취미로 하는 데이터 분석 시리즈02(Covid 확진자 데이터 분석)

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn

In [3]:

covid  = pd.read_csv("./Datas/owid-covid-data.csv")

Covid 데이터를 이용해, 어떤 지역에서 가장 많은 확진자가 발생했는지 알아보자

In [19]:

covid.head()

Out[19]:

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	AFG	Asia	Afghanistan	2020-02-24	5.0	5.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
1	AFG	Asia	Afghanistan	2020-02-25	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
2	AFG	Asia	Afghanistan	2020-02-26	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
3	AFG	Asia	Afghanistan	2020-02-27	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
4	AFG	Asia	Afghanistan	2020-02-28	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN

5 rows × 67 columns

In [37]:

covid.continent.unique()

Out[37]:

array(['Asia', nan, 'Europe', 'Africa', 'North America', 'South America',
       'Oceania'], dtype=object)

In [38]:

df = covid.groupby("continent").sum()["total_cases"].sort_values(ascending=False)

In [39]:

df

Out[39]:

continent
Asia             2.645692e+10
Europe           2.602631e+10
North America    2.162411e+10
South America    1.457782e+10
Africa           3.025414e+09
Oceania          1.703324e+08
Name: total_cases, dtype: float64

In [50]:

plt.plot(df)
plt.ylim(0,30000000000)
plt.xlabel("Continent")
plt.ylabel("cases")
plt.title("Total cases by Continents")
plt.xticks(rotation = 30)
plt.show()

In [108]:

covid.groupby("location").sum().total_cases.sort_values(ascending=False)

Out[108]:

location
World                  9.188144e+10
High income            4.233457e+10
Upper middle income    2.799119e+10
Asia                   2.645692e+10
Europe                 2.602631e+10
                           ...     
Tokelau                0.000000e+00
Niue                   0.000000e+00
Northern Cyprus        0.000000e+00
Pitcairn               0.000000e+00
Guernsey               0.000000e+00
Name: total_cases, Length: 238, dtype: float64

In [125]:

covid["location"].drop(columns = ["World"], axis=1, inplace=True)

In [153]:

covid.location

Out[153]:

0         Afghanistan
1         Afghanistan
2         Afghanistan
3         Afghanistan
4         Afghanistan
             ...     
162602       Zimbabwe
162603       Zimbabwe
162604       Zimbabwe
162605       Zimbabwe
162606       Zimbabwe
Name: location, Length: 162607, dtype: object

In [173]:

location_covid = covid

location_covid = location_covid[location_covid.location!="World"]
location_covid = location_covid[location_covid.location!="Asia"]
location_covid = location_covid[location_covid.location!="Africa"]
location_covid = location_covid[location_covid.location!="High income"]
location_covid = location_covid[location_covid.location!="Upper middle income"]
location_covid = location_covid[location_covid.location!="Europe"]
location_covid = location_covid[location_covid.location!="North America"]
location_covid = location_covid[location_covid.location!="Lower middle income"]
location_covid = location_covid[location_covid.location!="European Union"]
location_covid = location_covid[location_covid.location!="South America"]
loc_covid = location_covid.groupby("location").sum().total_cases.sort_values(ascending=False)

In [174]:

loc_covid

Out[174]:

location
United States                1.833960e+10
India                        1.218840e+10
Brazil                       8.283593e+09
United Kingdom               3.189000e+09
France                       3.146283e+09
                                 ...     
Pitcairn                     0.000000e+00
Sint Maarten (Dutch part)    0.000000e+00
Jersey                       0.000000e+00
Tuvalu                       0.000000e+00
Tokelau                      0.000000e+00
Name: total_cases, Length: 228, dtype: float64

In [183]:

df6 = loc_covid.head(10)

In [184]:

df6 = df6.sort_values()

In [210]:

loc_covid["China"]

Out[210]:

66506587.0

In [211]:

df6["China"] = 66506587.0

In [212]:

plt.plot(df6)
plt.xticks(rotation=30)
plt.show()

Covid 19에 가장 많은 확진자가 나온 곳은 인구수가 많은 US, India가 1,2위였음

중국이 왜 순위권 안에 없는지 궁금해서, 위의 자료에 중국을 추가해서 보았는데, 예상과 달리 굉장히 낮은 total 수치를 가지고 있었음. 이럴 경우 데이터의 신뢰성에 대해 의심할 만 하다.

In [99]:

covid.groupby("date").sum().new_cases

Out[99]:

date
2020-01        37493.0
2020-02       304387.0
2020-03      3557162.0
2020-04     10290649.0
2020-05     11776327.0
2020-06     17287360.0
2020-07     28649641.0
2020-08     32244386.0
2020-09     34966024.0
2020-10     51922678.0
2020-11     74467013.0
2020-12     82176550.0
2021-01     82395466.0
2021-02     47967457.0
2021-03     63534680.0
2021-04     94119668.0
2021-05     80545061.0
2021-06     46442899.0
2021-07     64331187.0
2021-08     81584162.0
2021-09     65240567.0
2021-10     54744350.0
2021-11     68848336.0
2021-12    111529439.0
2022-01    388495588.0
2022-02    179260175.0
Name: new_cases, dtype: float64

In [55]:

plt.plot(df2)
plt.show()

Date를 하루하루로 입력하니 너무 데이터가 방대해졌음. 월단위로 한번 해볼까?

In [57]:

df2

Out[57]:

date
2020-01-01           0.0
2020-01-02           0.0
2020-01-03           0.0
2020-01-04           0.0
2020-01-05           0.0
                 ...    
2022-02-13     6260275.0
2022-02-14     7732210.0
2022-02-15     7572471.0
2022-02-16    10836497.0
2022-02-17     8575864.0
Name: new_cases, Length: 779, dtype: float64

In [75]:

df3 = covid

In [76]:

df3 ["date"] = df3["date"].apply(lambda x : x[:7])

In [77]:

df3["date"]

Out[77]:

0         2020-02
1         2020-02
2         2020-02
3         2020-02
4         2020-02
           ...   
162602    2022-02
162603    2022-02
162604    2022-02
162605    2022-02
162606    2022-02
Name: date, Length: 162607, dtype: object

In [84]:

df4 = df3.groupby("date").sum().new_cases

In [89]:

plt.plot(df4)
plt.xticks(rotation=90)
plt.title("covid new cases by month")
plt.show()

In [214]:

from IPython.core.display import display, HTML

display(HTML("<style>.container { width:90% !important; }</style>"))

#창 맞추기위함

In [ ]: