# 世界地图 **Repository Path**: NFUNM107/worldmap123 ## Basic Information - **Project Name**: 世界地图 - **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**: 2024-11-15 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README 世界地图

数据内容

数据：1.各国青春期生育率 2.中小学女男入学比例。 数据来源：世界银行。

数据故事

预想：

1.非洲青春期生育数会高 2.非洲女生入学率低 3.青春期生育率低与当地女性的地位有关

从地图可以看出：

图中可以看出非洲地区的青春期生育很高，并且尤为明显。其他地区都基本为0，非洲却上百。 女男入学率也是非洲地区低，意思是在非洲只有少数女孩能进入学校上学。 非洲的为人熟知的是女性地位的低下，频发的性侵事件，当女性独自出门会不太安全。 非洲男女地位的悬殊体现在中小学入学女性的占比小，并且青春期生育也是各自家庭对女性健康的忽视。

In [1]:
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("商家A", [list(z) for z in zip(Faker.country, Faker.values())], "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_=200),
        )
    )
    return c
In [2]:
世界地图 = map_world()
世界地图.render_notebook()
Out[2]:
In [51]:
import pandas as pd
df = pd.read_csv('data\shengyu.csv',encoding='gbk')
In [52]:
df
Out[52]:
country Country Code rate
0 Aruba ABW 22.67
1 Afghanistan AFG 68.96
2 Angola AGO 150.53
3 Albania ALB 19.64
4 Andorra AND NaN
5 Arab World ARB 46.69
6 United Arab Emirates ARE 6.55
7 Argentina ARG 62.78
8 Armenia ARM 21.49
9 American Samoa ASM NaN
10 Antigua and Barbuda ATG 42.78
11 Australia AUS 11.72
12 Austria AUT 7.34
13 Azerbaijan AZE 55.84
14 Burundi BDI 55.59
15 Belgium BEL 4.65
16 Benin BEN 86.10
17 Burkina Faso BFA 104.33
18 Bangladesh BGD 82.96
19 Bulgaria BGR 39.86
20 Bahrain BHR 13.37
21 Bahamas, The BHS 30.00
22 Bosnia and Herzegovina BIH 9.64
23 Belarus BLR 14.51
24 Belize BLZ 68.49
25 Bermuda BMU NaN
26 Bolivia BOL 64.90
27 Brazil BRA 59.11
28 Barbados BRB 33.55
29 Brunei Darussalam BRN 10.27
... ... ... ...
234 Latin America & the Caribbean (IDA & IBRD coun... TLA 63.37
235 Timor-Leste TLS 33.79
236 Middle East & North Africa (IDA & IBRD countries) TMN 43.75
237 Tonga TON 14.66
238 South Asia (IDA & IBRD) TSA 25.58
239 Sub-Saharan Africa (IDA & IBRD countries) TSS 102.79
240 Trinidad and Tobago TTO 30.09
241 Tunisia TUN 7.84
242 Turkey TUR 26.56
243 Tuvalu TUV NaN
244 Tanzania TZA 118.39
245 Uganda UGA 118.84
246 Ukraine UKR 23.71
247 Upper middle income UMC 30.73
248 Uruguay URY 58.73
249 United States USA 19.86
250 Uzbekistan UZB 23.79
251 St. Vincent and the Grenadines VCT 49.02
252 Venezuela, RB VEN 85.34
253 British Virgin Islands VGB NaN
254 Virgin Islands (U.S.) VIR 28.89
255 Vietnam VNM 30.93
256 Vanuatu VUT 49.44
257 World WLD 42.46
258 Samoa WSM 23.89
259 Kosovo XKX NaN
260 Yemen, Rep. YEM 60.35
261 South Africa ZAF 67.91
262 Zambia ZMB 120.11
263 Zimbabwe ZWE 86.14

264 rows × 3 columns

In [53]:
各国青春期生育 = list(zip(list(df.country),list(df.rate)))
print(各国青春期生育)

[('Aruba', 22.67), ('Afghanistan', 68.96), ('Angola', 150.53), ('Albania', 19.64), ('Andorra', nan), ('Arab World', 46.69), ('United Arab Emirates', 6.55), ('Argentina', 62.78), ('Armenia', 21.49), ('American Samoa', nan), ('Antigua and Barbuda', 42.78), ('Australia', 11.72), ('Austria', 7.34), ('Azerbaijan', 55.84), ('Burundi', 55.59), ('Belgium', 4.65), ('Benin', 86.1), ('Burkina Faso', 104.33), ('Bangladesh', 82.96), ('Bulgaria', 39.86), ('Bahrain', 13.37), ('Bahamas, The', 30.0), ('Bosnia and Herzegovina', 9.64), ('Belarus', 14.51), ('Belize', 68.49), ('Bermuda', nan), ('Bolivia', 64.9), ('Brazil', 59.11), ('Barbados', 33.55), ('Brunei Darussalam', 10.27), ('Bhutan', 20.18), ('Botswana', 46.06), ('Central African Republic', 129.07), ('Canada', 8.39), ('Central Europe and the Baltics', 19.68), ('Switzerland', 2.76), ('Channel Islands', 6.86), ('Chile', 41.05), ('China', 7.64), ("Cote d'Ivoire", 117.63), ('Cameroon', 105.8), ('Congo, Dem. Rep.', 124.22), ('Congo, Rep.', 112.23), ('Colombia', 66.65), ('Comoros', 65.35), ('Cabo Verde', 73.76), ('Costa Rica', 53.46), ('Caribbean small states', 51.48), ('Cuba', 51.59), ('Curacao', 27.94), ('Cayman Islands', nan), ('Cyprus', 4.58), ('Czech Republic', 11.97), ('Germany', 8.1), ('Djibouti', 18.84), ('Dominica', nan), ('Denmark', 4.12), ('Dominican Republic', 94.26), ('Algeria', 10.07), ('East Asia & Pacific (excluding high income)', 22.14), ('Early-demographic dividend', 36.98), ('East Asia & Pacific', 20.67), ('Europe & Central Asia (excluding high income)', 26.46), ('Europe & Central Asia', 17.09), ('Ecuador', 79.26), ('Egypt, Arab Rep.', 53.82), ('Euro area', 6.67), ('Eritrea', 52.55), ('Spain', 7.73), ('Estonia', 7.7), ('Ethiopia', 66.73), ('European Union', 9.74), ('Fragile and conflict affected situations', 87.17), ('Finland', 5.81), ('Fiji', 49.35), ('France', 4.73), ('Faroe Islands', nan), ('Micronesia, Fed. Sts.', 13.92), ('Gabon', 96.23), ('United Kingdom', 13.37), ('Georgia', 46.41), ('Ghana', 66.61), ('Gibraltar', nan), ('Guinea', 135.29), ('Gambia, The', 78.19), ('Guinea-Bissau', 104.82), ('Equatorial Guinea', 155.62), ('Greece', 7.22), ('Grenada', 29.18), ('Greenland', nan), ('Guatemala', 70.93), ('Guam', 31.71), ('Guyana', 74.38), ('High income', 12.51), ('Hong Kong SAR, China', 2.71), ('Honduras', 72.91), ('Heavily indebted poor countries (HIPC)', 101.61), ('Croatia', 8.68), ('Haiti', 51.68), ('Hungary', 23.98), ('IBRD only', 28.25), ('IDA & IBRD total', 46.36), ('IDA total', 84.46), ('IDA blend', 71.69), ('Indonesia', 47.37), ('IDA only', 90.69), ('Isle of Man', nan), ('India', 13.18), ('Not classified', nan), ('Ireland', 7.52), ('Iran, Islamic Rep.', 40.64), ('Iraq', 71.73), ('Iceland', 6.27), ('Israel', 9.61), ('Italy', 5.24), ('Jamaica', 52.76), ('Jordan', 25.88), ('Japan', 3.78), ('Kazakhstan', 29.75), ('Kenya', 75.08), ('Kyrgyz Republic', 32.76), ('Cambodia', 50.18), ('Kiribati', 16.18), ('St. Kitts and Nevis', nan), ('Korea, Rep.', 1.38), ('Kuwait', 8.19), ('Latin America & Caribbean (excluding high income)', 63.72), ('Lao PDR', 65.41), ('Lebanon', 14.5), ('Liberia', 135.96), ('Libya', 5.77), ('St. Lucia', 40.54), ('Latin America & Caribbean', 63.04), ('Least developed countries: UN classification', 94.0), ('Low income', 97.52), ('Liechtenstein', nan), ('Sri Lanka', 20.93), ('Lower middle income', 42.16), ('Low & middle income', 46.33), ('Lesotho', 