(코로나 바이러스 예측)Analysis of COVID-19 data using Python

mport urllib
import datetime as dt
from matplotlib import pyplot as plt
import matplotlib
import pandas as pd
import seaborn as sns
url = "https://covid.ourworldindata.org/data/ecdc/full_data.csv"
CVD = pd.read_csv(url)
print(CVD.head(5))

        date     location  new_cases  new_deaths  total_cases  total_deaths  \
0 2019-12-31  Afghanistan        0.0         0.0          NaN           NaN   
1 2020-01-01  Afghanistan        0.0         0.0          NaN           NaN   
2 2020-01-02  Afghanistan        0.0         0.0          NaN           NaN   
3 2020-01-03  Afghanistan        0.0         0.0          NaN           NaN   
4 2020-01-04  Afghanistan        0.0         0.0          NaN           NaN   

   weekly_cases  weekly_deaths  biweekly_cases  biweekly_deaths  
0           NaN            NaN             NaN              NaN  
1           NaN            NaN             NaN              NaN  
2           NaN            NaN             NaN              NaN  
3           NaN            NaN             NaN              NaN  
4           NaN            NaN             NaN              NaN  
print(CVD.dtypes)
date                object
location            object
new_cases          float64
new_deaths         float64
total_cases        float64
total_deaths       float64
weekly_cases       float64
weekly_deaths      float64
biweekly_cases     float64
biweekly_deaths    float64
dtype: object
#날짜의 데이트 형식을 변환
CVD['date']  = [dt.datetime.strptime(x, '%Y-%m-%d') for x in CVD['date']]
print(CVD.dtypes)
date               datetime64[ns]
location                   object
new_cases                 float64
new_deaths                float64
total_cases               float64
total_deaths              float64
weekly_cases              float64
weekly_deaths             float64
biweekly_cases            float64
biweekly_deaths           float64
dtype: object
countries = ['United States', 'Spain', 'Italy', 'South Korea']
CVD_country = CVD[CVD.location.isin(countries)]
CVD_country
date	location	new_cases	new_deaths	total_cases	total_deaths	weekly_cases	weekly_deaths	biweekly_cases	biweekly_deaths
27241	2019-12-31	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
27242	2020-01-01	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
27243	2020-01-02	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
27244	2020-01-03	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
27245	2020-01-04	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
56361	2020-11-25	United States	170293.0	2224.0	12591165.0	259925.0	1231363.0	11238.0	2333339.0	20242.0
56362	2020-11-26	United States	186589.0	2341.0	12777754.0	262266.0	1247947.0	11729.0	2376622.0	20466.0
56363	2020-11-27	United States	106091.0	1189.0	12883845.0	263455.0	1166018.0	10900.0	2329044.0	21025.0
56364	2020-11-28	United States	207913.0	1404.0	13091758.0	264859.0	1177814.0	10446.0	2352144.0	20514.0
56365	2020-11-29	United States	154893.0	1204.0	13246651.0	266063.0	1157213.0	10164.0	2341760.0	20463.0
1339 rows × 10 columns

CVD_country.set_index('date', inplace= True)
CVD_country
location	new_cases	new_deaths	total_cases	total_deaths	weekly_cases	weekly_deaths	biweekly_cases	biweekly_deaths
date									
2019-12-31	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-01	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-02	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-03	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-04	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...
2020-11-25	United States	170293.0	2224.0	12591165.0	259925.0	1231363.0	11238.0	2333339.0	20242.0
2020-11-26	United States	186589.0	2341.0	12777754.0	262266.0	1247947.0	11729.0	2376622.0	20466.0
2020-11-27	United States	106091.0	1189.0	12883845.0	263455.0	1166018.0	10900.0	2329044.0	21025.0
2020-11-28	United States	207913.0	1404.0	13091758.0	264859.0	1177814.0	10446.0	2352144.0	20514.0
2020-11-29	United States	154893.0	1204.0	13246651.0	266063.0	1157213.0	10164.0	2341760.0	20463.0
1339 rows × 9 columns

