republic of Tanzania', 'Zambia', 'Zimbabwe', 'Angola', 'Cameroon', 'Central African Republic', 'Chad', 'Congo', 'Democratic Republic of the Congo', 'Equatorial Guinea', 'Gabon', 'Sao Tome and Principe', 'Botswana', 'Eswatini', 'Lesotho', 'Namibia', 'South Africa', 'Benin', 'Burkina Faso', 'Cabo Verde', "C?te d'Ivoire", 'Gambia', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Liberia', 'Mali', 'Mauritania', 'Niger', 'Nigeria', 'Saint Helena', 'Senegal', 'Sierra Leone', 'Togo', 'Algeria', 'Egypt', 'Libya', 'Morocco', 'Sudan', 'Tunisia', 'Western Sahara', 'Armenia', 'Azerbaijan', 'Bahrain', 'Cyprus', 'Georgia', 'Iraq', 'Israel', 'Jordan', 'Kuwait', 'Lebanon', 'Oman', 'Qatar', 'Saudi Arabia', 'State of Palestine', 'Syrian Arab Republic', 'Turkey', 'United Arab Emirates', 'Yemen', 'Kazakhstan', 'Kyrgyzstan', 'Tajikistan', 'Turkmenistan', 'Uzbekistan', 'Afghanistan', 'Bangladesh', 'Bhutan', 'India', 'Iran (Islamic Republic of)', 'Maldives', 'Nepal', 'Pakistan', 'Sri Lanka', 'China', 'China, Hong Kong SAR', 'China, Macao SAR', 'China, Taiwan Province of China', "Dem. People's Republic of Korea", 'Japan', 'Mongolia', 'Republic of Korea', 'Brunei Darussalam', 'Cambodia', 'Indonesia', "Lao People's Democratic Republic", 'Malaysia', 'Myanmar', 'Philippines', 'Singapore', 'Thailand', 'Timor-Leste', 'Viet Nam', 'Anguilla', 'Antigua and Barbuda', 'Aruba', 'Bahamas', 'Barbados', 'Bonaire, Sint Eustatius and Saba', 'British Virgin Islands', 'Cayman Islands', 'Cuba', 'Cura?ao', 'Dominica', 'Dominican Republic', 'Grenada', 'Guadeloupe', 'Haiti', 'Jamaica', 'Martinique', 'Montserrat', 'Puerto Rico', 'Saint Barthélemy', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Martin (French part)', 'Saint Vincent and the Grenadines', 'Sint Maarten (Dutch part)', 'Trinidad and Tobago', 'Turks and Caicos Islands', 'United States Virgin Islands', 'Belize', 'Costa Rica', 'El Salvador', 'Guatemala', 'Honduras', 'Mexico', 'Nicaragua', 'Panama', 'Argentina', 'Bolivia (Plurinational State of)', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Falkland Islands (Malvinas)', 'French Guiana', 'Guyana', 'Paraguay', 'Peru', 'Suriname', 'Uruguay', 'Venezuela (Bolivarian Republic of)', 'Australia', 'New Zealand', 'Fiji', 'New Caledonia', 'Papua New Guinea', 'Solomon Islands', 'Vanuatu', 'Guam', 'Kiribati', 'Marshall Islands', 'Micronesia (Fed. States of)', 'Nauru', 'Northern Mariana Islands', 'Palau', 'American Samoa', 'Cook Islands', 'French Polynesia', 'Niue', 'Samoa', 'Tokelau', 'Tonga', 'Tuvalu', 'Wallis and Futuna Islands', 'Belarus', 'Bulgaria', 'Czechia', 'Hungary', 'Poland', 'Republic of Moldova', 'Romania', 'Russian Federation', 'Slovakia', 'Ukraine', 'Channel Islands', 'Denmark', 'Estonia', 'Faroe Islands', 'Finland', 'Iceland', 'Ireland', 'Isle of Man', 'Latvia', 'Lithuania', 'Norway', 'Sweden', 'United Kingdom', 'Albania', 'Andorra', 'Bosnia and Herzegovina', 'Croatia', 'Gibraltar', 'Greece', 'Holy See', 'Italy', 'Malta', 'Montenegro', 'North Macedonia', 'Portugal', 'San Marino', 'Serbia', 'Slovenia', 'Spain', 'Austria', 'Belgium', 'France', 'Germany', 'Liechtenstein', 'Luxembourg', 'Monaco', 'Netherlands', 'Switzerland', 'Bermuda', 'Canada', 'Greenland', 'Saint Pierre and Miquelon', 'United States of America']

print(list(df['1990']))