92.73), ('Late-demographic dividend', 21.03), ('Lithuania', 10.85), ('Luxembourg', 4.73), ('Latvia', 16.17), ('Macao SAR, China', 2.38), ('St. Martin (French part)', nan), ('Morocco', 31.03), ('Monaco', nan), ('Moldova', 22.42), ('Madagascar', 109.59), ('Maldives', 7.81), ('Middle East & North Africa', 39.97), ('Mexico', 60.37), ('Marshall Islands', nan), ('Middle income', 37.72), ('North Macedonia', 15.72), ('Mali', 169.13), ('Malta', 12.88), ('Myanmar', 28.51), ('Middle East & North Africa (excluding high income)', 43.89), ('Montenegro', 9.31), ('Mongolia', 30.99), ('Northern Mariana Islands', nan), ('Mozambique', 148.63), ('Mauritania', 71.05), ('Mauritius', 25.74), ('Malawi', 132.67), ('Malaysia', 13.41), ('North America', 18.86), ('Namibia', 63.63), ('New Caledonia', 14.83), ('Niger', 186.54), ('Nigeria', 107.33), ('Nicaragua', 84.99), ('Netherlands', 3.79), ('Norway', 5.14), ('Nepal', 65.12), ('Nauru', nan), ('New Zealand', 19.27), ('OECD members', 20.35), ('Oman', 13.06), ('Other small states', 61.55), ('Pakistan', 38.8), ('Panama', 81.83), ('Peru', 56.88), ('Philippines', 54.15), ('Palau', nan), ('Papua New Guinea', 52.66), ('Poland', 10.54), ('Pre-demographic dividend', 108.93), ('Puerto Rico', 29.1), ('Korea, Dem. People’s Rep.', 0.28), ('Portugal', 8.38), ('Paraguay', 70.5), ('West Bank and Gaza', 52.77), ('Pacific island small states', 50.42), ('Post-demographic dividend', 12.65), ('French Polynesia', 38.69), ('Qatar', 9.92), ('Romania', 36.21), ('Russian Federation', 20.7), ('Rwanda', 39.11), ('South Asia', 25.58), ('Saudi Arabia', 7.32), ('Sudan', 63.99), ('Senegal', 72.73), ('Singapore', 3.53), ('Solomon Islands', 78.0), ('Sierra Leone', 112.83), ('El Salvador', 69.46), ('San Marino', nan), ('Somalia', 100.09), ('Serbia', 14.71), ('Sub-Saharan Africa (excluding high income)', 102.79), ('South Sudan', 62.04), ('Sub-Saharan Africa', 102.79), ('Small states', 59.02), ('Sao Tome and Principe', 94.61), ('Suriname', 61.66), ('Slovak Republic', 25.68), ('Slovenia', 3.78), ('Sweden', 5.08), ('Eswatini', 76.7), ('Sint Maarten (Dutch part)', nan), ('Seychelles', 62.05), ('Syrian Arab Republic', 38.59), ('Turks and Caicos Islands', nan), ('Chad', 161.09), ('East Asia & Pacific (IDA & IBRD countries)', 22.45), ('Europe & Central Asia (IDA & IBRD countries)', 25.22), ('Togo', 89.09), ('Thailand', 44.91), ('Tajikistan', 57.08), ('Turkmenistan', 24.42), ('Latin America & the Caribbean (IDA & IBRD countries)', 63.37), ('Timor-Leste', 33.79), ('Middle East & North Africa (IDA & IBRD countries)', 43.75), ('Tonga', 14.66), ('South Asia (IDA & IBRD)', 25.58), ('Sub-Saharan Africa (IDA & IBRD countries)', 102.79), ('Trinidad and Tobago', 30.09), ('Tunisia', 7.84), ('Turkey', 26.56), ('Tuvalu', nan), ('Tanzania', 118.39), ('Uganda', 118.84), ('Ukraine', 23.71), ('Upper middle income', 30.73), ('Uruguay', 58.73), ('United States', 19.86), ('Uzbekistan', 23.79), ('St. Vincent and the Grenadines', 49.02), ('Venezuela, RB', 85.34), ('British Virgin Islands', nan), ('Virgin Islands (U.S.)', 28.89), ('Vietnam', 30.93), ('Vanuatu', 49.44), ('World', 42.46), ('Samoa', 23.89), ('Kosovo', nan), ('Yemen, Rep.', 60.35), ('South Africa', 67.91), ('Zambia', 120.11), ('Zimbabwe', 86.14)]
In [54]:
df.rate.min()
Out[54]:
0.28
In [55]:
df.rate.max()
Out[55]:
186.54
In [56]:
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.globals import ChartType, SymbolType

