# pyecharts1026 **Repository Path**: NFUNM089/pyecharts1026 ## Basic Information - **Project Name**: pyecharts1026 - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-10-26 - **Last Updated**: 2024-10-29 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README Untitled
In [1]:
from pyecharts.faker import Faker
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.globals import ChartType, SymbolType
In [2]:
import pandas as pd
In [44]:
df = pd.read_csv("bird.csv",encoding='gbk')
In [45]:
df
Out[45]:
Country 2018
0 Aruba 2.0
1 Afghanistan 16.0
2 Angola 32.0
3 Albania 8.0
4 Andorra 3.0
5 Arab World 319.0
6 United Arab Emirates 13.0
7 Argentina 52.0
8 Armenia 14.0
9 American Samoa 8.0
10 Antigua and Barbuda 3.0
11 Australia 52.0
12 Austria 13.0
13 Azerbaijan 17.0
14 Burundi 15.0
15 Belgium 8.0
16 Benin 12.0
17 Burkina Faso 12.0
18 Bangladesh 36.0
19 Bulgaria 17.0
20 Bahrain 7.0
21 Bahamas, The 10.0
22 Bosnia and Herzegovina 7.0
23 Belarus 9.0
24 Belize 6.0
25 Bermuda 3.0
26 Bolivia 55.0
27 Brazil 175.0
28 Barbados 4.0
29 Brunei Darussalam 31.0
... ... ...
234 Latin America & the Caribbean (IDA & IBRD coun... 1054.0
235 Timor-Leste 6.0
236 Middle East & North Africa (IDA & IBRD countries) 181.0
237 Tonga 5.0
238 South Asia (IDA & IBRD) 253.0
239 Sub-Saharan Africa (IDA & IBRD countries) 993.0
240 Trinidad and Tobago 5.0
241 Tunisia 11.0
242 Turkey 20.0
243 Tuvalu 1.0
244 Tanzania 49.0
245 Uganda 30.0
246 Ukraine 17.0
247 Upper middle income 1632.0
248 Uruguay 22.0
249 United States 91.0
250 Uzbekistan 19.0
251 St. Vincent and the Grenadines 4.0
252 Venezuela, RB 52.0
253 British Virgin Islands 3.0
254 Virgin Islands (U.S.) 3.0
255 Vietnam 52.0
256 Vanuatu 8.0
257 World NaN
258 Samoa 6.0
259 Kosovo NaN
260 Yemen, Rep. 16.0
261 South Africa 54.0
262 Zambia 20.0
263 Zimbabwe 19.0

264 rows × 2 columns

In [46]:
country=list(df['Country'])
country
Out[46]:
['Aruba',
 'Afghanistan',
 'Angola',
 'Albania',
 'Andorra',
 'Arab World',
 'United Arab Emirates',
 'Argentina',
 'Armenia',
 'American Samoa',
 'Antigua and Barbuda',
 'Australia',
 'Austria',
 'Azerbaijan',
 'Burundi',
 'Belgium',
 'Benin',
 'Burkina Faso',
 'Bangladesh',
 'Bulgaria',
 'Bahrain',
 'Bahamas, The',
 'Bosnia and Herzegovina',
 'Belarus',
 'Belize',
 'Bermuda',
 'Bolivia',
 'Brazil',
 'Barbados',
 'Brunei Darussalam',
 'Bhutan',
 'Botswana',
 'Central African Republic',
 'Canada',
 'Central Europe and the Baltics',
 'Switzerland',
 'Channel Islands',
 'Chile',
 'China',
 "Cote d'Ivoire",
 'Cameroon',
 'Congo, Dem. Rep.',
 'Congo, Rep.',
 'Colombia',
 'Comoros',
 'Cabo Verde',
 'Costa Rica',
 'Caribbean small states',
 'Cuba',
 'Curacao',
 'Cayman Islands',
 'Cyprus',
 'Czech Republic',
 'Germany',
 'Djibouti',
 'Dominica',
 'Denmark',
 'Dominican Republic',
 'Algeria',
 'East Asia & Pacific (excluding high income)',