[211.8, 221.2, 25.5, 22.4, 47.9, 41.7, 19.9, 99.8, 520.1, 252.7, 16.5, 244.2, 295.5, 153.4, 11.5, 9.0, 86.9, 28.5, 10.8, 27.0, 9.5, 24.9, 4.5, 4.7, 6.9, 15.3, 14.9, 3.7, 124.2, 2.3, 47.8, 56.1, 1.7, 30.3, 44.2, 32.2, 83.9, 37.5, 94.4, 64.9, 25.9, 34.7, 21.6, 6.9, 2.0, 6.3, 104.5, 17.1, 39.1, 59.8, 69.4, 10.8, 56.4, 2.5, 55.6, 11.4, 53.1, 0.8, 124.3, 87.6, 652.5, 83.0, 77.9, 40.1, 205.6, 40.2, 117.6, 274.0, 5.9, 41.0, 7.6, 349.1, 67.8, 70.1, 21.9, 22.2, 6.1, 22.8, 37.8, 7.8, 48.0, 19.0, 792.6, 13.9, 293.7, 34.6, 743.9, 131.9, 139.6, 276.3, 125.4, 5455.2, 11498.9, 578.3, 168.5, 341.5, 1.4, 441.4, 49.1, 50.8, 100.1, 18.5, 54.9, 63.3, 207.6, 4304.2, 110.7, 49.6, 219.3, 98.8, 142.1, 345.3, 25.6, 606.8, 39.7, 116.6, 105.4, 99.6, 330.4, 93.9, 147.6, 283.3, 239.1, 255.4, 223.4, 338.2, 106.2, 383.7, 243.0, 154.8, 226.3, 594.8, 275.6, 847.5, 238.0, 12.8, 296.4, 8.2, 61.1, 254.3, 86.4, 44.3, 43.2, 34.7, 33.2, 11.9, 6.3, 17.8, 17.9, 29.8, 41.2, 0.2, 1.4, 3.8, 10.6, 17.2, 2.6, 17.8, 22.3, 2.2, 12.9, 39.9, 9.3, 10.2, 11.1, 12.0, 241.6, 89.4, 262.6, 137.6, 475.3, 99.5, 32.7, 236.8, 75.8, 54.6, 9.0, 57.5, 160.4, 132.0, 297.0, 98.5, 50.0, 81.4, 133.9, 114.6, 124.0, 132.9, 102.1, 9.0, 110.0, 88.8, 740.4, 121.2, 36.9, 33.9, 16.4, 2.5, 51.0, 123.3, 42.8, 59.0, 11.6, 20.9, 236.2, 119.9, 116.0, 87.5, 85.4, 2914.9, 79.3, 1731.8, 193.9, 1131.3, 45.7, 79.2, 108.0, 402.1, 108.8, 99.6, 78.6, 93.7, 330.5, 103.5, 226.8, 179.9, 147.4, 19753.7, 443.8, 168.4, 1232.6, 3.0, 0.1, 27.8, 27.6]

print(list(df['2019']))

[449.0, 457.2, 42.0, 34.6, 112.1, 92.4, 46.4, 197.6, 625.5, 709.7, 38.6, 355.6, 511.8, 212.5, 24.6, 18.1, 221.6, 65.5, 24.0, 37.9, 25.5, 54.7, 7.6, 12.7, 15.8, 38.3, 48.3, 8.4, 224.0, 4.1, 66.8, 70.0, 3.0, 48.3, 104.7, 74.3, 136.5, 80.9, 232.0, 133.7, 52.0, 68.3, 51.3, 16.1, 4.4, 18.4, 220.7, 15.5, 84.6, 108.2, 148.6, 18.1, 100.8, 3.9, 81.7, 24.3, 75.3, 2.2, 103.9, 121.6, 2159.4, 129.7, 57.5, 90.5, 393.7, 113.8, 236.1, 670.2, 16.1, 243.9, 15.9, 827.5, 93.0, 108.4, 116.9, 55.2, 6.9, 33.5, 66.6, 12.6, 77.5, 58.3, 1252.6, 20.0, 459.6, 50.9, 1769.9, 199.6, 280.9, 340.0, 152.7, 7082.1, 21419.6, 671.4, 213.2, 348.0, 2.1, 526.8, 82.2, 93.4, 149.4, 31.1, 97.2, 82.7, 362.6, 8291.9, 136.3, 87.0, 311.1, 165.2, 220.7, 590.6, 38.9, 667.5, 79.2, 200.2, 270.6, 106.5, 368.1, 95.7, 222.2, 329.4, 245.7, 408.7, 272.2, 354.3, 49.9, 330.7, 447.9, 203.2, 299.7, 717.0, 283.6, 1246.7, 271.9, 40.2, 298.8, 17.1, 98.9, 311.5, 164.1, 87.1, 65.6, 54.4, 57.1, 16.4, 10.6, 25.3, 25.5, 45.4, 70.0, 0.3, 3.5, 4.0, 17.7, 25.4, 3.7, 19.8, 32.3, 3.3, 18.2, 48.7, 15.5, 19.4, 23.9, 24.6, 309.8, 145.2, 326.6, 162.6, 538.2, 124.4, 39.1, 276.6, 73.1, 76.3, 6.2, 69.6, 133.0, 145.1, 388.5, 81.7, 46.6, 64.5, 138.4, 107.0, 123.7, 123.1, 84.1, 8.9, 113.5, 75.9, 906.7, 136.0, 31.3, 34.9, 18.2, 3.4, 70.9, 148.4, 30.7, 44.0, 14.7, 24.5, 279.1, 105.1, 164.1, 64.7, 73.8, 3370.6, 81.3, 1852.3, 205.9, 1376.2, 46.7, 82.6, 111.7, 564.4, 100.3, 103.2, 93.7, 108.7, 381.1, 118.9, 239.6, 237.6, 237.7, 26152.3, 507.0, 217.4, 1250.2, 4.1, 0.1, 25.3, 36.0]