def map_world() -> Map:
    c = (
        Map()
        .add("青春期生育数", 各国青春期生育, "world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="各国青春期生育图"),
            visualmap_opts=opts.VisualMapOpts(min_=0.28,max_=186.54),
        )
    )
    return c
In [57]:
各国青春期生育率图= map_world()
各国青春期生育率图.render_notebook()
Out[57]:
In [58]:
import pandas as pd
df = pd.read_csv('data/ruxue.csv',encoding='gbk')
In [59]:
df
Out[59]:
country rate
0 Aruba NaN
1 Afghanistan 0.68
2 Angola NaN
3 Albania 1.03
4 Andorra NaN
5 Arab World 0.94
6 United Arab Emirates 0.98
7 Argentina 1.00
8 Armenia 1.00
9 American Samoa NaN
10 Antigua and Barbuda 0.98
11 Australia 1.00
12 Austria 1.00
13 Azerbaijan 1.02
14 Burundi 1.01
15 Belgium 1.00
16 Benin 0.94
17 Burkina Faso 0.98
18 Bangladesh NaN
19 Bulgaria NaN
20 Bahrain 1.00
21 Bahamas, The NaN
22 Bosnia and Herzegovina NaN
23 Belarus 0.99
24 Belize 0.95
25 Bermuda NaN
26 Bolivia 0.99
27 Brazil 0.97
28 Barbados 0.97
29 Brunei Darussalam 1.00
... ... ...
234 Latin America & the Caribbean (IDA & IBRD coun... 0.98
235 Timor-Leste 0.96
236 Middle East & North Africa (IDA & IBRD countries) 0.96
237 Tonga NaN
238 South Asia (IDA & IBRD) 1.08
239 Sub-Saharan Africa (IDA & IBRD countries) 0.96
240 Trinidad and Tobago NaN
241 Tunisia 1.00
242 Turkey 0.99
243 Tuvalu NaN
244 Tanzania 1.03
245 Uganda 1.03
246 Ukraine NaN
247 Upper middle income 1.00
248 Uruguay NaN
249 United States 0.99
250 Uzbekistan 0.99
251 St. Vincent and the Grenadines 0.98
252 Venezuela, RB 0.98
253 British Virgin Islands 0.96
254 Virgin Islands (U.S.) NaN
255 Vietnam 1.02
256 Vanuatu NaN
257 World 1.01
258 Samoa 1.00
259 Kosovo NaN
260 Yemen, Rep. NaN
261 South Africa 0.97
262 Zambia 1.02
263 Zimbabwe NaN