 'Early-demographic dividend',
 'East Asia & Pacific',
 'Europe & Central Asia (excluding high income)',
 'Europe & Central Asia',
 'Ecuador',
 'Egypt, Arab Rep.',
 'Euro area',
 'Eritrea',
 'Spain',
 'Estonia',
 'Ethiopia',
 'European Union',
 'Fragile and conflict affected situations',
 'Finland',
 'Fiji',
 'France',
 'Faroe Islands',
 'Micronesia, Fed. Sts.',
 'Gabon',
 'United Kingdom',
 'Georgia',
 'Ghana',
 'Gibraltar',
 'Guinea',
 'Gambia, The',
 'Guinea-Bissau',
 'Equatorial Guinea',
 'Greece',
 'Grenada',
 'Greenland',
 'Guatemala',
 'Guam',
 'Guyana',
 'High income',
 'Hong Kong SAR, China',
 'Honduras',
 'Heavily indebted poor countries (HIPC)',
 'Croatia',
 'Haiti',
 'Hungary',
 'IBRD only',
 'IDA & IBRD total',
 'IDA total',
 'IDA blend',
 'Indonesia',
 'IDA only',
 'Isle of Man',
 'India',
 'Not classified',
 'Ireland',
 'Iran, Islamic Rep.',
 'Iraq',
 'Iceland',
 'Israel',
 'Italy',
 'Jamaica',
 'Jordan',
 'Japan',
 'Kazakhstan',
 'Kenya',
 'Kyrgyz Republic',
 'Cambodia',
 'Kiribati',
 'St. Kitts and Nevis',
 'Korea, Rep.',
 'Kuwait',
 'Latin America & Caribbean (excluding high income)',
 'Lao PDR',
 'Lebanon',
 'Liberia',
 'Libya',
 'St. Lucia',
 'Latin America & Caribbean',
 'Least developed countries: UN classification',
 'Low income',
 'Liechtenstein',
 'Sri Lanka',
 'Lower middle income',
 'Low & middle income',
 'Lesotho',
 'Late-demographic dividend',
 'Lithuania',
 'Luxembourg',
 'Latvia',
 'Macao SAR, China',
 'St. Martin (French part)',
 'Morocco',
 'Monaco',
 'Moldova',
 'Madagascar',
 'Maldives',
 'Middle East & North Africa',
 'Mexico',
 'Marshall Islands',
 'Middle income',
 'North Macedonia',
 'Mali',
 'Malta',
 'Myanmar',
 'Middle East & North Africa (excluding high income)',
 'Montenegro',
 'Mongolia',
 'Northern Mariana Islands',
 'Mozambique',
 'Mauritania',
 'Mauritius',
 'Malawi',
 'Malaysia',
 'North America',
 'Namibia',
 'New Caledonia',
 'Niger',
 'Nigeria',
 'Nicaragua',
 'Netherlands',
 'Norway',
 'Nepal',
 'Nauru',
 'New Zealand',
 'OECD members',
 'Oman',
 'Other small states',
 'Pakistan',
 'Panama',
 'Peru',
 'Philippines',
 'Palau',
 'Papua New Guinea',
 'Poland',
 'Pre-demographic dividend',
 'Puerto Rico',
 'Korea, Dem. People鈥檚 Rep.',
 'Portugal',
 'Paraguay',
 'West Bank and Gaza',
 'Pacific island small states',
 'Post-demographic dividend',
 'French Polynesia',
 'Qatar',
 'Romania',
 'Russia',
 'Rwanda',
 'South Asia',
 'Saudi Arabia',
 'Sudan',
 'Senegal',
 'Singapore',
 'Solomon Islands',
 'Sierra Leone',
 'El Salvador',
 'San Marino',
 'Somalia',
 'Serbia',
 'Sub-Saharan Africa (excluding high income)',
 'South Sudan',
 'Sub-Saharan Africa',
 'Small states',
 'Sao Tome and Principe',
 'Suriname',
 'Slovak Republic',
 'Slovenia',
 'Sweden',
 'Eswatini',
 'Sint Maarten (Dutch part)',
 'Seychelles',
 'Syrian Arab Republic',
 'Turks and Caicos Islands',
 'Chad',
 'East Asia & Pacific (IDA & IBRD countries)',
 'Europe & Central Asia (IDA & IBRD countries)',
 'Togo',
 'Thailand',
 'Tajikistan',
 'Turkmenistan',
 'Latin America & the Caribbean (IDA & IBRD countries)',
 'Timor-Leste',
 'Middle East & North Africa (IDA & IBRD countries)',
 'Tonga',
 'South Asia (IDA & IBRD)',
 'Sub-Saharan Africa (IDA & IBRD countries)',
 'Trinidad and Tobago',
 'Tunisia',
 'Turkey',
 'Tuvalu',
 'Tanzania',
 'Uganda',
 'Ukraine',
 'Upper middle income',
 'Uruguay',
 'United States',
 'Uzbekistan',
 'St. Vincent and the Grenadines',
 'Venezuela, RB',
 'British Virgin Islands',
 'Virgin Islands (U.S.)',
 'Vietnam',
 'Vanuatu',
 'World',
 'Samoa',
 'Kosovo',
 'Yemen, Rep.',
 'South Africa',
 'Zambia',
 'Zimbabwe']
In [47]:
a=list(df['2018'])
a
Out[47]:
[2.0,
 16.0,
 32.0,
 8.0,
 3.0,
 319.0,
 13.0,
 52.0,
 14.0,
 8.0,
 3.0,
 52.0,
 13.0,
 17.0,
 15.0,
 8.0,
 12.0,
 12.0,
 36.0,
 17.0,
 7.0,
 10.0,
 7.0,
 9.0,
 6.0,
 3.0,
 55.0,
 175.0,
 4.0,
 31.0,
 21.0,
 16.0,
 16.0,
 24.0,
 133.0,
 9.0,
 nan,
 35.0,
 96.0,
 25.0,
 29.0,
 42.0,
 7.0,
 126.0,
 14.0,
 7.0,
 27.0,
 87.0,
 19.0,
 2.0,
 3.0,
 7.0,
 9.0,
 11.0,
 12.0,
 7.0,
 9.0,
 17.0,
 15.0,
 830.0,
 1792.0,
 1224.0,
 342.0,
 678.0,
 106.0,
 14.0,
 213.0,
 21.0,
 19.0,
 9.0,
 35.0,
 325.0,
 620.0,
 11.0,
 14.0,
 16.0,
 6.0,
 12.0,
 7.0,
 11.0,
 14.0,
 23.0,
 6.0,
 20.0,
 14.0,
 12.0,
 6.0,
 17.0,
 2.0,
 6.0,
 17.0,
 14.0,
 16.0,
 1092.0,
 21.0,
 14.0,
 823.0,
 14.0,
 17.0,
 13.0,
 2241.0,
 3647.0,
 1406.0,
 293.0,
 160.0,
 1113.0,
 0.0,
 93.0,
 nan,
 9.0,
 28.0,
 17.0,
 7.0,
 18.0,
 17.0,
 11.0,
 14.0,
 49.0,
 27.0,
 44.0,
 15.0,
 31.0,
 6.0,
 3.0,
 33.0,
 11.0,
 980.0,
 29.0,
 11.0,
 14.0,
 8.0,
 7.0,
 1117.0,
 982.0,
 663.0,
 2.0,
 16.0,
 1286.0,
 3581.0,
 8.0,
 1303.0,
 10.0,
 3.0,
 11.0,
 4.0,
 1.0,
 18.0,
 0.0,
 11.0,
 37.0,
 0.0,
 290.0,
 71.0,
 4.0,
 2918.0,
 13.0,
 17.0,
 5.0,
 56.0,
 196.0,
 13.0,
 24.0,
 17.0,
 30.0,
 19.0,
 12.0,
 19.0,
 63.0,
 118.0,
 32.0,
 17.0,
 13.0,
 21.0,
 17.0,
 10.0,
 11.0,
 38.0,
 2.0,
 69.0,
 754.0,
 13.0,
 293.0,
 33.0,
 25.0,
 119.0,
 93.0,
 6.0,
 39.0,
 11.0,
 761.0,
 11.0,
 29.0,
 15.0,
 27.0,
 15.0,
 88.0,
 693.0,
 35.0,
 9.0,
 17.0,
 57.0,
 20.0,
 253.0,
 18.0,
 26.0,
 19.0,
 22.0,
 24.0,
 16.0,
 7.0,
 0.0,
 21.0,
 13.0,
 980.0,
 21.0,
 993.0,
 468.0,
 12.0,
 9.0,
 12.0,
 10.0,
 11.0,
 13.0,
 1.0,
 13.0,
 17.0,
 4.0,
 16.0,
 799.0,
 367.0,
 13.0,
 62.0,
 15.0,
 19.0,
 1054.0,
 6.0,
 181.0,
 5.0,
 253.0,
 993.0,
 5.0,
 11.0,
 20.0,
 1.0,
 49.0,
 30.0,
 17.0,
 1632.0,
 22.0,
 91.0,
 19.0,
 4.0,
 52.0,
 3.0,
 3.0,
 52.0,
 8.0,
 nan,
 6.0,
 nan,
 16.0,
 54.0,
 20.0,
 19.0]
In [48]:
zip的结果 = list(zip(country,a))
In [50]:
def map_world() -> Map:
    c = (
        Map()
        .add("2018年世界各国受胁鸟类数量", zip的结果, "world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="2018年世界各国受胁鸟类数量"),
            visualmap_opts=opts.VisualMapOpts(max_=137),
        )
    )
    return c
q = map_world()
q.render_notebook()
Out[50]:
In [69]:
df1 = pd.read_csv("FRST.csv")
In [70]:
df1
Out[70]:
Country 2018年
0 Aruba 4.200000e+00
1 Afghanistan 1.350000e+04
2 Angola 5.773120e+05
3 Albania 7.705400e+03
4 Andorra 1.600000e+02
5 Arab World 3.793290e+05
6 United Arab Emirates 3.236600e+03
7 Argentina 2.681520e+05
8 Armenia 3.322000e+03
9 American Samoa 1.750000e+02
10 Antigua and Barbuda 9.800000e+01
11 Australia 1.250590e+06
12 Austria 3.870800e+04
13 Azerbaijan 1.165620e+04
14 Burundi 2.806000e+03
15 Belgium 6.838400e+03
16 Benin 4.261000e+04
17 Burkina Faso 5.290200e+04
18 Bangladesh 1.426400e+04
19 Bulgaria 3.840200e+04
20 Bahrain 6.100000e+00
21 Bahamas, The 5.150000e+03
22 Bosnia and Herzegovina 2.185000e+04
23 Belarus 8.653400e+04
24 Belize 1.361280e+04
25 Bermuda 1.000000e+01
26 Bolivia 5.447500e+05
27 Brazil 4.925540e+06
28 Barbados 6.300000e+01
29 Brunei Darussalam 3.800000e+03
... ... ...
234 Latin America & the Caribbean (IDA & IBRD coun... 9.207158e+06
235 Timor-Leste 6.748000e+03
236 Middle East & North Africa (IDA & IBRD countries) 2.172686e+05
237 Tonga 9.000000e+01
238 South Asia (IDA & IBRD) 8.353105e+05
239 Sub-Saharan Africa (IDA & IBRD countries) 6.115291e+06
240 Trinidad and Tobago 2.361000e+03
241 Tunisia 1.051200e+04
242 Turkey 1.181740e+05
243 Tuvalu 1.000000e+01
244 Tanzania 4.568800e+05
245 Uganda 1.941800e+04
246 Ukraine 9.678800e+04
247 Upper middle income 2.019530e+07
248 Uruguay 1.867740e+04
249 United States 3.103700e+06
250 Uzbekistan 3.208780e+04
251 St. Vincent and the Grenadines 2.700000e+02
252 Venezuela, RB 4.651860e+05
253 British Virgin Islands 3.620000e+01
254 Virgin Islands (U.S.) 1.749000e+02
255 Vietnam 1.490200e+05
256 Vanuatu 4.400000e+03
257 World 3.995825e+07
258 Samoa 1.710000e+03
259 Kosovo NaN
260 Yemen, Rep. 5.490000e+03
261 South Africa 9.241000e+04
262 Zambia 4.846840e+05
263 Zimbabwe 1.374960e+05

264 rows × 2 columns

In [71]:
country=list(df1['Country'])
country
Out[71]:
['Aruba',
 'Afghanistan',
 'Angola',
 'Albania',
 'Andorra',
 'Arab World',
 'United Arab Emirates',
 'Argentina',
 'Armenia',
 'American Samoa',
 'Antigua and Barbuda',
 'Australia',
 'Austria',
 'Azerbaijan',
 'Burundi',
 'Belgium',
 'Benin',
 'Burkina Faso',
 'Bangladesh',
 'Bulgaria',
 'Bahrain',
 'Bahamas, The',
 'Bosnia and Herzegovina',
 'Belarus',
 'Belize',
 'Bermuda',
 'Bolivia',
 'Brazil',
 'Barbados',
 'Brunei Darussalam',
 'Bhutan',
 'Botswana',
 'Central African Republic',
 'Canada',
 'Central Europe and the Baltics',
 'Switzerland',
 'Channel Islands',
 'Chile',
 'China',
 "Cote d'Ivoire",
 'Cameroon',
 'Congo, Dem. Rep.',
 'Congo, Rep.',
 'Colombia',
 'Comoros',
 'Cabo Verde',
 'Costa Rica',
 'Caribbean small states',
 'Cuba',
 'Curacao',
 'Cayman Islands',
 'Cyprus',
 'Czech Republic',
 'Germany',
 'Djibouti',
 'Dominica',
 'Denmark',
 'Dominican Republic',
 'Algeria',
 'East Asia & Pacific (excluding high income)',
 'Early-demographic dividend',
 'East Asia & Pacific',
 'Europe & Central Asia (excluding high income)',
 'Europe & Central Asia',
 'Ecuador',
 'Egypt, Arab Rep.',
 'Euro area',
 'Eritrea',
 'Spain',
 'Estonia',
 'Ethiopia',
 'European Union',
 'Fragile and conflict affected situations',
 'Finland',
 'Fiji',
 'France',
 'Faroe Islands',
 'Micronesia, Fed. Sts.',
 'Gabon',
 'United Kingdom',
 'Georgia',
 'Ghana',
 'Gibraltar',
 'Guinea',
 'Gambia, The',
 'Guinea-Bissau',
 'Equatorial Guinea',
 'Greece',
 'Grenada',
 'Greenland',
 'Guatemala',
 'Guam',
 'Guyana',
 'High income',
 'Hong Kong SAR, China',
 'Honduras',
 'Heavily indebted poor countries (HIPC)',
 'Croatia',
 'Haiti',
 'Hungary',
 'IBRD only',
 'IDA & IBRD total',
 'IDA total',
 'IDA blend',
 'Indonesia',
 'IDA only',
 'Isle of Man',
 'India',
 'Not classified',
 'Ireland',
 'Iran, Islamic Rep.',
 'Iraq',
 'Iceland',
 'Israel',
 'Italy',
 'Jamaica',
 'Jordan',
 'Japan',
 'Kazakhstan',
 'Kenya',
 'Kyrgyz Republic',
 'Cambodia',
 'Kiribati',
 'St. Kitts and Nevis',
 'Korea, Rep.',
 'Kuwait',
 'Latin America & Caribbean (excluding high income)',
 'Lao PDR',
 'Lebanon',
 'Liberia',
 'Libya',
 'St. Lucia',
 'Latin America & Caribbean',
 'Least developed countries: UN classification',
 'Low income',
 'Liechtenstein',
 'Sri Lanka',
 'Lower middle income',
 'Low & middle income',
 'Lesotho',
 'Late-demographic dividend',
 'Lithuania',
 'Luxembourg',
 'Latvia',
 'Macao SAR, China',
 'St. Martin (French part)',
 'Morocco',
 'Monaco',
 'Moldova',
 'Madagascar',
 'Maldives',
 'Middle East & North Africa',
 'Mexico',
 'Marshall Islands',
 'Middle income',
 'North Macedonia',
 'Mali',
 'Malta',
 'Myanmar',
 'Middle East & North Africa (excluding high income)',
 'Montenegro',
 'Mongolia',
 'Northern Mariana Islands',
 'Mozambique',
 'Mauritania',
 'Mauritius',
 'Malawi',
 'Malaysia',
 'North America',
 'Namibia',
 'New Caledonia',
 'Niger',
 'Nigeria',
 'Nicaragua',
 'Netherlands',
 'Norway',
 'Nepal',
 'Nauru',
 'New Zealand',
 'OECD members',
 'Oman',
 'Other small states',
 'Pakistan',
 'Panama',
 'Peru',
 'Philippines',
 'Palau',
 'Papua New Guinea',
 'Poland',
 'Pre-demographic dividend',
 'Puerto Rico',
 'Korea, Dem. People’s Rep.',
 'Portugal',
 'Paraguay',
 'West Bank and Gaza',
 'Pacific island small states',
 'Post-demographic dividend',
 'French Polynesia',
 'Qatar',
 'Romania',
 'Russia',
 'Rwanda',
 'South Asia',
 'Saudi Arabia',
 'Sudan',
 'Senegal',
 'Singapore',
 'Solomon Islands',
 'Sierra Leone',
 'El Salvador',
