数据可视化案例:世界幸福指数报告

2024-07-04 20:46| 来源: 网络整理| 查看: 265

文章目录 1. 背景2. 内容3. dataset列4. 人均GDP最高、最低的国家5. 幸福指数最高、最低的10个国家6. 幸福指数与GDP的二维散点图7. 人均寿命与GDP的二维散点图背景

《世界幸福报告》是对全球幸福状况的里程碑式调查。随着政府、组织和民间社会越来越多地使用幸福指数来为他们的决策提供信息，该报告继续获得全球认可。跨领域的领先专家——经济学、心理学、调查分析、国家统计、健康、公共政策等——描述了如何有效地使用幸福的衡量来评估国家的进步。这些报告回顾了当今世界的幸福状况，并展示了新的幸福科学如何解释幸福的个人和国家差异。

内容

幸福指数和排名使用来自盖洛普世界民意调查的数据。幸福分数后面的列估计了六个因素中的每一个——经济生产、社会支持、预期寿命、自由、没有腐败和慷慨——在多大程度上有助于使每个国家的生活评价高于反乌托邦。它们对每个国家/地区报告的总分没有影响，但它们确实解释了为什么有些国家/地区的排名高于其他国家/地区。

数据集：世界幸福指数报告

1234567891011121314from pylab import mplmpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题#importing the librariesimport pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3Dimport warningswarnings.filterwarnings('ignore') dataset列 Country name：国家名字 Regional indicator：地区 Ladder score：幸福指数 Standard error of ladder score ：幸福指数标准差 upperwhisker : 标准差上界 lowerwhisker：标准差下界 Logged GDP per capita：人均GDP Social support：社会保障？ Healthy life expectancy：人均寿命 Freedom to make life choices：做出人生决定的自由度？ Generosity：慷慨？ Perceptions of corruption：腐败程度 Ladder score in Dystopia Explained by: Log GDP per capita Explained by: Social support Explained by: Healthy life expectancy Explained by: Freedom to make life choices Explained by: Generosity Explained by: Perceptions of corruption Dystopia + residual A B 社会保障人均寿命人均GDP 幸福指数慷慨指数做出人生决定的自由度地区 123#load the datasetdf = pd.read_csv('world-happiness-report-2021.csv')df.head().transpose() 0 1 2 3 4 Country name Finland Denmark Switzerland Iceland Netherlands Regional indicator Western Europe Western Europe Western Europe Western Europe Western Europe Ladder score 7.842 7.62 7.571 7.554 7.464 Standard error of ladder score 0.032 0.035 0.036 0.059 0.027 upperwhisker 7.904 7.687 7.643 7.67 7.518 lowerwhisker 7.78 7.552 7.5 7.438 7.41 Logged GDP per capita 10.775 10.933 11.117 10.878 10.932 Social support 0.954 0.954 0.942 0.983 0.942 Healthy life expectancy 72.0 72.7 74.4 73.0 72.4 Freedom to make life choices 0.949 0.946 0.919 0.955 0.913 Generosity -0.098 0.03 0.025 0.16 0.175 Perceptions of corruption 0.186 0.179 0.292 0.673 0.338 Ladder score in Dystopia 2.43 2.43 2.43 2.43 2.43 Explained by: Log GDP per capita 1.446 1.502 1.566 1.482 1.501 Explained by: Social support 1.106 1.108 1.079 1.172 1.079 Explained by: Healthy life expectancy 0.741 0.763 0.816 0.772 0.753 Explained by: Freedom to make life choices 0.691 0.686 0.653 0.698 0.647 Explained by: Generosity 0.124 0.208 0.204 0.293 0.302 Explained by: Perceptions of corruption 0.481 0.485 0.413 0.17 0.384 Dystopia + residual 3.253 2.868 2.839 2.967 2.798 1# df[["Country name", "Freedom to make life choices"]].sort_values(by="Freedom to make life choices", ascending=False).values 1df.shape (149, 20) 1# df.info 1df.describe().transpose() count mean std min 25% 50% 75% max Ladder score 149.0 5.532839 1.073924e+00 2.523 4.852 5.534 6.255 7.842 Standard error of ladder score 149.0 0.058752 2.200120e-02 0.026 0.043 0.054 0.070 0.173 upperwhisker 149.0 5.648007 1.054330e+00 2.596 4.991 5.625 6.344 7.904 lowerwhisker 149.0 5.417631 1.094879e+00 2.449 4.706 5.413 6.128 7.780 Logged GDP per capita 149.0 9.432208 1.158601e+00 6.635 8.541 9.569 10.421 11.647 Social support 149.0 0.814745 1.148890e-01 0.463 0.750 0.832 0.905 0.983 Healthy life expectancy 149.0 64.992799 6.762043e+00 48.478 59.802 66.603 69.600 76.953 Freedom to make life choices 149.0 0.791597 1.133318e-01 0.382 0.718 0.804 0.877 0.970 Generosity 149.0 -0.015134 1.506567e-01 -0.288 -0.126 -0.036 0.079 0.542 Perceptions of corruption 149.0 0.727450 1.792263e-01 0.082 0.667 0.781 0.845 0.939 Ladder score in Dystopia 149.0 2.430000 5.347044e-15 2.430 2.430 2.430 2.430 2.430 Explained by: Log GDP per capita 149.0 0.977161 4.047399e-01 0.000 0.666 1.025 1.323 1.751 Explained by: Social support 149.0 0.793315 2.588713e-01 0.000 0.647 0.832 0.996 1.172 Explained by: Healthy life expectancy 149.0 0.520161 2.130191e-01 0.000 0.357 0.571 0.665 0.897 Explained by: Freedom to make life choices 149.0 0.498711 1.378884e-01 0.000 0.409 0.514 0.603 0.716 Explained by: Generosity 149.0 0.178047 9.827033e-02 0.000 0.105 0.164 0.239 0.541 Explained by: Perceptions of corruption 149.0 0.135141 1.143614e-01 0.000 0.060 0.101 0.174 0.547 Dystopia + residual 149.0 2.430329 5.376452e-01 0.648 2.138 2.509 2.794 3.482 1df.duplicated().sum() 0 12plt.figure(figsize=[18,9])sns.heatmap(df.corr(),annot=True,cmap="Blues");

人均GDP最高、最低的国家12gdp = df[['Logged GDP- per capita','Country name']].sort_values('Logged GDP per capita', ascending=False)gdp[:10] Logged GDP per capita Country name 7 11.647 Luxembourg 31 11.488 Singapore 14 11.342 Ireland 2 11.117 Switzerland 24 11.085 United Arab Emirates 5 11.053 Norway 18 11.023 United States 76 11.000 Hong Kong S.A.R. of China 1 10.933 Denmark 4 10.932 Netherlands 1gdp[-10:] Logged GDP per capita Country name 142 7.477 Haiti 137 7.434 Sierra Leone 134 7.396 Madagascar 127 7.364 Chad 135 7.362 Togo 119 7.288 Liberia 114 7.158 Mozambique 95 7.098 Niger 143 6.958 Malawi 139 6.635 Burundi 幸福指数最高、最低的10个国家123# top10 ladder score countryladder_score = df[['Ladder score',"Standard error of ladder score",'Country name']].sort_values("Ladder score", ascending=False)ladder_score[:10] Ladder score Standard error of ladder score Country name 0 7.842 0.032 Finland 1 7.620 0.035 Denmark 2 7.571 0.036 Switzerland 3 7.554 0.059 Iceland 4 7.464 0.027 Netherlands 5 7.392 0.035 Norway 6 7.363 0.036 Sweden 7 7.324 0.037 Luxembourg 8 7.277 0.040 New Zealand 9 7.268 0.036 Austria 12# low10 ladder score ladder_score[-10:] Ladder score Standard error of ladder score Country name 139 3.775 0.107 Burundi 140 3.658 0.070 Yemen 141 3.623 0.071 Tanzania 142 3.615 0.173 Haiti 143 3.600 0.092 Malawi 144 3.512 0.120 Lesotho 145 3.467 0.074 Botswana 146 3.415 0.068 Rwanda 147 3.145 0.058 Zimbabwe 148 2.523 0.038 Afghanistan 12 幸福指数与GDP的二维散点图

*　https://stackoverflow.com/questions/13872533/plot-different-dataframes-in-the-same-figure

揭示了幸福指数与人均GDP存在强正相关性 1234567fig1, ax1 = plt.subplots()# fig1.set_size_inches(10, 10)df.plot(y='Ladder score', x = "Logged GDP per capita", kind="scatter", ax=ax1)plt.title("幸福指数与人均GDP")# for cn, xs, ys in zip(df['Country name'], df['Logged GDP per capita'], df['Ladder score']):# plt.text(xs, ys, cn) Text(0.5, 1.0, '幸福指数与人均GDP')

人均寿命与GDP的二维散点图揭示了人均寿命与人均GDP存在强正相关性 12345df.plot( x = "Logged GDP per capita", y="Healthy life expectancy",kind="scatter")plt.title("预估人均寿命与人均GDP的散点图")plt.ylabel('人均寿命',fontsize=10,fontweight='bold')plt.xlabel('人均GDP',fontsize=10,fontweight='bold') Text(0.5, 0, '人均GDP')

1234567threedee = plt.figure(figsize=(16, 12)).gca(projection='3d')threedee.scatter(df["Logged GDP per capita"], df["Healthy life expectancy"], df['Ladder score'], c=df["Logged GDP per capita"])threedee.set_xlabel('人均GDP', fontsize=20)threedee.set_ylabel('人均寿命', fontsize=20)threedee.set_zlabel('幸福指数', fontsize=20)plt.title("人均GDP-人均寿命-幸福指数三维图", fontsize=30)plt.show()

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