264 rows × 2 columns

In [41]:
各国中小学女男入学比例 = list(zip(list(df.country),list(df.rate)))
print(各国中小学女男入学比例)

[('Aruba', nan), ('Afghanistan', 0.68), ('Angola', nan), ('Albania', 1.03), ('Andorra', nan), ('Arab World', 0.94), ('United Arab Emirates', 0.98), ('Argentina', 1.0), ('Armenia', 1.0), ('American Samoa', nan), ('Antigua and Barbuda', 0.98), ('Australia', 1.0), ('Austria', 1.0), ('Azerbaijan', 1.02), ('Burundi', 1.01), ('Belgium', 1.0), ('Benin', 0.94), ('Burkina Faso', 0.98), ('Bangladesh', nan), ('Bulgaria', nan), ('Bahrain', 1.0), ('Bahamas, The', nan), ('Bosnia and Herzegovina', nan), ('Belarus', 0.99), ('Belize', 0.95), ('Bermuda', nan), ('Bolivia', 0.99), ('Brazil', 0.97), ('Barbados', 0.97), ('Brunei Darussalam', 1.0), ('Bhutan', 1.0), ('Botswana', nan), ('Central African Republic', nan), ('Canada', 1.0), ('Central Europe and the Baltics', 1.0), ('Switzerland', 0.99), ('Channel Islands', nan), ('Chile', 0.97), ('China', 1.01), ("Cote d'Ivoire", 0.91), ('Cameroon', nan), ('Congo, Dem. Rep.', nan), ('Congo, Rep.', nan), ('Colombia', 0.97), ('Comoros', 0.96), ('Cabo Verde', 0.93), ('Costa Rica', 1.01), ('Caribbean small states', 0.98), ('Cuba', 0.95), ('Curacao', nan), ('Cayman Islands', nan), ('Cyprus', nan), ('Czech Republic', 1.01), ('Germany', 1.0), ('Djibouti', 1.06), ('Dominica', nan), ('Denmark', 0.99), ('Dominican Republic', 0.93), ('Algeria', 0.95), ('East Asia & Pacific (excluding high income)', 1.0), ('Early-demographic dividend', 1.04), ('East Asia & Pacific', 1.0), ('Europe & Central Asia (excluding high income)', 1.0), ('Europe & Central Asia', 1.0), ('Ecuador', 1.01), ('Egypt, Arab Rep.', 1.0), ('Euro area', 1.0), ('Eritrea', 0.86), ('Spain', 1.02), ('Estonia', 1.0), ('Ethiopia', nan), ('European Union', 1.0), ('Fragile and conflict affected situations', 0.9), ('Finland', 0.99), ('Fiji', nan), ('France', 0.99), ('Faroe Islands', nan), ('Micronesia, Fed. Sts.', nan), ('Gabon', nan), ('United Kingdom', 1.0), ('Georgia', 1.01), ('Ghana', 1.01), ('Gibraltar', 1.0), ('Guinea', nan), ('Gambia, The', 1.08), ('Guinea-Bissau', nan), ('Equatorial Guinea', nan), ('Greece', 1.0), ('Grenada', 0.95), ('Greenland', nan), ('Guatemala', 0.97), ('Guam', nan), ('Guyana', nan), ('High income', 1.0), ('Hong Kong SAR, China', 1.04), ('Honduras', 1.0), ('Heavily indebted poor countries (HIPC)', 0.95), ('Croatia', nan), ('Haiti', nan), ('Hungary', 0.99), ('IBRD only', 1.04), ('IDA & IBRD total', 1.01), ('IDA total', 0.95), ('IDA blend', 0.93), ('Indonesia', 0.97), ('IDA only', 0.96), ('Isle of Man', nan), ('India', 1.15), ('Not classified', nan), ('Ireland', 1.0), ('Iran, Islamic Rep.', 1.06), ('Iraq', nan), ('Iceland', 1.0), ('Israel', 1.01), ('Italy', 0.97), ('Jamaica', 0.98), ('Jordan', 0.98), ('Japan', nan), ('Kazakhstan', 1.02), ('Kenya', nan), ('Kyrgyz Republic', 0.99), ('Cambodia', 0.98), ('Kiribati', 1.07), ('St. Kitts and Nevis', nan), ('Korea, Rep.', 1.0), ('Kuwait', 1.03), ('Latin America & Caribbean (excluding high income)', 0.98), ('Lao PDR', 0.96), ('Lebanon', nan), ('Liberia', 0.99), ('Libya', nan), ('St. Lucia', 0.99), ('Latin America & Caribbean', 0.98), ('Least developed countries: UN classification', 0.96), ('Low income', 0.94), ('Liechtenstein', nan), ('Sri Lanka', 0.99), ('Lower middle income', 1.04), ('Low & middle income', 1.01), ('Lesotho', 0.95), ('Late-demographic dividend', 1.0), ('Lithuania', 1.0), ('Luxembourg', 0.99), ('Latvia', 1.0), ('Macao SAR, China', 0.99), ('St. Martin (French part)', nan), ('Morocco', 