数据分析-人的寿命

本文最后更新于：July 15, 2022 am 

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import pandas as pd
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
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life = pd.read_csv('C:/Users/86155/Desktop/Life Expectancy Data.csv')
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life.head()
Country Year Status Life expectancy Adult Mortality infant deaths Alcohol percentage expenditure Hepatitis B Measles ... Polio Total expenditure Diphtheria HIV/AIDS GDP Population thinness 1-19 years thinness 5-9 years Income composition of resources Schooling
0 Afghanistan 2015 Developing 65.0 263.0 62 0.01 71.279624 65.0 1154 ... 6.0 8.16 65.0 0.1 584.259210 33736494.0 17.2 17.3 0.479 10.1
1 Afghanistan 2014 Developing 59.9 271.0 64 0.01 73.523582 62.0 492 ... 58.0 8.18 62.0 0.1 612.696514 327582.0 17.5 17.5 0.476 10.0
2 Afghanistan 2013 Developing 59.9 268.0 66 0.01 73.219243 64.0 430 ... 62.0 8.13 64.0 0.1 631.744976 31731688.0 17.7 17.7 0.470 9.9
3 Afghanistan 2012 Developing 59.5 272.0 69 0.01 78.184215 67.0 2787 ... 67.0 8.52 67.0 0.1 669.959000 3696958.0 17.9 18.0 0.463 9.8
4 Afghanistan 2011 Developing 59.2 275.0 71 0.01 7.097109 68.0 3013 ... 68.0 7.87 68.0 0.1 63.537231 2978599.0 18.2 18.2 0.454 9.5

5 rows × 22 columns

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import matplotlib.pyplot as plt
import seaborn as sns
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life.describe()
Year Life expectancy Adult Mortality infant deaths Alcohol percentage expenditure Hepatitis B Measles BMI under-five deaths Polio Total expenditure Diphtheria HIV/AIDS GDP Population thinness 1-19 years thinness 5-9 years Income composition of resources Schooling
count 2938.000000 2928.000000 2928.000000 2938.000000 2744.000000 2938.000000 2385.000000 2938.000000 2904.000000 2938.000000 2919.000000 2712.00000 2919.000000 2938.000000 2490.000000 2.286000e+03 2904.000000 2904.000000 2771.000000 2775.000000
mean 2007.518720 69.224932 164.796448 30.303948 4.602861 738.251295 80.940461 2419.592240 38.321247 42.035739 82.550188 5.93819 82.324084 1.742103 7483.158469 1.275338e+07 4.839704 4.870317 0.627551 11.992793
std 4.613841 9.523867 124.292079 117.926501 4.052413 1987.914858 25.070016 11467.272489 20.044034 160.445548 23.428046 2.49832 23.716912 5.077785 14270.169342 6.101210e+07 4.420195 4.508882 0.210904 3.358920
min 2000.000000 36.300000 1.000000 0.000000 0.010000 0.000000 1.000000 0.000000 1.000000 0.000000 3.000000 0.37000 2.000000 0.100000 1.681350 3.400000e+01 0.100000 0.100000 0.000000 0.000000
25% 2004.000000 63.100000 74.000000 0.000000 0.877500 4.685343 77.000000 0.000000 19.300000 0.000000 78.000000 4.26000 78.000000 0.100000 463.935626 1.957932e+05 1.600000 1.500000 0.493000 10.100000
50% 2008.000000 72.100000 144.000000 3.000000 3.755000 64.912906 92.000000 17.000000 43.500000 4.000000 93.000000 5.75500 93.000000 0.100000 1766.947595 1.386542e+06 3.300000 3.300000 0.677000 12.300000
75% 2012.000000 75.700000 228.000000 22.000000 7.702500 441.534144 97.000000 360.250000 56.200000 28.000000 97.000000 7.49250 97.000000 0.800000 5910.806335 7.420359e+06 7.200000 7.200000 0.779000 14.300000
max 2015.000000 89.000000 723.000000 1800.000000 17.870000 19479.911610 99.000000 212183.000000 87.300000 2500.000000 99.000000 17.60000 99.000000 50.600000 119172.741800 1.293859e+09 27.700000 28.600000 0.948000 20.700000 
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life.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2938 entries, 0 to 2937
Data columns (total 22 columns):
 #   Column                           Non-Null Count  Dtype  
---  ------                           --------------  -----  
 0   Country                          2938 non-null   object 
 1   Year                             2938 non-null   int64  
 2   Status                           2938 non-null   object 
 3   Life expectancy                  2928 non-null   float64
 4   Adult Mortality                  2928 non-null   float64
 5   infant deaths                    2938 non-null   int64  
 6   Alcohol                          2744 non-null   float64
 7   percentage expenditure           2938 non-null   float64
 8   Hepatitis B                      2385 non-null   float64
 9   Measles                          2938 non-null   int64  
 10   BMI                             2904 non-null   float64
 11  under-five deaths                2938 non-null   int64  
 12  Polio                            2919 non-null   float64
 13  Total expenditure                2712 non-null   float64
 14  Diphtheria                       2919 non-null   float64
 15   HIV/AIDS                        2938 non-null   float64
 16  GDP                              2490 non-null   float64
 17  Population                       2286 non-null   float64
 18   thinness  1-19 years            2904 non-null   float64
 19   thinness 5-9 years              2904 non-null   float64
 20  Income composition of resources  2771 non-null   float64
 21  Schooling                        2775 non-null   float64
dtypes: float64(16), int64(4), object(2)
memory usage: 505.1+ KB

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life.isnull().sum()
Country                              0
Year                                 0
Status                               0
Life expectancy                     10
Adult Mortality                     10
infant deaths                        0
Alcohol                            194
percentage expenditure               0
Hepatitis B                        553
Measles                              0
 BMI                                34
under-five deaths                    0
Polio                               19
Total expenditure                  226
Diphtheria                          19
 HIV/AIDS                            0
GDP                                448
Population                         652
 thinness  1-19 years               34
 thinness 5-9 years                 34
Income composition of resources    167
Schooling                          163
dtype: int64

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#  数据缺失值填补

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy='median')
imputer.fit(life.iloc[:, 3:])
life.iloc[:, 3:]=imputer.transform(life.iloc[:, 3:])
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#  观察数据中的空值的总和

life.isnull().sum()
Country                             0
Year                                0
Status                              0
Life expectancy                    10
Adult Mortality                     0
infant deaths                       0
Alcohol                             0
percentage expenditure              0
Hepatitis B                         0
Measles                             0
 BMI                                0
under-five deaths                   0
Polio                               0
Total expenditure                   0
Diphtheria                          0
 HIV/AIDS                           0
GDP                                 0
Population                          0
 thinness  1-19 years               0
 thinness 5-9 years                 0
Income composition of resources     0
Schooling                           0
dtype: int64

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life.describe()
Year Life expectancy Adult Mortality infant deaths Alcohol percentage expenditure Hepatitis B Measles BMI under-five deaths Polio Total expenditure Diphtheria HIV/AIDS GDP Population thinness 1-19 years thinness 5-9 years Income composition of resources Schooling
count 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2938.000000 2.938000e+03 2938.000000 2938.000000 2938.000000 2938.000000
mean 2007.518720 69.234717 164.725664 30.303948 4.546875 738.251295 83.022124 2419.592240 38.381178 42.035739 82.617767 5.924098 82.393125 1.742103 6611.523863 1.023085e+07 4.821886 4.852144 0.630362 12.009837
std 4.613841 9.509115 124.086215 117.926501 3.921946 1987.914858 22.996984 11467.272489 19.935375 160.445548 23.367166 2.400770 23.655562 5.077785 13296.603449 5.402242e+07 4.397621 4.485854 0.205140 3.265139
min 2000.000000 36.300000 1.000000 0.000000 0.010000 0.000000 1.000000 0.000000 1.000000 0.000000 3.000000 0.370000 2.000000 0.100000 1.681350 3.400000e+01 0.100000 0.100000 0.000000 0.000000
25% 2004.000000 63.200000 74.000000 0.000000 1.092500 4.685343 82.000000 0.000000 19.400000 0.000000 78.000000 4.370000 78.000000 0.100000 580.486996 4.189172e+05 1.600000 1.600000 0.504250 10.300000
50% 2008.000000 72.100000 144.000000 3.000000 3.755000 64.912906 92.000000 17.000000 43.500000 4.000000 93.000000 5.755000 93.000000 0.100000 1766.947595 1.386542e+06 3.300000 3.300000 0.677000 12.300000
75% 2012.000000 75.600000 227.000000 22.000000 7.390000 441.534144 96.000000 360.250000 56.100000 28.000000 97.000000 7.330000 97.000000 0.800000 4779.405190 4.584371e+06 7.100000 7.200000 0.772000 14.100000
max 2015.000000 89.000000 723.000000 1800.000000 17.870000 19479.911610 99.000000 212183.000000 87.300000 2500.000000 99.000000 17.600000 99.000000 50.600000 119172.741800 1.293859e+09 27.700000 28.600000 0.948000 20.700000 
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life.head()
Country Year Status Life expectancy Adult Mortality infant deaths Alcohol percentage expenditure Hepatitis B Measles ... Polio Total expenditure Diphtheria HIV/AIDS GDP Population thinness 1-19 years thinness 5-9 years Income composition of resources Schooling
0 Afghanistan 2015 Developing 65.0 263.0 62.0 0.01 71.279624 65.0 1154.0 ... 6.0 8.16 65.0 0.1 584.259210 33736494.0 17.2 17.3 0.479 10.1
1 Afghanistan 2014 Developing 59.9 271.0 64.0 0.01 73.523582 62.0 492.0 ... 58.0 8.18 62.0 0.1 612.696514 327582.0 17.5 17.5 0.476 10.0
2 Afghanistan 2013 Developing 59.9 268.0 66.0 0.01 73.219243 64.0 430.0 ... 62.0 8.13 64.0 0.1 631.744976 31731688.0 17.7 17.7 0.470 9.9
3 Afghanistan 2012 Developing 59.5 272.0 69.0 0.01 78.184215 67.0 2787.0 ... 67.0 8.52 67.0 0.1 669.959000 3696958.0 17.9 18.0 0.463 9.8
4 Afghanistan 2011 Developing 59.2 275.0 71.0 0.01 7.097109 68.0 3013.0 ... 68.0 7.87 68.0 0.1 63.537231 2978599.0 18.2 18.2 0.454 9.5

5 rows × 22 columns

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#  去除完全重复的行数据

life.drop_duplicates(inplace = True)
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life.columns
Index(['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',
       'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',
       'Measles ', ' BMI ', 'under-five deaths ', 'Polio', 'Total expenditure',
       'Diphtheria ', ' HIV/AIDS', 'GDP', 'Population',
       ' thinness  1-19 years', ' thinness 5-9 years',
       'Income composition of resources', 'Schooling'],
      dtype='object')

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a = life[['Country', 'Year', 'Status', 'Life expectancy ', 'Adult Mortality',
       'infant deaths']]
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sns.pairplot(a)
<seaborn.axisgrid.PairGrid at 0x1387c388bb0>

output-14-1.png

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b = life[['Alcohol', 'percentage expenditure', 'Hepatitis B',
       'Measles ', ' BMI ', 'under-five deaths ', 'Life expectancy ']]
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sns.pairplot(b)
<seaborn.axisgrid.PairGrid at 0x1387d22be80>

output-16-1.png

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c = life[[ 'Polio', 'Total expenditure','Diphtheria ', ' HIV/AIDS', 'GDP', 'Life expectancy ']]
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sns.pairplot(c)
<seaborn.axisgrid.PairGrid at 0x1387d305fd0>

output-18-1.png

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e = life[['Population',
       ' thinness  1-19 years', ' thinness 5-9 years',
       'Income composition of resources', 'Schooling','Life expectancy ']]
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sns.pairplot(e)
<seaborn.axisgrid.PairGrid at 0x13803724460>

output-20-1.png

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plt.figure(figsize=(16, 16))
sns.heatmap(life.corr(), annot= True)
<matplotlib.axes._subplots.AxesSubplot at 0x138076038b0>

output-21-1.png

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#  成人死亡率对预期寿命的影响大于婴儿死亡率。

# 婴儿死亡率与预期寿命的相关 =-0.2
# 成人死亡率与预期寿命的相关 =-0.7
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# 身体质量指数又称体重指数，意思是人们健康的饮食习惯、生活方式、锻炼方式对日常生活有很好的影响。
# 另外，bmi 指数与预期寿命呈正相关，酒精与预期寿命呈正相关。对于吸烟，没有给出数据，但根据经验法则，我可以说吸烟与预期寿命呈负相关。
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#  学校教育对人类寿命有正向影响，正相关系数为0.71
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#  酒精与预期寿命呈正相关，相关系数为0.4
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#  免疫接种与生命期限呈正相关。
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#  人口和预期寿命之间存在负相关。随着人口增长，预期寿命减少，因为在那个国家的每个人都不能从政府那里得到医疗福利。
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#  实际影响预期寿命的因素

# 百分比支出的中位数
life['percentage expenditure'].median()
64.912906045

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o = life.loc[life['Life expectancy '] <65]
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o.head()
Country Year Status Life expectancy Adult Mortality infant deaths Alcohol percentage expenditure Hepatitis B Measles ... Polio Total expenditure Diphtheria HIV/AIDS GDP Population thinness 1-19 years thinness 5-9 years Income composition of resources Schooling
1 Afghanistan 2014 Developing 59.9 271.0 64 0.01 73.523582 62.0 492 ... 58.0 8.18 62.0 0.1 612.696514 327582.0 17.5 17.5 0.476 10.0
2 Afghanistan 2013 Developing 59.9 268.0 66 0.01 73.219243 64.0 430 ... 62.0 8.13 64.0 0.1 631.744976 31731688.0 17.7 17.7 0.470 9.9
3 Afghanistan 2012 Developing 59.5 272.0 69 0.01 78.184215 67.0 2787 ... 67.0 8.52 67.0 0.1 669.959000 3696958.0 17.9 18.0 0.463 9.8
4 Afghanistan 2011 Developing 59.2 275.0 71 0.01 7.097109 68.0 3013 ... 68.0 7.87 68.0 0.1 63.537231 2978599.0 18.2 18.2 0.454 9.5
5 Afghanistan 2010 Developing 58.8 279.0 74 0.01 79.679367 66.0 1989 ... 66.0 9.20 66.0 0.1 553.328940 2883167.0 18.4 18.4 0.448 9.2

5 rows × 22 columns

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o.describe()
Year Life expectancy Adult Mortality infant deaths Alcohol percentage expenditure Hepatitis B Measles BMI under-five deaths Polio Total expenditure Diphtheria HIV/AIDS GDP Population thinness 1-19 years thinness 5-9 years Income composition of resources Schooling
count 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 851.000000 8.510000e+02 851.000000 851.00000 851.000000 851.000000
mean 2006.819036 56.717979 282.206816 64.045828 2.732844 72.750935 76.294947 4755.605170 22.198590 94.283196 66.230317 5.382127 65.615746 5.493537 1148.534646 1.212722e+07 7.688484 7.66839 0.438837 8.830787
std 4.564844 5.618431 150.316652 169.930696 2.727926 168.242431 24.938488 16995.691061 11.521692 235.109718 26.326655 2.129323 27.054876 8.290629 1963.526909 7.028048e+07 4.359019 4.49745 0.160284 2.647701
min 2000.000000 36.300000 2.000000 0.000000 0.010000 0.000000 1.000000 0.000000 2.100000 0.000000 3.000000 0.650000 2.000000 0.100000 3.685949 1.679000e+03 0.200000 0.20000 0.000000 0.000000
25% 2003.000000 52.700000 228.500000 7.000000 0.365000 1.792246 71.000000 13.500000 15.900000 10.000000 54.000000 3.970000 51.000000 0.800000 263.129142 7.237965e+05 5.600000 5.50000 0.388000 7.200000
50% 2006.000000 57.500000 285.000000 27.000000 1.830000 22.097580 88.000000 245.000000 19.500000 42.000000 73.000000 5.300000 74.000000 2.500000 561.997387 1.386542e+06 7.700000 7.50000 0.451000 9.100000
75% 2011.000000 61.800000 366.000000 56.500000 4.150000 62.523467 92.000000 1902.000000 26.800000 88.000000 86.000000 6.410000 86.000000 5.800000 1766.947595 7.078446e+06 9.300000 9.20000 0.514000 10.600000
max 2015.000000 64.900000 723.000000 1800.000000 11.890000 1705.678619 99.000000 212183.000000 72.100000 2500.000000 99.000000 14.390000 99.000000 50.600000 22742.381590 1.161978e+09 27.700000 28.60000 0.741000 14.400000 
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# 预测寿命值低于65的国家是否应该增加其医疗保健支出以及改善其平均寿命

plt.figure(figsize=(16, 13))
sns.heatmap(o.corr(), annot= True)
<matplotlib.axes._subplots.AxesSubplot at 0x209b4a9afa0>

output-32-1.png

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#   可以看出支出百分比和寿命之间的相关性只有  0.012
#   没有必要增加支出
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plt.figure(figsize=(30,7))
sns.countplot(x='Adult Mortality',data = o.head(5), hue = 'Life expectancy ',palette='PuBuGn',saturation=0.8)
plt.xticks(fontsize=13)
plt.yticks(fontsize=13)
plt.show()

output-34-0.png

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x = o[['Adult Mortality']]
y = o[['Life expectancy ']]
plt.figure(figsize=(700,420))
plt.plot(x,y)
plt.show()
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X=o['Adult Mortality'].values
Y=o['Life expectancy '].values
mean_x =np.mean(X)
mean_y =np.mean(Y)
l=len(X)
n=0
d=0
for i in range(l):
    n+=(X[i]-mean_x)*(Y[i]-mean_y)
    d+=(X[i]-mean_x)**2
b1=n/d
b0=mean_y-(b1*mean_x)
print(b1,b0)
-0.013979330333748737 60.66304114487947

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max_x=np.max(X)+10
min_x=np.min(X)
x=np.linspace(min_x,max_x)
y=b0+b1*x

sns.lineplot(x,y)
sns.scatterplot(x=o["Adult Mortality"],y=o["Life expectancy "])
<matplotlib.axes._subplots.AxesSubplot at 0x1380d8a03d0>

output-37-1.png