CVD_country['mortality_rate'] = CVD_country['total_deaths']/CVD_country['total_cases']
<ipython-input-76-9aae57fb628b>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  CVD_country['mortality_rate'] = CVD_country['total_deaths']/CVD_country['total_cases']
CVD_country
location	new_cases	new_deaths	total_cases	total_deaths	weekly_cases	weekly_deaths	biweekly_cases	biweekly_deaths	mortality_rate
date										
2019-12-31	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-01	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-02	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-03	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2020-01-04	Italy	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
2020-11-25	United States	170293.0	2224.0	12591165.0	259925.0	1231363.0	11238.0	2333339.0	20242.0	0.020643
2020-11-26	United States	186589.0	2341.0	12777754.0	262266.0	1247947.0	11729.0	2376622.0	20466.0	0.020525
2020-11-27	United States	106091.0	1189.0	12883845.0	263455.0	1166018.0	10900.0	2329044.0	21025.0	0.020448
2020-11-28	United States	207913.0	1404.0	13091758.0	264859.0	1177814.0	10446.0	2352144.0	20514.0	0.020231
2020-11-29	United States	154893.0	1204.0	13246651.0	266063.0	1157213.0	10164.0	2341760.0	20463.0	0.020085
1339 rows × 10 columns

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14,20))

CVD_country.groupby('location')['new_cases'].plot(ax=axes[0,0], legend = True)
CVD_country.groupby('location')['new_deaths'].plot(ax=axes[0,1], legend = True)
CVD_country.groupby('location')['total_cases'].plot(ax=axes[1,0], legend = True)
CVD_country.groupby('location')['total_deaths'].plot(ax=axes[1,1], legend = True)
location
Italy            AxesSubplot(0.547727,0.125;0.352273x0.343182)
South Korea      AxesSubplot(0.547727,0.125;0.352273x0.343182)
Spain            AxesSubplot(0.547727,0.125;0.352273x0.343182)
United States    AxesSubplot(0.547727,0.125;0.352273x0.343182)
Name: total_deaths, dtype: object
axes[0,0].set_title("New Cases")
axes[0,1].set_title("New Deaths")
axes[1,0].set_title("Total Cases")
axes[1,1].set_title("Total Deaths")
Text(0.5, 1.0, 'Total Deaths')

rint(CVD.isnull().sum())
date                   0
location               0
new_cases            333
new_deaths           333
total_cases         3303
total_deaths       12940
weekly_cases        1132
weekly_deaths       1132
biweekly_cases      2637
biweekly_deaths     2637
dtype: int64
CVD.columns = ['date', 'Country', 'New Cases', 'New deaths', 'Total Cases', 'Total Deaths', 'weekly_cases','weekly_deaths','biweekly_cases','biweekly_deaths']
CVD
date	Country	New Cases	New deaths	Total Cases	Total Deaths	weekly_cases	weekly_deaths	biweekly_cases	biweekly_deaths
0	2019-12-31	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
1	2020-01-01	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
2	2020-01-02	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
3	2020-01-03	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
4	2020-01-04	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
59349	2020-11-25	Zimbabwe	90.0	1.0	9398.0	274.0	453.0	14.0	788.0	19.0
59350	2020-11-26	Zimbabwe	110.0	0.0	9508.0	274.0	527.0	13.0	841.0	19.0
59351	2020-11-27	Zimbabwe	115.0	0.0	9623.0	274.0	577.0	9.0	927.0	19.0
59352	2020-11-28	Zimbabwe	91.0	1.0	9714.0	275.0	594.0	10.0	949.0	18.0
59353	2020-11-29	Zimbabwe	108.0	0.0	9822.0	275.0	650.0	10.0	1036.0	18.0
59354 rows × 10 columns

# ~부정하는 것 
CVD_no_china = CVD.loc[~(CVD['Country'].isin(["China", "World"]))]
CVD_no_china
date	Country	New Cases	New deaths	Total Cases	Total Deaths	weekly_cases	weekly_deaths	biweekly_cases	biweekly_deaths
0	2019-12-31	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
1	2020-01-01	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
2	2020-01-02	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
3	2020-01-03	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
4	2020-01-04	Afghanistan	0.0	0.0	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...
59349	2020-11-25	Zimbabwe	90.0	1.0	9398.0	274.0	453.0	14.0	788.0	19.0
59350	2020-11-26	Zimbabwe	110.0	0.0	9508.0	274.0	527.0	13.0	841.0	19.0
59351	2020-11-27	Zimbabwe	115.0	0.0	9623.0	274.0	577.0	9.0	927.0	19.0
59352	2020-11-28	Zimbabwe	91.0	1.0	9714.0	275.0	594.0	10.0	949.0	18.0
59353	2020-11-29	Zimbabwe	108.0	0.0	9822.0	275.0	650.0	10.0	1036.0	18.0
58684 rows × 10 columns

CVD_no_china  = pd.DataFrame(CVD_no_china.groupby(['Country', 'date'])['Total Cases', 'Total Deaths'].sum()).reset_index()
<ipython-input-105-74dc02810ca5>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  CVD_no_china  = pd.DataFrame(CVD_no_china.groupby(['Country', 'date'])['Total Cases', 'Total Deaths'].sum()).reset_index()
CVD_no_china
Country	date	Total Cases	Total Deaths
0	Afghanistan	2019-12-31	0.0	0.0
1	Afghanistan	2020-01-01	0.0	0.0
2	Afghanistan	2020-01-02	0.0	0.0
3	Afghanistan	2020-01-03	0.0	0.0
4	Afghanistan	2020-01-04	0.0	0.0
...	...	...	...	...
58679	Zimbabwe	2020-11-25	9398.0	274.0
58680	Zimbabwe	2020-11-26	9508.0	274.0
58681	Zimbabwe	2020-11-27	9623.0	274.0
58682	Zimbabwe	2020-11-28	9714.0	275.0
58683	Zimbabwe	2020-11-29	9822.0	275.0
58684 rows × 4 columns

CVD_no_china = CVD_no_china.sort_values(by = ['Country', 'date'], ascending=False)
CVD_no_china
Country	date	Total Cases	Total Deaths
58683	Zimbabwe	2020-11-29	9822.0	275.0
58682	Zimbabwe	2020-11-28	9714.0	275.0
58681	Zimbabwe	2020-11-27	9623.0	274.0
58680	Zimbabwe	2020-11-26	9508.0	274.0
58679	Zimbabwe	2020-11-25	9398.0	274.0
...	...	...	...	...
4	Afghanistan	2020-01-04	0.0	0.0
3	Afghanistan	2020-01-03	0.0	0.0
2	Afghanistan	2020-01-02	0.0	0.0
1	Afghanistan	2020-01-01	0.0	0.0
0	Afghanistan	2019-12-31	0.0	0.0
58684 rows × 4 columns

#plot 함수만들자

def plot_bar(feature, value, title, df, size):
    f, ax = plt.subplots(1,1, figsize=(4*size,4))
    df = df.sort_values([value], ascending=False).reset_index(drop=True)
    g = sns.barplot(df[feature][0:10], df[value][0:10], palette='Set2')
    g.set_title("Number of {} - highest 10 values".format(title))
#    ax.set_xticklabels(ax.get_xticklabels(),rotation=90)
    plt.show()    
filtered_CVD_no_china = CVD_no_china.drop_duplicates(subset = ['Country'], keep='first')
plot_bar('Country', 'Total Cases', 'Total cases in the World except China', filtered_CVD_no_china, size=4)
plot_bar('Country', 'Total Deaths', 'Total deaths in the World except China', filtered_CVD_no_china, size=4)

