지난 게시글에서 데이터 처리 연습한것을 토대로,

날짜에 따라 국가별 코로나 사망자 수를 시각화하기 위해 데이터 처리 연습해봤다.

확실히 강의 내용 없이 혼자 하려니 쉽지 않다.

코드를 더 깔끔하게 작성할 수 있겠지만, 일단 이게 내 수준이니까 우선 수정없이 올려본다.

pandas_fnal_code_deaths

시간에 따른 국가별 사망자 추이를 시각화 해보자¶

Table은 Country/Flag/Deaths(시간순) 순서로 구성 필요¶

In [1]:

import pandas as pd
import os

path = ('../COVID-19-master/csse_covid_19_data/csse_covid_19_daily_reports/')
df = pd.read_csv(path + '01-22-2020.csv', encoding = 'utf-8-sig')

print(df.shape)
df.head()

(38, 6)

Out[1]:

	Province/State	Country/Region	Last Update	Confirmed	Deaths	Recovered
0	Anhui	Mainland China	1/22/2020 17:00	1.0	NaN	NaN
1	Beijing	Mainland China	1/22/2020 17:00	14.0	NaN	NaN
2	Chongqing	Mainland China	1/22/2020 17:00	6.0	NaN	NaN
3	Fujian	Mainland China	1/22/2020 17:00	1.0	NaN	NaN
4	Gansu	Mainland China	1/22/2020 17:00	NaN	NaN	NaN

In [2]:

df = pd.read_csv(path + '05-21-2020.csv', encoding = 'utf-8-sig')
df.head()

Out[2]:

	FIPS	Admin2	Province_State	Country_Region	Last_Update	Lat	Long_	Confirmed	Deaths	Active	Combined_Key
0	45001.0	Abbeville	South Carolina	US	2020-05-22 02:36:51	34.223334	-82.461707	36	0	36	Abbeville, South Carolina, US
1	22001.0	Acadia	Louisiana	US	2020-05-22 02:36:51	30.295065	-92.414197	269	15	254	Acadia, Louisiana, US
2	51001.0	Accomack	Virginia	US	2020-05-22 02:36:51	37.767072	-75.632346	709	11	698	Accomack, Virginia, US
3	16001.0	Ada	Idaho	US	2020-05-22 02:36:51	43.452658	-116.241552	792	23	769	Ada, Idaho, US
4	19001.0	Adair	Iowa	US	2020-05-22 02:36:51	41.330756	-94.471059	6	0	6	Adair, Iowa, US

- 1월과 5월의 csv Data 확인시 Format이 달라짐

- 어짜피 내가 필요한건, 국가(Country_Region, Country/Region), Death, 날짜

In [3]:

df[['Country_Region', 'Deaths']].groupby('Country_Region').sum().isnull()

Out[3]:

	Deaths
Country_Region
Afghanistan	False
Albania	False
Algeria	False
Andorra	False
Angola	False
...	...
West Bank and Gaza	False
Western Sahara	False
Yemen	False
Zambia	False
Zimbabwe	False

188 rows × 1 columns

In [ ]:

csse_covid_19_daily_reports 폴더에 있는 모든 Data를 받아오기

csse_covid_19_daily_reports 폴더에 있는 모든 Data를 불러오기
csv 파일에서 국가 컬러명, 사망자수만 가져오기
국가 컬럼명은 Country/Region으로 통일
null 값 있는 Column은 삭제하기
국가명은 표준 국가명으로 통일
dir : 'COVID-19-master/csse_covid_19_data/country_convert.json'
Country/Region으로 Groupby해서 값은 합해주기
새로운 파일 읽어질 때마다 기존 파일에 merge 하기

In [4]:

import json
import pandas as pd

with open('../COVID-19-master/csse_covid_19_data/country_convert.json', 'r', encoding ='utf-8-sig') as json_file:
    json_data = json.load(json_file)
    
# print(json_data)

def change_country_name(row):
    if row['Country/Region'] in json_data:
        row['Country/Region'] = json_data[row['Country/Region']]
    
    return row['Country/Region']

    
def read_df(file):
    
    path = ('../COVID-19-master/csse_covid_19_data/csse_covid_19_daily_reports/')
    
    df = pd.read_csv(path + file, encoding = 'utf-8-sig')
    
    try:
        df = df[['Country/Region', 'Deaths']]
    except:
        df = df[['Country_Region', 'Deaths']]
        df.columns = ['Country/Region', 'Deaths']
    
    df = df.dropna(subset = ['Deaths'])
    df['Country/Region'] = df.apply(change_country_name, axis = 1) # axis = 1 행순서로 들어감
    df = df.groupby('Country/Region').sum()    
    
#     print(df.head())
    
    return df
    

In [5]:

file_list = os.listdir(path)
csv_list = list()
i = 0

first_doc = True

for file in file_list:
    if file.split('.')[1] == 'csv':
        csv_list.append(file)
        
for file in csv_list:
    
    doc = read_df(file)
    
    file_date = file.split('.')[0].lstrip('0').replace('-','/')
    doc.columns = [file_date]
    
    # doc.columns[0] = file_date
    # 이렇게 적으면 'Index does not support mutable operations' 이런 Error 발생
    # 찾아보니, index 데이터 객체는 인덱싱을 활용한 값 할당이 안됨.
    # list 형태로 전부다 바꿔줘야함.
    
    
    if first_doc:
        final_doc = doc
        first_doc = False
    else:
        final_doc = pd.merge(final_doc, doc, how = 'outer', on = 'Country/Region')

final_doc = final_doc.fillna(0)    
final_doc.head()

Out[5]:

	1/22/2020	1/23/2020	1/24/2020	1/25/2020	1/26/2020	1/27/2020	1/28/2020	1/29/2020	1/30/2020	1/31/2020	...	6/08/2020	6/09/2020	6/10/2020	6/11/2020	6/12/2020	6/13/2020	6/14/2020	6/15/2020	6/16/2020	6/17/2020
Country/Region
China	17.0	18.0	26.0	42.0	56.0	82.0	131.0	133.0	171.0	213.0	...	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0
Australia	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0
Cambodia	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Canada	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	7910.0	7970.0	8038.0	8071.0	8125.0	8183.0	8218.0	8228.0	8271.0	8312.0
Finland	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	323.0	324.0	324.0	325.0	325.0	325.0	326.0	326.0	326.0	326.0

5 rows × 148 columns

In [6]:

# final_doc.shape
final_doc.astype('int64')

Out[6]:

	1/22/2020	1/23/2020	1/24/2020	1/25/2020	1/26/2020	1/27/2020	1/28/2020	1/29/2020	1/30/2020	1/31/2020	...	6/08/2020	6/09/2020	6/10/2020	6/11/2020	6/12/2020	6/13/2020	6/14/2020	6/15/2020	6/16/2020	6/17/2020
Country/Region
China	17	18	26	42	56	82	131	133	171	213	...	4638	4638	4638	4638	4638	4638	4638	4638	4638	4638
Australia	0	0	0	0	0	0	0	0	0	0	...	102	102	102	102	102	102	102	102	102	102
Cambodia	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
Canada	0	0	0	0	0	0	0	0	0	0	...	7910	7970	8038	8071	8125	8183	8218	8228	8271	8312
Finland	0	0	0	0	0	0	0	0	0	0	...	323	324	324	325	325	325	326	326	326	326
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Sao Tome and Principe	0	0	0	0	0	0	0	0	0	0	...	12	12	12	12	12	12	12	12	12	12
Yemen	0	0	0	0	0	0	0	0	0	0	...	112	127	129	136	139	160	164	208	214	244
Comoros	0	0	0	0	0	0	0	0	0	0	...	2	2	2	2	2	2	2	2	3	3
Tajikistan	0	0	0	0	0	0	0	0	0	0	...	48	48	48	49	49	50	50	50	50	51
Lesotho	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

187 rows × 148 columns

Country/Region와 국기 이미지 불러오기 위한 iso2 매칭

한가지 문제가 있는데, Namibia 국가의 iso2 값이 'NA'이다. 결측치여서 NA가 아니라 iso2 값이 'NA'인 것이다 그래서 그냥 불러오면, NA를 결측치로 인식하기 때문에 옵션을 사용해야한다.

. keep_default_na = False : default 결측치 변환데이터를 사용하지 않고, na_values로 지정한 데이터만 결측치로 변환

. na_values = "" : 결측치로 변환할 값을 지정(여기서는 0(공백)을 결측치로 보면됨)

In [7]:

country_info = pd.read_csv("../COVID-19-master/csse_covid_19_data/UID_ISO_FIPS_LookUp_Table.csv", encoding='utf-8-sig', keep_default_na = False, na_values='')
country_info[country_info['Country_Region'] == 'Namibia']

Out[7]:

	Unnamed: 0	Unnamed: 0.1	UID	iso2	iso3	code3	FIPS	Admin2	Province_State	Country_Region	Lat	Long_	Combined_Key
115	115	115	516.0	NA	NAM	516.0	NaN	NaN	NaN	Namibia	-22.9576	18.4904	Namibia

나는 국가 컬럼과 그에따른 iso2 값만 필요하다

In [8]:

country_info = country_info[['Country_Region', 'iso2']]
country_info.columns = ['Country/Region', 'iso2']
# drop_duplicates 로 country/region Column의 중복 데이터 제거해주고, 중복 제거시 마지막 data를 남긴다
country_info = country_info.drop_duplicates(subset = 'Country/Region', keep = 'last')

country_info.head()

Out[8]:

	Country/Region	iso2
0	Botswana	BW
1	Burundi	BI
2	Sierra Leone	SL
3	Afghanistan	AF
4	Albania	AL

일별 사망자 데이터 머지해놓은 dataframe과 Country_info를 merge해주자

In [9]:

final_graph_doc = pd.merge(final_doc, country_info, how = 'left', on = 'Country/Region')

# 국가와 iso2 매칭후에 iso2 결측값인 row를 확인해보자
# 필요하면 iso2를 수작업으로 매칭해주던지, 필요없다 생각되면 삭제하자
# 나는 삭제할거야
final_graph_doc[final_graph_doc['iso2'].isnull()]

Out[9]:

	Country/Region	...	6/09/2020	6/10/2020	6/11/2020	6/12/2020	6/13/2020	6/14/2020	6/15/2020	6/16/2020	6/17/2020	iso2
26	Others	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	NaN
109	Cruise Ship	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	NaN
173	Diamond Princess	...	13.0	13.0	13.0	13.0	13.0	13.0	13.0	13.0	13.0	NaN
178	MS Zaandam	...	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	2.0	NaN
182	Sao Tome and Principe	...	12.0	12.0	12.0	12.0	12.0	12.0	12.0	12.0	12.0	NaN
183	Yemen	...	127.0	129.0	136.0	139.0	160.0	164.0	208.0	214.0	244.0	NaN
184	Comoros	...	2.0	2.0	2.0	2.0	2.0	2.0	2.0	3.0	3.0	NaN
185	Tajikistan	...	48.0	48.0	49.0	49.0	50.0	50.0	50.0	50.0	51.0	NaN
186	Lesotho	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	NaN

9 rows × 150 columns

In [10]:

final_graph_doc.dropna(subset = ['iso2']).isnull().sum()
# 이렇게 하면 iso2의 null 갑이 0가 된걸 볼 수 있다

Out[10]:

Country/Region    0
1/22/2020         0
1/23/2020         0
1/24/2020         0
1/25/2020         0
                 ..
6/14/2020         0
6/15/2020         0
6/16/2020         0
6/17/2020         0
iso2              0
Length: 150, dtype: int64

In [11]:

final_graph_doc = final_graph_doc.dropna(subset = ['iso2'])
print(final_graph_doc.isnull().sum())
final_graph_doc.head()

Country/Region    0
1/22/2020         0
1/23/2020         0
1/24/2020         0
1/25/2020         0
                 ..
6/14/2020         0
6/15/2020         0
6/16/2020         0
6/17/2020         0
iso2              0
Length: 150, dtype: int64

Out[11]:

	Country/Region	1/22/2020	1/23/2020	1/24/2020	1/25/2020	1/26/2020	1/27/2020	1/28/2020	1/29/2020	1/30/2020	...	6/09/2020	6/10/2020	6/11/2020	6/12/2020	6/13/2020	6/14/2020	6/15/2020	6/16/2020	6/17/2020	iso2
0	China	17.0	18.0	26.0	42.0	56.0	82.0	131.0	133.0	171.0	...	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	CN
1	Australia	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	AU
2	Cambodia	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	KH
3	Canada	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	7970.0	8038.0	8071.0	8125.0	8183.0	8218.0	8228.0	8271.0	8312.0	CA
4	Finland	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	324.0	324.0	325.0	325.0	325.0	326.0	326.0	326.0	326.0	FI