0.96), ('Monaco', nan), ('Moldova', 1.0), ('Madagascar', nan), ('Maldives', 1.02), ('Middle East & North Africa', 0.97), ('Mexico', 1.0), ('Marshall Islands', nan), ('Middle income', 1.02), ('North Macedonia', nan), ('Mali', 0.89), ('Malta', nan), ('Myanmar', 0.95), ('Middle East & North Africa (excluding high income)', 0.96), ('Montenegro', 1.0), ('Mongolia', 0.98), ('Northern Mariana Islands', nan), ('Mozambique', 0.93), ('Mauritania', 1.07), ('Mauritius', 1.02), ('Malawi', 1.03), ('Malaysia', 1.01), ('North America', 0.99), ('Namibia', nan), ('New Caledonia', nan), ('Niger', 0.86), ('Nigeria', nan), ('Nicaragua', nan), ('Netherlands', 1.0), ('Norway', 1.0), ('Nepal', 1.02), ('Nauru', nan), ('New Zealand', 1.0), ('OECD members', 1.0), ('Oman', 1.05), ('Other small states', 0.98), ('Pakistan', 0.86), ('Panama', 0.98), ('Peru', 0.97), ('Philippines', 0.96), ('Palau', nan), ('Papua New Guinea', nan), ('Poland', 1.0), ('Pre-demographic dividend', 0.94), ('Puerto Rico', nan), ('Korea, Dem. People’s Rep.', nan), ('Portugal', 0.97), ('Paraguay', nan), ('West Bank and Gaza', 1.0), ('Pacific island small states', 0.99), ('Post-demographic dividend', 1.0), ('French Polynesia', nan), ('Qatar', 1.0), ('Romania', nan), ('Russian Federation', 1.0), ('Rwanda', 0.99), ('South Asia', 1.08), ('Saudi Arabia', nan), ('Sudan', 0.94), ('Senegal', 1.16), ('Singapore', 1.0), ('Solomon Islands', 0.99), ('Sierra Leone', 1.02), ('El Salvador', 0.97), ('San Marino', nan), ('Somalia', nan), ('Serbia', 1.0), ('Sub-Saharan Africa (excluding high income)', 0.96), ('South Sudan', nan), ('Sub-Saharan Africa', 0.96), ('Small states', 0.98), ('Sao Tome and Principe', 0.97), ('Suriname', 1.0), ('Slovak Republic', 0.99), ('Slovenia', 1.0), ('Sweden', 1.01), ('Eswatini', 0.92), ('Sint Maarten (Dutch part)', nan), ('Seychelles', 1.05), ('Syrian Arab Republic', nan), ('Turks and Caicos Islands', nan), ('Chad', nan), ('East Asia & Pacific (IDA & IBRD countries)', 1.0), ('Europe & Central Asia (IDA & IBRD countries)', 1.0), ('Togo', 0.95), ('Thailand', 1.0), ('Tajikistan', 0.99), ('Turkmenistan', nan), ('Latin America & the Caribbean (IDA & IBRD countries)', 0.98), ('Timor-Leste', 0.96), ('Middle East & North Africa (IDA & IBRD countries)', 0.96), ('Tonga', nan), ('South Asia (IDA & IBRD)', 1.08), ('Sub-Saharan Africa (IDA & IBRD countries)', 0.96), ('Trinidad and Tobago', nan), ('Tunisia', 1.0), ('Turkey', 0.99), ('Tuvalu', nan), ('Tanzania', 1.03), ('Uganda', 1.03), ('Ukraine', nan), ('Upper middle income', 1.0), ('Uruguay', nan), ('United States', 0.99), ('Uzbekistan', 0.99), ('St. Vincent and the Grenadines', 0.98), ('Venezuela, RB', 0.98), ('British Virgin Islands', 0.96), ('Virgin Islands (U.S.)', nan), ('Vietnam', 1.02), ('Vanuatu', nan), ('World', 1.01), ('Samoa', 1.0), ('Kosovo', nan), ('Yemen, Rep.', nan), ('South Africa', 0.97), ('Zambia', 1.02), ('Zimbabwe', nan)]
In [42]:
df.rate.min()
Out[42]:
0.68
In [43]:
df.rate.max()
Out[43]:
1.16
In [60]:
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.globals import ChartType, SymbolType

def map_world() -> Map:
    c = (
        Map()
        .add("女生入学 / 男生入学)", 各国中小学女男入学比例, "world")
        .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
        .set_global_opts(
            title_opts=opts.TitleOpts(title="中小学女男入学比例图"),
            visualmap_opts=opts.VisualMapOpts(min_=0.68,max_=1.16),
        )
    )
    return c
In [61]:
中小学女男入学比例图 = map_world()
中小学女男入学比例图.render_notebook()
Out[61]:
In [ ]: