dataVis/ourWorldInData.py at master · DimbyTa/dataVis · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
import scrappingFunction as sc

html = sc.getHTML("https://covid.ourworldindata.org/data/owid-covid-data.csv")

tempD = html.read()

html.close()

globalData = sc.byteStreamtoDataFrame(tempD,"utf-8")


globalData['iso_code'] = globalData['iso_code'].fillna("unknown")
globalData['continent'] = globalData['continent'].fillna("unknown")
globalData = globalData.fillna(0)
test = ['total_cases','new_cases','total_deaths','new_deaths', 'new_deaths_smoothed','new_cases_smoothed',
'total_cases_per_million',
'new_cases_per_million','new_cases_smoothed_per_million',
'total_deaths_per_million',
'new_deaths_per_million','new_deaths_smoothed_per_million',
'new_tests',
'total_tests',
'total_tests_per_thousand',
'new_tests_per_thousand',
'new_tests_smoothed',
'new_tests_smoothed_per_thousand',
'tests_per_case',
'positive_rate',
'stringency_index',
'population',
'population_density',
'median_age',
'aged_65_older',
'aged_70_older',
'gdp_per_capita',
'extreme_poverty',
'cardiovasc_death_rate',
'diabetes_prevalence',
'female_smokers',
'male_smokers',
'handwashing_facilities',
'hospital_beds_per_thousand',
'life_expectancy']

for column in test:
    #print(column)
    tp = []
    for data in globalData[column]:
        tp.append(str(data))
    globalData[column] = tp
print(globalData.info())
sc.DataFrameToTableGlobal(globalData)