5 rows × 150 columns

국가별 국기 image 를 가져오기 위해서 iso2를 토대로 url을 만들어주자

In [12]:

flag_url = 'https://www.countryflags.io/' + final_graph_doc['iso2'] + '/flat/64.png'

final_graph_doc['iso2'] = flag_url
final_graph_doc.head()

Out[12]:

	Country/Region	1/22/2020	1/23/2020	1/24/2020	1/25/2020	1/26/2020	1/27/2020	1/28/2020	1/29/2020	1/30/2020	...	6/09/2020	6/10/2020	6/11/2020	6/12/2020	6/13/2020	6/14/2020	6/15/2020	6/16/2020	6/17/2020	iso2
0	China	17.0	18.0	26.0	42.0	56.0	82.0	131.0	133.0	171.0	...	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	https://www.countryflags.io/CN/flat/64.png
1	Australia	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	https://www.countryflags.io/AU/flat/64.png
2	Cambodia	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	https://www.countryflags.io/KH/flat/64.png
3	Canada	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	7970.0	8038.0	8071.0	8125.0	8183.0	8218.0	8228.0	8271.0	8312.0	https://www.countryflags.io/CA/flat/64.png
4	Finland	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	324.0	324.0	325.0	325.0	325.0	326.0	326.0	326.0	326.0	https://www.countryflags.io/FI/flat/64.png

5 rows × 150 columns

그래프를 그리려면 table column 순서가 국가/iso2/사망자 데이터... 여야한다

iso2 column을 Coutnry/Region 옆으로 옮기자

In [13]:

cols = final_graph_doc.columns.tolist() # tolist()를 쓰면 list 타입으로 변수에 저장
cols.remove('iso2')
cols.insert(1, 'iso2')
cols

Out[13]:

['Country/Region',
 'iso2',
 '1/22/2020',
 '1/23/2020',
 '1/24/2020',
 '1/25/2020',
 '1/26/2020',
 '1/27/2020',
 '1/28/2020',
 '1/29/2020',
 '1/30/2020',
 '1/31/2020',
 '2/01/2020',
 '2/02/2020',
 '2/03/2020',
 '2/04/2020',
 '2/05/2020',
 '2/06/2020',
 '2/07/2020',
 '2/08/2020',
 '2/09/2020',
 '2/10/2020',
 '2/11/2020',
 '2/12/2020',
 '2/13/2020',
 '2/14/2020',
 '2/15/2020',
 '2/16/2020',
 '2/17/2020',
 '2/18/2020',
 '2/19/2020',
 '2/20/2020',
 '2/21/2020',
 '2/22/2020',
 '2/23/2020',
 '2/24/2020',
 '2/25/2020',
 '2/26/2020',
 '2/27/2020',
 '2/28/2020',
 '2/29/2020',
 '3/01/2020',
 '3/02/2020',
 '3/03/2020',
 '3/04/2020',
 '3/05/2020',
 '3/06/2020',
 '3/07/2020',
 '3/08/2020',
 '3/09/2020',
 '3/10/2020',
 '3/11/2020',
 '3/12/2020',
 '3/13/2020',
 '3/14/2020',
 '3/15/2020',
 '3/16/2020',
 '3/17/2020',
 '3/18/2020',
 '3/19/2020',
 '3/20/2020',
 '3/21/2020',
 '3/22/2020',
 '3/23/2020',
 '3/24/2020',
 '3/25/2020',
 '3/26/2020',
 '3/27/2020',
 '3/28/2020',
 '3/29/2020',
 '3/30/2020',
 '3/31/2020',
 '4/01/2020',
 '4/02/2020',
 '4/03/2020',
 '4/04/2020',
 '4/05/2020',
 '4/06/2020',
 '4/07/2020',
 '4/08/2020',
 '4/09/2020',
 '4/10/2020',
 '4/11/2020',
 '4/12/2020',
 '4/13/2020',
 '4/14/2020',
 '4/15/2020',
 '4/16/2020',
 '4/17/2020',
 '4/18/2020',
 '4/19/2020',
 '4/20/2020',
 '4/21/2020',
 '4/22/2020',
 '4/23/2020',
 '4/24/2020',
 '4/25/2020',
 '4/26/2020',
 '4/27/2020',
 '4/28/2020',
 '4/29/2020',
 '4/30/2020',
 '5/01/2020',
 '5/02/2020',
 '5/03/2020',
 '5/04/2020',
 '5/05/2020',
 '5/06/2020',
 '5/07/2020',
 '5/08/2020',
 '5/09/2020',
 '5/10/2020',
 '5/11/2020',
 '5/12/2020',
 '5/13/2020',
 '5/14/2020',
 '5/15/2020',
 '5/16/2020',
 '5/17/2020',
 '5/18/2020',
 '5/19/2020',
 '5/20/2020',
 '5/21/2020',
 '5/22/2020',
 '5/23/2020',
 '5/24/2020',
 '5/25/2020',
 '5/26/2020',
 '5/27/2020',
 '5/28/2020',
 '5/29/2020',
 '5/30/2020',
 '5/31/2020',
 '6/01/2020',
 '6/02/2020',
 '6/03/2020',
 '6/04/2020',
 '6/05/2020',
 '6/06/2020',
 '6/07/2020',
 '6/08/2020',
 '6/09/2020',
 '6/10/2020',
 '6/11/2020',
 '6/12/2020',
 '6/13/2020',
 '6/14/2020',
 '6/15/2020',
 '6/16/2020',
 '6/17/2020']

In [14]:

cols[1]

Out[14]:

'iso2'

In [15]:

# 변환된 Columns 순서로 final_graph_doc 재할당

final_graph_doc = final_graph_doc[cols]
final_graph_doc.head()

# Column 이름 바꿔주기 iso2 -> Country_Flag

# print(cols[1])
# cols[1] = 'Country_Flag' # cols가 list type이어서 인덱싱으로 값변환 가능

# final_graph_doc.columns = [cols] # dataframe index는 list로 한번에 바꿔줘야함

# final_graph_doc.head()

Out[15]:

	Country/Region	iso2	1/22/2020	1/23/2020	1/24/2020	1/25/2020	1/26/2020	1/27/2020	1/28/2020	1/29/2020	...	6/08/2020	6/09/2020	6/10/2020	6/11/2020	6/12/2020	6/13/2020	6/14/2020	6/15/2020	6/16/2020	6/17/2020
0	China	https://www.countryflags.io/CN/flat/64.png	17.0	18.0	26.0	42.0	56.0	82.0	131.0	133.0	...	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0
1	Australia	https://www.countryflags.io/AU/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0
2	Cambodia	https://www.countryflags.io/KH/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	Canada	https://www.countryflags.io/CA/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	7910.0	7970.0	8038.0	8071.0	8125.0	8183.0	8218.0	8228.0	8271.0	8312.0
4	Finland	https://www.countryflags.io/FI/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	323.0	324.0	324.0	325.0	325.0	325.0	326.0	326.0	326.0	326.0

5 rows × 150 columns

In [16]:

cols[1] = 'Country_Flag'
final_graph_doc.columns = [cols]
final_graph_doc.head()

Out[16]:

	Country/Region	Country_Flag	1/22/2020	1/23/2020	1/24/2020	1/25/2020	1/26/2020	1/27/2020	1/28/2020	1/29/2020	...	6/08/2020	6/09/2020	6/10/2020	6/11/2020	6/12/2020	6/13/2020	6/14/2020	6/15/2020	6/16/2020	6/17/2020
0	China	https://www.countryflags.io/CN/flat/64.png	17.0	18.0	26.0	42.0	56.0	82.0	131.0	133.0	...	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0	4638.0
1	Australia	https://www.countryflags.io/AU/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0	102.0
2	Cambodia	https://www.countryflags.io/KH/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	Canada	https://www.countryflags.io/CA/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	7910.0	7970.0	8038.0	8071.0	8125.0	8183.0	8218.0	8228.0	8271.0	8312.0
4	Finland	https://www.countryflags.io/FI/flat/64.png	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	323.0	324.0	324.0	325.0	325.0	325.0	326.0	326.0	326.0	326.0

5 rows × 150 columns

완성된 Data csv 파일로 추출

In [17]:

final_graph_doc.to_csv('./final_graph_doc.csv')

In [ ]:

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