from pyecharts.faker import Faker

from pyecharts import options as opts
from pyecharts .charts import Map
from pyecharts.globals import ChartType,SymbolType
def map_world() -> Map:
    c = (
        Map()
        .add("人口密度", [list(z) for z in zip(list(df.country),list(df['1990']))], "world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Map_世界地图"),
            visualmap_opts=opts.VisualMapOpts(max_=300),
        )
       
    )
    return c

map_world().render_notebook()

from pyecharts.faker import Faker

from pyecharts import options as opts
from pyecharts .charts import Map
from pyecharts.globals import ChartType,SymbolType
def map_world() -> Map:
    c = (
        Map()
        .add("人口密度", [list(z) for z in zip(list(df.country),list(df['2019']))], "world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="Map_世界地图"),
            visualmap_opts=opts.VisualMapOpts(max_=300),
        )
       
    )
    return c

map_world().render_notebook()

	country	1990	2019
0	Burundi	211.8	449.0
1	Comoros	221.2	457.2
2	Djibouti	25.5	42.0
3	Eritrea	22.4	34.6
4	Ethiopia	47.9	112.1
5	Kenya	41.7	92.4
6	Madagascar	19.9	46.4
7	Malawi	99.8	197.6
8	Mauritius	520.1	625.5
9	Mayotte	252.7	709.7
10	Mozambique	16.5	38.6
11	Réunion	244.2	355.6
12	Rwanda	295.5	511.8
13	Seychelles	153.4	212.5
14	Somalia	11.5	24.6
15	South Sudan	9.0	18.1
16	Uganda	86.9	221.6
17	United Republic of Tanzania	28.5	65.5
18	Zambia	10.8	24.0
19	Zimbabwe	27.0	37.9
20	Angola	9.5	25.5
21	Cameroon	24.9	54.7
22	Central African Republic	4.5	7.6
23	Chad	4.7	12.7
24	Congo	6.9	15.8
25	Democratic Republic of the Congo	15.3	38.3
26	Equatorial Guinea	14.9	48.3
27	Gabon	3.7	8.4
28	Sao Tome and Principe	124.2	224.0
29	Botswana	2.3	4.1
...	...	...	...
205	Albania	119.9	105.1
206	Andorra	116.0	164.1
207	Bosnia and Herzegovina	87.5	64.7
208	Croatia	85.4	73.8
209	Gibraltar	2914.9	3370.6
210	Greece	79.3	81.3
211	Holy See	1731.8	1852.3
212	Italy	193.9	205.9
213	Malta	1131.3	1376.2
214	Montenegro	45.7	46.7
215	North Macedonia	79.2	82.6
216	Portugal	108.0	111.7
217	San Marino	402.1	564.4
218	Serbia	108.8	100.3
219	Slovenia	99.6	103.2
220	Spain	78.6	93.7
221	Austria	93.7	108.7
222	Belgium	330.5	381.1
223	France	103.5	118.9
224	Germany	226.8	239.6
225	Liechtenstein	179.9	237.6
226	Luxembourg	147.4	237.7
227	Monaco	19753.7	26152.3
228	Netherlands	443.8	507.0
229	Switzerland	168.4	217.4
230	Bermuda	1232.6	1250.2
231	Canada	3.0	4.1
232	Greenland	0.1	0.1
233	Saint Pierre and Miquelon	27.8	25.3
234	United States of America	27.6	36.0