# 10-25Visual **Repository Path**: NFUNM026/10-25Visual ## Basic Information - **Project Name**: 10-25Visual - **Description**: No description available - **Primary Language**: HTML - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-10-26 - **Last Updated**: 2024-10-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
import pandas as pd
df=pd.read_csv("strength_of_legal_rights_index.csv",encoding="gbk")
df
| country_name | y_2013 | y_2014 | y_2015 | y_2016 | y_2017 | y_2018 | |
|---|---|---|---|---|---|---|---|
| 0 | Aruba | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | Afghanistan | 9.00000 | 9.00000 | 9.000000 | 9.000000 | 9.000000 | 10.000000 |
| 2 | Angola | 1.00000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 3 | Albania | 7.00000 | 6.00000 | 6.000000 | 6.000000 | 8.000000 | 8.000000 |
| 4 | Andorra | NaN | NaN | NaN | NaN | NaN | NaN |
| 5 | Arab World | 1.47619 | 1.47619 | 1.409091 | 1.409091 | 1.772727 | 2.363636 |
| 6 | United Arab Emirates | 2.00000 | 2.00000 | 2.000000 | 2.000000 | 2.000000 | 6.000000 |
| 7 | Argentina | 3.00000 | 3.00000 | 3.000000 | 3.000000 | 3.000000 | 3.000000 |
| 8 | Armenia | 4.00000 | 4.00000 | 4.000000 | 6.000000 | 6.000000 | 6.000000 |
| 9 | Australia | 11.00000 | 11.00000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 |
| 10 | Austria | 4.00000 | 4.00000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 |
| 11 | Azerbaijan | 2.00000 | 2.00000 | 2.000000 | 2.000000 | 2.000000 | 8.000000 |
| 12 | Burundi | 2.00000 | 2.00000 | 2.000000 | 2.000000 | 2.000000 | 2.000000 |
| 13 | Belgium | 4.00000 | 4.00000 | 4.000000 | 4.000000 | 4.000000 | 8.000000 |
| 14 | Benin | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 15 | Burkina Faso | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 16 | Bangladesh | 5.00000 | 5.00000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 |
| 17 | Bulgaria | 8.00000 | 8.00000 | 8.000000 | 8.000000 | 8.000000 | 8.000000 |
| 18 | Bahrain | 1.00000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 19 | Belize | 4.00000 | 4.00000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 |
| 20 | Bermuda | NaN | NaN | NaN | NaN | NaN | NaN |
| 21 | Bolivia | 0.00000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 22 | Brazil | 2.00000 | 2.00000 | 2.000000 | 2.000000 | 2.000000 | 2.000000 |
| 23 | Barbados | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 24 | Brunei Darussalam | 4.00000 | 4.00000 | 4.000000 | 5.000000 | 12.000000 | 12.000000 |
| 25 | Bhutan | 4.00000 | 4.00000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 |
| 26 | Botswana | 5.00000 | 5.00000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 |
| 27 | Central African Republic | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 28 | Canada | 9.00000 | 9.00000 | 9.000000 | 9.000000 | 9.000000 | 9.000000 |
| 29 | Switzerland | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 152 | Sweden | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 153 | Eswatini | 4.00000 | 4.00000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 |
| 154 | Seychelles | 2.00000 | 2.00000 | 2.000000 | 2.000000 | 2.000000 | 2.000000 |
| 155 | Chad | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 156 | Togo | 6.00000 | 6.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 157 | Thailand | 3.00000 | 3.00000 | 3.000000 | 3.000000 | 7.000000 | 7.000000 |
| 158 | Tajikistan | 1.00000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 159 | Turkmenistan | NaN | NaN | NaN | NaN | NaN | NaN |
| 160 | Timor-Leste | 0.00000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 161 | Tonga | 10.00000 | 10.00000 | 10.000000 | 10.000000 | 10.000000 | 10.000000 |
| 162 | Trinidad and Tobago | 6.00000 | 7.00000 | 7.000000 | 7.000000 | 7.000000 | 7.000000 |
| 163 | Tunisia | 3.00000 | 3.00000 | 3.000000 | 3.000000 | 3.000000 | 3.000000 |
| 164 | Turkey | 2.00000 | 2.00000 | 2.000000 | 2.000000 | 4.000000 | 7.000000 |
| 165 | Tuvalu | NaN | NaN | NaN | NaN | NaN | NaN |
| 166 | Tanzania | 5.00000 | 5.00000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 |
| 167 | Uganda | 5.00000 | 5.00000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 |
| 168 | Ukraine | 8.00000 | 8.00000 | 8.000000 | 8.000000 | 8.000000 | 8.000000 |
| 169 | Uruguay | 4.00000 | 4.00000 | 4.000000 | 4.000000 | 4.000000 | 4.000000 |
| 170 | United States | 11.00000 | 11.00000 | 11.000000 | 11.000000 | 11.000000 | 11.000000 |
| 171 | Uzbekistan | 1.00000 | 1.00000 | 6.000000 | 6.000000 | 6.000000 | 6.000000 |
| 172 | Venezuela, RB | 1.00000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 173 | Vietnam | 7.00000 | 7.00000 | 7.000000 | 7.000000 | 8.000000 | 8.000000 |
| 174 | Vanuatu | 10.00000 | 10.00000 | 10.000000 | 11.000000 | 11.000000 | 11.000000 |
| 175 | Samoa | 5.00000 | 5.00000 | 5.000000 | 5.000000 | 9.000000 | 9.000000 |
| 176 | Kosovo | 9.00000 | 9.00000 | 9.000000 | 9.000000 | 11.000000 | 11.000000 |
| 177 | Yemen | 0.00000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 178 | South Africa | 5.00000 | 5.00000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 |
| 179 | Zambia | 7.00000 | 7.00000 | 7.000000 | 7.000000 | 11.000000 | 11.000000 |
| 180 | Zimbabwe | 5.00000 | 5.00000 | 5.000000 | 5.000000 | 5.000000 | 5.000000 |
| 181 | Greenland | NaN | NaN | NaN | NaN | NaN | NaN |
182 rows × 7 columns
法律权利=list(zip(list(df.country_name),list(df.y_2018.fillna(0))))
print(法律权利)
[('Aruba', 0.0), ('Afghanistan', 10.0), ('Angola', 1.0), ('Albania', 8.0), ('Andorra', 0.0), ('Arab World', 2.363636364), ('United Arab Emirates', 6.0), ('Argentina', 3.0), ('Armenia', 6.0), ('Australia', 11.0), ('Austria', 4.0), ('Azerbaijan', 8.0), ('Burundi', 2.0), ('Belgium', 8.0), ('Benin', 6.0), ('Burkina Faso', 6.0), ('Bangladesh', 5.0), ('Bulgaria', 8.0), ('Bahrain', 1.0), ('Belize', 4.0), ('Bermuda', 0.0), ('Bolivia', 0.0), ('Brazil', 2.0), ('Barbados', 6.0), ('Brunei Darussalam', 12.0), ('Bhutan', 4.0), ('Botswana', 5.0), ('Central African Republic', 6.0), ('Canada', 9.0), ('Switzerland', 6.0), ('Chile', 4.0), ('China', 4.0), ("Cote d'Ivoire", 6.0), ('Cameroon', 6.0), ('Colombia', 12.0), ('Comoros', 6.0), ('Cabo Verde', 1.0), ('Costa Rica', 10.0), ('Cuba', 0.0), ('Curacao', 0.0), ('Cyprus', 7.0), ('Germany', 6.0), ('Djibouti', 5.0), ('Dominica', 6.0), ('Denmark', 8.0), ('Algeria', 2.0), ('Ecuador', 1.0), ('Spain', 5.0), ('Estonia', 7.0), ('Ethiopia', 3.0), ('Finland', 7.0), ('Fiji', 5.0), ('France', 4.0), ('Gabon', 6.0), ('Georgia', 9.0), ('Ghana', 6.0), ('Gibraltar', 0.0), ('Guinea', 6.0), ('Greece', 3.0), ('Grenada', 6.0), ('Guatemala', 9.0), ('Guyana', 3.0), ('Hong Kong SAR, China', 8.0), ('Honduras', 9.0), ('Croatia', 5.0), ('Hungary', 9.0), ('Indonesia', 6.0), ('India', 9.0), ('Ireland', 7.0), ('Iraq', 0.0), ('Israel', 6.0), ('Italy', 2.0), ('Jamaica', 9.0), ('Jordan', 0.0), ('Japan', 5.0), ('Kazakhstan', 6.0), ('Kenya', 10.0), ('Cambodia', 10.0), ('Kiribati', 4.0), ('St. Kitts and Nevis', 5.0), ('Korea', 5.0), ('Kuwait', 1.0), ('Lao', 6.0), ('Lebanon', 2.0), ('Liberia', 9.0), ('Libya', 0.0), ('St. Lucia', 5.0), ('Liechtenstein', 0.0), ('Sri Lanka', 2.0), ('Lesotho', 5.0), ('Lithuania', 6.0), ('Luxembourg', 3.0), ('Latvia', 9.0), ('Morocco', 2.0), ('Monaco', 0.0), ('Moldova', 8.0), ('Madagascar', 2.0), ('Maldives', 2.0), ('Mexico', 10.0), ('North Macedonia', 10.0), ('Mali', 6.0), ('Malta', 2.0), ('Myanmar', 2.0), ('Montenegro', 12.0), ('Mongolia', 9.0), ('Mozambique', 1.0), ('Mauritania', 2.0), ('Mauritius', 6.0), ('Malawi', 11.0), ('Malaysia', 7.0), ('North America', 10.0), ('Namibia', 5.0), ('New Caledonia', 0.0), ('Niger', 6.0), ('Nigeria', 9.0), ('Nicaragua', 2.0), ('Netherlands', 2.0), ('Norway', 5.0), ('Nepal', 10.0), ('Nauru', 0.0), ('New Zealand', 12.0), ('Oman', 1.0), ('Pakistan', 2.0), ('Panama', 8.0), ('Peru', 7.0), ('Philippines', 1.0), ('Palau', 10.0), ('Papua New Guinea', 9.0), ('Poland', 7.0), ('Pre-demographic dividend', 5.216216216), ('Puerto Rico', 12.0), ('Portugal', 2.0), ('Paraguay', 1.0), ('Qatar', 1.0), ('Romania', 9.0), ('Russia', 9.0), ('Rwanda', 11.0), ('South Asia', 5.5), ('Saudi Arabia', 1.0), ('Sudan', 5.0), ('Senegal', 6.0), ('Singapore', 8.0), ('Sierra Leone', 5.0), ('El Salvador', 9.0), ('San Marino', 1.0), ('Somalia', 0.0), ('Serbia', 6.0), ('South Sudan', 2.0), ('Sub-Saharan Africa', 5.145833333), ('Suriname', 2.0), ('Slovak Republic', 7.0), ('Slovenia', 3.0), ('Sweden', 6.0), ('Eswatini', 4.0), ('Seychelles', 2.0), ('Chad', 6.0), ('Togo', 6.0), ('Thailand', 7.0), ('Tajikistan', 1.0), ('Turkmenistan', 0.0), ('Timor-Leste', 0.0), ('Tonga', 10.0), ('Trinidad and Tobago', 7.0), ('Tunisia', 3.0), ('Turkey', 7.0), ('Tuvalu', 0.0), ('Tanzania', 5.0), ('Uganda', 5.0), ('Ukraine', 8.0), ('Uruguay', 4.0), ('United States', 11.0), ('Uzbekistan', 6.0), ('Venezuela, RB', 1.0), ('Vietnam', 8.0), ('Vanuatu', 11.0), ('Samoa', 9.0), ('Kosovo', 11.0), ('Yemen', 0.0), ('South Africa', 5.0), ('Zambia', 11.0), ('Zimbabwe', 5.0), ('Greenland', 0.0)]
from pyecharts import options as opts
from pyecharts.charts import Map
from pyecharts.globals import ChartType, SymbolType
def map_world() -> Map:
c = (
Map()
.add("2018年法律权利力度指数", 法律权利, "world")
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
title_opts=opts.TitleOpts(title="法律权利力度指数(0=弱,12=强)"),
visualmap_opts=opts.VisualMapOpts(min_=0.000000, max_=12.000000),
)
)
return c
地理图 = map_world()
地理图.render_notebook()
通过世界地图查看世界各个国家的法律权利力度指数,来看各个国家对于法律力度的管控程度。0即蓝色代表法律权利力度指数是最弱的,12即红色是最强的。
格陵兰岛的由于数据缺失所以显示蓝色,但是总体来说北美洲的法律权利力度指数是最高的。大洋洲的法律权利力度指数也是最高的,我们知道近代的法律起源是英国的大宪章运动,最早掀法律运动的狂潮,所有年代历史的久远,对于法律的完善越来越好,所以他们的法律权利力度指数高也是一个正常现象。大洋洲只有澳大利亚一个国家,由于地广人稀的特质在加上他们审判制度,所以相对其他国家来说法律权利力度指数是相对比较高的。
中国的法律权利力度指数很低,虽然中华文化有5千多年的历史,但是5千多年法律掌握在权贵手里,近代的法律变革,虽然保障公民的权益,但是很多法律明令没有完善。