filtered_CVD_no_china
Country	date	Total Cases	Total Deaths
58683	Zimbabwe	2020-11-29	9822.0	275.0
58429	Zambia	2020-11-29	17589.0	357.0
58173	Yemen	2020-11-29	2160.0	615.0
57939	Western Sahara	2020-11-29	766.0	1.0
57721	Wallis and Futuna	2020-11-29	3.0	0.0
...	...	...	...	...
1455	Angola	2020-11-29	15087.0	345.0
1202	Andorra	2020-11-29	6670.0	76.0
935	Algeria	2020-11-29	81212.0	2393.0
600	Albania	2020-11-29	36790.0	787.0
334	Afghanistan	2020-11-29	45844.0	1763.0
213 rows × 4 columns

def plot_world_aggregate(df, title='Aggregate plot', size=1):
    f, ax = plt.subplots(1,1, figsize=(4*size,2*size))
    g = sns.lineplot(x="date", y='Total Cases', data=df, color='blue', label='Total Cases')
    g = sns.lineplot(x="date", y='Total Deaths', data=df, color='red', label='Total Deaths')
    plt.xlabel('date')
    plt.ylabel(f'Total {title} cases')
    plt.xticks(rotation=90)
    plt.title(f'Total {title} cases')
    ax.grid(color='black', linestyle='dotted', linewidth=0.75)
    plt.show()  
CVD_no_china_aggregate = CVD_no_china.groupby(['date']).sum().reset_index()
CVD_no_china_aggregate
date	Total Cases	Total Deaths
0	2019-12-31	0.0	0.0
1	2020-01-01	0.0	0.0
2	2020-01-02	0.0	0.0
3	2020-01-03	0.0	0.0
4	2020-01-04	0.0	0.0
...	...	...	...
330	2020-11-25	59810576.0	1406042.0
331	2020-11-26	60460221.0	1418625.0
332	2020-11-27	61010116.0	1429263.0
333	2020-11-28	60612242.0	1412281.0
334	2020-11-29	59732850.0	1380975.0
335 rows × 3 columns

plot_world_aggregate(CVD_no_china_aggregate, 'Rest of the World except China', size=4)

def plot_aggregate_countries(df, countries, case_type='Total Cases', size=3, is_log=False):
    f, ax = plt.subplots(1,1, figsize=(4*size, 3*size))
    for country in countries:
        df_ = df[(df['Country']==country) & (df['date'] > '2020-02-15')] 
        g = sns.lineplot(x="date", y=case_type, data=df_,  label=country)  
        ax.text(max(df_['date']), max(df_[case_type]), str(country))
    plt.xlabel('date')
    plt.ylabel(f' {case_type} ')
    plt.title(f' {case_type} ')
    plt.xticks(rotation=90)
    if(is_log):
        ax.set(yscale="log")
    ax.grid(color='black', linestyle='dotted', linewidth=0.75)
    plt.show()  

CVD_country_aggregate = CVD_no_china.groupby(['Country', 'date']).sum().reset_index()

countries = ["United States", "Italy", "Spain", "South Korea", 
                         "France", "Germany", "Switzerland", "India"]
plot_aggregate_countries(CVD_country_aggregate, countries, case_type = 'Total Cases', size=4)    

plot_aggregate_countries(CVD_country_aggregate, countries, case_type = 'Total Deaths', size=4)

#log scale
plot_aggregate_countries(CVD_country_aggregate, countries, case_type = 'Total Cases', size=4, is_log=True)

def plot_mortality(df, title='Mainland China', size=1):
    f, ax = plt.subplots(1,1, figsize=(4*size,2*size))
    g = sns.lineplot(x="date", y='Mortality (Deaths/Cases)', data=df, color='blue', label='Mortality (Deaths / Total Cases)')
    plt.xlabel('date')
    plt.ylabel(f'Mortality {title} [%]')
    plt.xticks(rotation=90)
    plt.title(f'Mortality percent {title}\nCalculated as Deaths/Confirmed cases')
    ax.grid(color='black', linestyle='dashed', linewidth=1)
    plt.show()  

CVD_no_china_aggregate['Mortality (Deaths/Cases)'] = CVD_no_china_aggregate['Total Deaths'] / CVD_no_china_aggregate['Total Cases'] * 100
plot_mortality(CVD_no_china_aggregate, title = ' - Rest of the World except China', size = 3)