 'San Marino',
 'Somalia',
 'Serbia',
 'Sub-Saharan Africa (excluding high income)',
 'South Sudan',
 'Sub-Saharan Africa',
 'Small states',
 'Sao Tome and Principe',
 'Suriname',
 'Slovak Republic',
 'Slovenia',
 'Sweden',
 'Eswatini',
 'Sint Maarten (Dutch part)',
 'Seychelles',
 'Syrian Arab Republic',
 'Turks and Caicos Islands',
 'Chad',
 'East Asia & Pacific (IDA & IBRD countries)',
 'Europe & Central Asia (IDA & IBRD countries)',
 'Togo',
 'Thailand',
 'Tajikistan',
 'Turkmenistan',
 'Latin America & the Caribbean (IDA & IBRD countries)',
 'Timor-Leste',
 'Middle East & North Africa (IDA & IBRD countries)',
 'Tonga',
 'South Asia (IDA & IBRD)',
 'Sub-Saharan Africa (IDA & IBRD countries)',
 'Trinidad and Tobago',
 'Tunisia',
 'Turkey',
 'Tuvalu',
 'Tanzania',
 'Uganda',
 'Ukraine',
 'Upper middle income',
 'Uruguay',
 'United States',
 'Uzbekistan',
 'St. Vincent and the Grenadines',
 'Venezuela, RB',
 'British Virgin Islands',
 'Virgin Islands (U.S.)',
 'Vietnam',
 'Vanuatu',
 'World',
 'Samoa',
 'Kosovo',
 'Yemen, Rep.',
 'South Africa',
 'Zambia',
 'Zimbabwe']
In [72]:
b=list(df1['2018年'])
b
Out[72]:
[4.199999869,
 13500.0,
 577311.9922,
 7705.39978,
 160.0,
 379328.9942,
 3236.600037,
 268151.9922,
 3322.000122,
 175.0,
 98.00000191,
 1250590.0,
 38708.00049,
 11656.19995,
 2806.0000609999997,
 6838.400269,
 42610.0,
 52902.00195,
 14264.000240000001,
 38401.99951,
 6.100000143,
 5150.0,
 21850.0,
 86534.00391,
 13612.800290000001,
 10.0,
 544750.0,
 4925540.0,
 63.00000191,
 3800.0,
 27648.50098,
 107377.998,
 221544.0039,
 3470224.063,
 378500.0037,
 12578.00049,
 8.000000119,
 180358.0078,
 2098635.0,
 104005.9961,
 185960.0,
 1522665.938,
 223185.9961,
 584750.1953,
 365.9999847,
 908.6000061000001,
 27861.99951,
 344257.9024,
 32536.00098,
 nan,
 126.99999809999998,
 1726.699982,
 26690.0,
 114210.0,
 55.99999905,
 430.60001370000003,
 6172.199707,
 20161.999509999998,
 19635.99976,
 4743036.487,
 7234343.966,
 6421326.392000001,
 8797314.406,
 10438609.31,
 124691.5039,
 735.9999847,
 1027076.598,
 15055.999759999999,
 184520.0,
 22316.00098,
 125395.9961,
 1614504.803,
 3777535.626,
 222180.0,
 10220.599979999999,
 171020.0,
 0.799999982,
 643.0000305,
 232000.0,
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In [73]:
zip1的结果 = list(zip(country,b))
In [77]:
def map_world() -> Map:
    c = (
        Map()
        .add("2018年各国森林面积", zip1的结果, "world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="2018年各国森林面积"),
            visualmap_opts=opts.VisualMapOpts(min_=89942,max_=2098635),
        )
    )
    return c
q = map_world()
q.render_notebook()
Out[77]:

故事

猜想：各国的森林面积越大，受胁鸟类数量越少。从已有数据来看，森林面积和受胁鸟类不能成反比的关系，如澳大利亚，森林面积在中等位置，受胁鸟类数量排名也在中等位置。但鸟类的受胁数量和森林面积的大小对有一定的关系，还可能和气候的变化和二氧化碳的排放量有关系。

建议：各国应该尽量保护森林，禁止对国家保护鸟类的杀害，加强对受胁鸟类的保护，以防灭绝。