cowidev.vax.incremental

cowidev.vax.incremental.africacdc

class cowidev.vax.incremental.africacdc.AfricaCDC(skip_who: bool = False)

Bases: object

_base_url = 'https://services8.arcgis.com/vWozsma9VzGndzx7/ArcGIS/rest/services/Admin_Boundaries_Africa_corr_Go_Vaccine_DB_JOIN/FeatureServer/0'

_map_vaccines(vaccine_raw: str)

_parse_date()

columns_rename = {'ADM0_SOVRN': 'location', 'Booster': 'total_boosters', 'FullyVacc': 'people_fully_vaccinated', 'TotAmtAdmi': 'total_vaccinations', 'VacAd1Dose': 'people_vaccinated'}

columns_use = ['ADM0_SOVRN', 'TotAmtAdmi', 'FullyVacc', 'VacAd1Dose', 'Booster', 'ISO_3_CODE', 'VacAd2Dose', 'VaccApprov']

export()

increment_countries(df: DataFrame)

pipe_date(df: DataFrame) → DataFrame

pipe_exclude_observations(df: DataFrame) → DataFrame

pipe_filter_columns(df: DataFrame) → DataFrame

pipe_filter_countries(df: DataFrame, countries: dict) → DataFrame

Get rows from selected countries.

pipe_one_dose_correction(df: DataFrame) → DataFrame

pipe_rename(df: DataFrame) → DataFrame

pipe_select_out_cols(df: DataFrame) → DataFrame

pipe_source(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipe_vaccine_who(df: DataFrame) → DataFrame

pipeline(df: DataFrame, countries: dict = {'Angola': 'Angola', 'Botswana': 'Botswana', 'Burkina Faso': 'Burkina Faso', 'Burundi': 'Burundi', 'Central African Republic': 'Central African Republic', 'Chad': 'Chad', 'Congo': 'Congo', 'Djibouti': 'Djibouti', 'Eswatini': 'Eswatini', 'Gabon': 'Gabon', 'Ghana': 'Ghana', 'Lesotho': 'Lesotho', 'Liberia': 'Liberia', 'Libya': 'Libya', 'Madagascar': 'Madagascar', 'Mauritania': 'Mauritania', 'Mauritius': 'Mauritius', 'Mozambique': 'Mozambique', 'Namibia': 'Namibia', 'Niger': 'Niger', 'Nigeria': 'Nigeria', 'Rwanda': 'Rwanda', 'Senegal': 'Senegal', 'Sudan': 'Sudan', 'Zambia': 'Zambia'}, exclude=True) → DataFrame

read() → DataFrame

property source_url

property source_url_date

source_url_ref = 'https://africacdc.org/covid-19-vaccination/'

cowidev.vax.incremental.africacdc.main()

cowidev.vax.incremental.antigua_barbuda

class cowidev.vax.incremental.antigua_barbuda.AntiguaBarbuda

Bases: object

_get_elements(soup)

_parse_date(dose1_elem, dose2_elem)

_parse_metric(elem)

export()

Generalized.

location = 'Antigua and Barbuda'

parse_data(soup)

pipe_location(ds: Series) → Series

pipe_people_vaccinated(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(df: Series) → Series

read() → DataFrame

regex = {'date': '\\[Updated on ([a-zA-Z]+ \\d{1,2}, 202\\d)\\]'}

source_url = 'https://covid19.gov.ag'

cowidev.vax.incremental.antigua_barbuda.main()

cowidev.vax.incremental.aruba

cowidev.vax.incremental.aruba.add_totals(ds: Series) → Series

cowidev.vax.incremental.aruba.enrich_date(ds: Series) → Series

cowidev.vax.incremental.aruba.enrich_location(ds: Series) → Series

cowidev.vax.incremental.aruba.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.aruba.main()

cowidev.vax.incremental.aruba.pipeline(ds: Series) → Series

cowidev.vax.incremental.aruba.read(source: str) → Series

cowidev.vax.incremental.azerbaijan

class cowidev.vax.incremental.azerbaijan.Azerbaijan

Bases: CountryVaxBase

_parse_data(soup: BeautifulSoup) → dict

get data from the source page.

_parse_date(text: str) → str

Parse date from text.

_parse_metrics(text: str) → tuple

Parse metrics from text.

_parse_pdf_link(soup: BeautifulSoup) → str

Parse pdf link from source page.

_parse_pdf_text(url: str) → str

Parse pdf text from url.

enrich_location(ds: Series) → Series

Enrich data with locationß.

enrich_vaccine(ds: Series) → Series

Enrich data with vaccine names.

export()

Export data to csv.

location: str = 'Azerbaijan'

pipeline(df: DataFrame) → DataFrame

Pipeline for data.

read() → Series

Read data from source.

regex = {'date': '(\\d{2}\\.\\d{2}\\.20\\d{2})', 'doses': '\\"Buster\\" doza vaksinlərin sayı (\\d+) (\\d+) (\\d+) (\\d+) (\\d+) Gün', 'title': 'Vaksinasiya'}

source_url = 'https://koronavirusinfo.az'

cowidev.vax.incremental.azerbaijan.main()

cowidev.vax.incremental.bahrain

class cowidev.vax.incremental.bahrain.Bahrain

Bases: object

_parse_data(soup: BeautifulSoup) → Series

export()

location: str = 'Bahrain'

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://healthalert.gov.bh/en/'

cowidev.vax.incremental.bahrain.main()

cowidev.vax.incremental.bangladesh

class cowidev.vax.incremental.bangladesh.Bangladesh

Bases: object

_parse_metrics(soup)

_parse_metrics_raw(soup, raise_err=True)

_parse_single_doses()

_parse_vaccines(soup)

export()

location: str = 'Bangladesh'

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'http://103.247.238.92/webportal/pages/covid19-vaccination-update.php'

vaccines_rename = {'AstraZeneca': 'Oxford/AstraZeneca', 'Janssen (Johnson & Johnson)': 'Johnson&Johnson', 'Moderna': 'Moderna', 'Pfizer': 'Pfizer/BioNTech', 'Pfizer-PF (Comirnaty)': 'Pfizer/BioNTech', 'Sinopharm': 'Sinopharm/Beijing', 'Sinovac': 'Sinovac'}

cowidev.vax.incremental.bangladesh.main()

cowidev.vax.incremental.barbados

class cowidev.vax.incremental.barbados.Barbados

Bases: CountryVaxBase

_parse_data(soup: BeautifulSoup) → DataFrame

Parse data from soup

_parse_date(soup: BeautifulSoup) → str

Parse date from soup

_parse_metrics(soup: BeautifulSoup) → int

Parse metrics from soup

export()

Export data to csv

location: str = 'Barbados'

pipe_vaccine(df: DataFrame) → DataFrame

Pipes vaccine names for main data.

pipeline(df: DataFrame) → DataFrame

Pipeline for data processing

read() → DataFrame

Read data from source

regex: dict = {'people_fully_vaccinated': 'fully? (?:vaccinated|vaccinated persons) is ([\\d,\\s]+)', 'people_vaccinated': 'at least one dose is ([\\d,\\s]+)', 'title': 'COVID-19 Update'}

source_url: str = 'https://gisbarbados.gov.bb/top-stories/'

source_url_ref: str = None

cowidev.vax.incremental.barbados.main()

cowidev.vax.incremental.brazil

class cowidev.vax.incremental.brazil.Brazil

Bases: object

export()

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read()

cowidev.vax.incremental.brazil.main()

cowidev.vax.incremental.bulgaria

class cowidev.vax.incremental.bulgaria.Bulgaria

Bases: CountryVaxBase

_parse_data(soup: BeautifulSoup) → Series

export()

location: str = 'Bulgaria'

pipe_date(ds: Series) → Series

pipe_index(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → DataFrame

read() → Series

source_url: str = 'https://coronavirus.bg/bg/statistika'

cowidev.vax.incremental.bulgaria.main()

cowidev.vax.incremental.china

class cowidev.vax.incremental.china.China

Bases: CountryVaxBase

_get_links(driver)

_get_links_complete(driver)

_parse_data(driver, url)

_parse_data_complete(driver, url)

chinese: str = '[\\u4e00-\\u9fff、（）]*'

export()

location: str = 'China'

metric: str = '((?:\\d+亿零?)?[\\d\\.]+万)'

metric_ignore: str = '(?:\\d+亿[\\u4e00-\\u96f5\\u96f7-\\u9fff，]{1,5})?'

month_day: str = '，?(?:\\d{2,4}年)?(\\d{1,2})月(\\d{1,2})[\\u4e00-\\u9fff，]{1,5}(?:\\d+个)?'

num_links_complete: int = 3

pipe_metadata(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

pipeline_merge(df_complete: DataFrame, df_last: DataFrame, df: DataFrame) → DataFrame

read(last_update: str) → DataFrame

read_complete() → DataFrame

regex: dict = {'date': '截至(20\\d{2})年(\\d{1,2})月(\\d{1,2})日', 'title': '新冠病毒疫苗接种情况', 'total_vaccinations': '([\\d\\.]+\\s*万)剂次'}

regex_complete: dict = {'boosters': '加强免疫[\\u4e00-\\u9fff、（）]*接种[\\u4e00-\\u9fff、（）]*(?:\\d+亿[\\u4e00-\\u96f5\\u96f7-\\u9fff，]{1,5})?((?:\\d+亿零?)?[\\d\\.]+万)', 'fully': '全程接种[\\u4e00-\\u9fff、（）]*(?:\\d+亿[\\u4e00-\\u96f5\\u96f7-\\u9fff，]{1,5})?((?:\\d+亿零?)?[\\d\\.]+万)', 'summary': '截[\\u4e00-\\u9fff、（）]*，?(?:\\d{2,4}年)?(\\d{1,2})月(\\d{1,2})[\\u4e00-\\u9fff，]{1,5}(?:\\d+个)?[\\u4e00-\\u9fff、（）]*接种[\\u4e00-\\u9fff、（）]*(?:\\d+亿[\\u4e00-\\u96f5\\u96f7-\\u9fff，]{1,5})?((?:\\d+亿零?)?[\\d\\.]+万)剂', 'title': '国务院(?:联防联控机制|新闻办公室)(20\\d{2})年(\\d{1,2})月(\\d{1,2})日新闻发布会', 'vaccinated': '接种[\\u4e00-\\u9fff、（）]*总人数[\\u4e00-\\u9fff、（）]*(?:\\d+亿[\\u4e00-\\u96f5\\u96f7-\\u9fff，]{1,5})?((?:\\d+亿零?)?[\\d\\.]+万)'}

source_url: str = 'http://www.nhc.gov.cn/xcs/yqjzqk/list_gzbd.shtml'

source_url_complete: str = 'http://www.nhc.gov.cn/xcs/s2906/new_list.shtml'

timeout: int = 30

cowidev.vax.incremental.china.main()

cowidev.vax.incremental.costa_rica

class cowidev.vax.incremental.costa_rica.CostaRica

Bases: object

_parse_data(soup: BeautifulSoup) → Series

_parse_date(soup)

_parse_table(soup)

export()

location: str = 'Costa Rica'

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://www.ccss.sa.cr/web/coronavirus/vacunacion'

cowidev.vax.incremental.costa_rica.main()

cowidev.vax.incremental.croatia

cowidev.vax.incremental.croatia.enrich_location(ds: Series) → Series

cowidev.vax.incremental.croatia.enrich_source(ds: Series) → Series

cowidev.vax.incremental.croatia.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.croatia.main()

cowidev.vax.incremental.croatia.pipeline(ds: Series) → Series

cowidev.vax.incremental.croatia.read(source: str) → Series

cowidev.vax.incremental.cuba

class cowidev.vax.incremental.cuba.Cuba

Bases: object

_parse_data(soup: BeautifulSoup) → Series

_parse_date(soup)

_parse_metrics(soup)

export()

pipe_vaccine(ds: Series) → Series

pipeline(df: Series) → Series

read() → Series

cowidev.vax.incremental.cuba.main()

cowidev.vax.incremental.curacao

class cowidev.vax.incremental.curacao.Curacao

Bases: object

_parse_data() → dict

export()

location = 'Curacao'

pipe_date(ds: Series) → Series

pipe_total_vaccinations(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url = 'https://bakuna-counter.ibis-management.com/init/'

source_url_ref = 'https://bakuna.cw/'

vaccine = 'Pfizer/BioNTech, Moderna'

cowidev.vax.incremental.curacao.main()

cowidev.vax.incremental.cyprus

cowidev.vax.incremental.cyprus.enrich_location(ds: Series) → Series

cowidev.vax.incremental.cyprus.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.cyprus.main()

cowidev.vax.incremental.cyprus.pipeline(ds: Series) → Series

cowidev.vax.incremental.cyprus.read(source: str) → Series

cowidev.vax.incremental.dominican_republic

class cowidev.vax.incremental.dominican_republic.DominicanRepublic

Bases: CountryVaxBase

_parse_date(driver)

_parse_metrics(driver)

export()

location: str = 'Dominican Republic'

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read()

source_url = 'https://vacunate.gob.do'

source_url_ref = 'https://vacunate.gob.do'

cowidev.vax.incremental.dominican_republic._find_h3(driver)

cowidev.vax.incremental.dominican_republic._find_potential_metric_elements(h3)

cowidev.vax.incremental.dominican_republic._find_potential_metrics(driver)

cowidev.vax.incremental.dominican_republic.main()

cowidev.vax.incremental.el_salvador

class cowidev.vax.incremental.el_salvador.ElSalvador

Bases: object

_get_infogram_value(infogram_data: dict, field_id: str, join_text: bool = False)

_parse_boosters(infogram_data: dict) → int

_parse_people_fully_vaccinated(infogram_data: dict) → int

_parse_people_vaccinated(infogram_data: dict) → int

_parse_total_vaccinations(infogram_data: dict) → int

export()

location: str = 'El Salvador'

parse_infogram_data(soup: BeautifulSoup) → dict

parse_infogram_date(infogram_data: dict) → str

parse_infogram_link(soup: BeautifulSoup) → str

pipe_location(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://covid19.gob.sv/'

cowidev.vax.incremental.el_salvador.main()

cowidev.vax.incremental.faeroe_islands

class cowidev.vax.incremental.faeroe_islands.FaeroeIslands

Bases: object

export()

location: str = 'Faeroe Islands'

pipe_format_date(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_metrics(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://corona.fo/json/stats'

source_url_ref: str = 'https://corona.fo/api'

cowidev.vax.incremental.faeroe_islands.main()

cowidev.vax.incremental.fiji

class cowidev.vax.incremental.fiji.Fiji

Bases: CountryVaxBase

__element = None

_get_list_of_elements(soup: BeautifulSoup) → None

Get the relevant elements list from the source page.

_get_relevant_element_and_year() → tuple

Get the relevant element and year from the element list.

_get_text_from_url(url: str) → str

Extract text from the url.

_num_max_pages: int = 3

_num_rows_per_page: int = 3

_parse_data(soup: BeautifulSoup) → tuple

Get data from the source page.

_parse_date_from_text(year: str, text: str) → str

Get date from relevant element.

_parse_link_from_element(elem: Tag) → str

Get link from relevant element.

_parse_metrics(text: str) → int

Get metrics from news text.

export()

Exports data to csv.

location: str = 'Fiji'

pipe_vaccine(df: DataFrame) → DataFrame

Pipes vaccine names.

pipeline(df: DataFrame) → DataFrame

Pipeline for data.

read() → DataFrame

Read data from source.

regex = {'booster': '(\\d+) individuals have so far received booster doses.', 'date': 'tests have been reported for (\\w+ \\d+)', 'title': 'COVID-19 Update', 'year': '\\d{4}'}

source_url: str = 'https://www.health.gov.fj/page/'

source_url_ref: str = ''

cowidev.vax.incremental.fiji.check_booster()

cowidev.vax.incremental.fiji.main()

cowidev.vax.incremental.finland

class cowidev.vax.incremental.finland.Finland

Bases: CountryVaxBase

export()

location: str = 'Finland'

metrics_mapping: dict = {'Booster dose': 'total_boosters', 'First dose': 'people_vaccinated', 'Second dose': 'people_fully_vaccinated', 'Third dose (NOT booster)': 'third_dose'}

pipe_date(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://sampo.thl.fi/pivot/prod/en/vaccreg/cov19cov/fact_cov19cov.csv?row=vacprod-533726&row=measure-533175.&column=cov_vac_dose-533174&'

source_url_ref: str = 'https://sampo.thl.fi/pivot/prod/en/vaccreg/cov19cov/fact_cov19cov'

vaccine_mapping: dict = {'COVID-19 Vaccine Janssen (JANSSEN-CILAG)': 'Johnson&Johnson', 'Comirnaty (BioNTech)': 'Pfizer/BioNTech', 'Spikevax (MODERNA)': 'Moderna', 'Vaxzevria (AstraZeneca)': 'Oxford/AstraZeneca'}

cowidev.vax.incremental.finland.main()

cowidev.vax.incremental.gabon

class cowidev.vax.incremental.gabon.Gabon

Bases: object

export()

Generalized.

location = 'Gabon'

parse_data(soup: BeautifulSoup) → Series

pipe_location(ds: Series) → Series

pipe_people_vaccinated(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(df: Series) → Series

read() → DataFrame

regex = {'date': '(\\d{1,2}-\\d{1,2}-202\\d) \\d{1,2}:\\d{1,2}:\\d{1,2}'}

source_url = 'https://monitoring-covid19gabon.ga/'

cowidev.vax.incremental.gabon.main()

cowidev.vax.incremental.georgia

class cowidev.vax.incremental.georgia.Georgia

Bases: object

export()

parse_data(soup)

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

cowidev.vax.incremental.georgia.main()

cowidev.vax.incremental.greenland

class cowidev.vax.incremental.greenland.Greenland

Bases: object

_parse_data(soup) → dict

_parse_data_date(soup) → dict

_parse_data_metrics(soup) → dict

export()

location: str = 'Greenland'

pipe_location(ds: Series) → Series

pipe_metrics(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

regex = {'date': '.*Nutarterneqarpoq: (\\d+. [a-zA-Z]+202\\d)'}

source_url: str = 'https://corona.nun.gl'

cowidev.vax.incremental.greenland.main()

cowidev.vax.incremental.guatemala

cowidev.vax.incremental.guatemala.main()

cowidev.vax.incremental.guernsey

class cowidev.vax.incremental.guernsey.Guernsey

Bases: object

_regex_date = 'This page was last updated on (\\d{1,2} [A-Za-z]+ 202\\d)'

export()

Generalized.

location = 'Guernsey'

parse_data(soup)

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url = 'https://covid19.gov.gg/guidance/vaccine/stats'

cowidev.vax.incremental.guernsey.main()

cowidev.vax.incremental.hungary

class cowidev.vax.incremental.hungary.Hungary

Bases: CountryVaxBase

export()

Generalized.

get_elements(soup: BeautifulSoup) → list

parse_data(soup: BeautifulSoup, last_update: str) → tuple

parse_data_news_page(soup: BeautifulSoup)

2021-09-10 We received confirmation from the International Communications Office, State Secretariat for International Communications and Relations, that the part of the report referring to people who received the 2nd dose (“közülük ([d ]+) fő már a második oltását is megkapt”) also included those who have received the J&J vaccine. On the other hand, we cannot estimate the number of vaccinations administered, as adding the two reported metrics would count J&J vaccines twice.

parse_link(elem)

pipe_drop_duplicates(df: DataFrame) → DataFrame

pipe_location(df: DataFrame) → DataFrame

pipe_select_output_columns(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: Series) → Series

read(last_update: str) → DataFrame

cowidev.vax.incremental.hungary.main()

cowidev.vax.incremental.iceland

class cowidev.vax.incremental.iceland.Iceland

Bases: CountryVaxBase

_get_json_data(soup)

_parse_data(json_data)

_parse_data_manufacturer(json_data)

_parse_date(json_data)

_parse_metrics(json_data)

export()

location: str = 'Iceland'

metric_entities: dict = {'additional_doses': 'c1286d9e-254c-434a-9455-21b94969d163', 'people_fully_vaccinated': '16a69e30-01fd-4806-920c-436f8f29e9bf', 'people_vaccinated': '8d14f33a-d482-4176-af55-71209314b07b', 'total_boosters': '209af2de-9927-4c51-a704-ddc85e28bab9', 'total_vaccinations': '7287c058-7921-4abc-a667-ce298827c969'}

pipeline_manufacturer(df)

read()

source_url: str = 'https://e.infogram.com/c3bc3569-c86d-48a7-9d4c-377928f102bf'

source_url_ref: str = 'https://www.covid.is/tolulegar-upplysingar-boluefni'

cowidev.vax.incremental.iceland.main()

cowidev.vax.incremental.india

class cowidev.vax.incremental.india.India

Bases: object

property date_str

export()

location: str = 'India'

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read()

read_cowin(json_data) → Series

read_mohfw(json_data) → Series

source_name: str = 'cowin'

property source_url

source_url_ref: str = {'cowin': 'https://dashboard.cowin.gov.in/', 'mohfw': 'https://www.mohfw.gov.in/'}

cowidev.vax.incremental.india.main()

cowidev.vax.incremental.iran

class cowidev.vax.incremental.iran.Iran

Bases: object

_base_url = 'https://irangov.ir/'

_get_link_and_date_from_element(elem: Tag) → tuple

Extract link and date from relevant element.

_get_relevant_element(soup: BeautifulSoup) → Tag

Get the relevant element in news feed.

_get_text_from_url(url: str) → str

Extract text from the url.

_num_max_pages = 3

_parse_data(soup: BeautifulSoup) → tuple

Get data from the source page.

_parse_date_from_element(elem: Tag) → str

Get date from relevant element.

_parse_link_from_element(elem: Tag) → str

Get link from relevant element.

_parse_metrics(text: str) → dict

Get metrics from news text.

_url_subdirectory = 'ministry-of-health-and-medical-education'

export()

location = 'Iran'

pipe_location(data_series: Series) → Series

pipe_vaccine(data_series: Series) → Series

pipeline(data_series: Series) → Series

read() → Series

regex = {'date': '(\\d+\\-\\d+\\-\\d+)', 'people_fully_vaccinated': '(\\d+) people have so far received the second dose', 'people_vaccinated': '(\\d+) Iranians have received the first dose', 'title': "Health Ministry's Updates on COVID-19", 'total_boosters': '(\\d+) people have received the third dose'}

cowidev.vax.incremental.iran.main()

cowidev.vax.incremental.isle_of_man

class cowidev.vax.incremental.isle_of_man.IsleOfMan

Bases: CountryVaxBase

_parse_data(df: DataFrame) → Series

property data_body

export()

property headers

location: str = 'Isle of Man'

metrics_mapping: dict = {'Booster dose': 'total_boosters', 'First dose': 'people_vaccinated', 'Second dose': 'people_fully_vaccinated', 'Third dose (NOT booster)': 'third_dose'}

pipe_date(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_metrics(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://wabi-west-europe-b-primary-api.analysis.windows.net/public/reports/querydata?synchronous=true'

source_url_ref: str = 'https://covid19.gov.im/general-information/covid-19-vaccination-statistics/'

cowidev.vax.incremental.isle_of_man.main()

cowidev.vax.incremental.jamaica

class cowidev.vax.incremental.jamaica.Jamaica

Bases: CountryVaxBase

_parse_data(soup) → Series

export()

location: str = 'Jamaica'

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://vaccination.moh.gov.jm'

source_url_ref: str = 'https://vaccination.moh.gov.jm'

cowidev.vax.incremental.jamaica.main()

cowidev.vax.incremental.kazakhstan

class cowidev.vax.incremental.kazakhstan.Kazakhstan

Bases: CountryVaxBase

_parse_boosters(driver: WebDriver) → tuple

_parse_date(driver: WebDriver) → str

_parse_vaccinations(driver: WebDriver) → tuple

export()

location: str = 'Kazakhstan'

pipe_metadata(ds: Series)

pipe_metrics(ds: Series)

pipe_to_frame(ds: Series)

pipe_vaccine(ds: Series)

pipeline(ds: Series) → Series

read() → Series

source_url = 'https://www.coronavirus2020.kz/'

cowidev.vax.incremental.kazakhstan.main()

cowidev.vax.incremental.kosovo

class cowidev.vax.incremental.kosovo.Kosovo

Bases: CountryVaxBase

_parse_data(soup: BeautifulSoup) → DataFrame

Parse data from the soup

_parse_metrics(soup: BeautifulSoup) → int

Parse metrics from the soup

export()

Exports data to csv

location: str = 'Kosovo'

pipe_date(df: DataFrame) → DataFrame

Pipes date

pipe_vaccine(df: DataFrame) → DataFrame

Pipes vaccine names.

pipeline(df: DataFrame) → DataFrame

Pipeline for data processing

read() → DataFrame

Read data from source

regex: dict = {'Boosters': 'Numri i dozave përforcuese të administruara', 'Dose2': 'Numri i të vaksinuarve me të dy dozat', 'Dose3': 'Numri i dozave të treta të administruara', 'Total': 'Numri total i vaksinave të administruara'}

source_url: str = 'https://msh.rks-gov.net/sq/statistikat-covid-19/'

source_url_ref: str = 'https://msh.rks-gov.net/sq/statistikat-covid-19/'

cowidev.vax.incremental.kosovo.main()

cowidev.vax.incremental.kyrgyzstan

class cowidev.vax.incremental.kyrgyzstan.Kyrgyzstan

Bases: object

_parse_data(soup)

export()

Generalized.

location = 'Kyrgyzstan'

pipe_date(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url = 'https://vc.emed.gov.kg/'

cowidev.vax.incremental.kyrgyzstan.main()

cowidev.vax.incremental.laos

class cowidev.vax.incremental.laos.Laos

Bases: object

_get_relevant_element(soup: BeautifulSoup) → Tag

Gets element from the soup.

_get_text_from_element(elem: Tag) → str

Gets text from element.

_parse_data(soup: BeautifulSoup) → dict

Gets data from the source page.

_parse_date(text: str) → str

Gets date from the text.

_parse_metrics(text: str) → tuple

Gets metrics from the text.

export()

Exports data to csv.

location = 'Laos'

pipe_location(ds: Series) → Series

Pipes location.

pipe_source(ds: Series) → Series

Pipes source url.

pipe_vaccine(ds: Series) → Series

Pipes vaccine names.

pipeline(ds: Series) → Series

Pipeline for the data.

read() → Series

Reads data from source.

regex = {'date': 'ຂໍ້ມູນ ເວລາ .*? (\\d+\\/\\d+\\/\\d+)', 'dose_1': 'ຮັບວັກຊິນເຂັມທີ 1 (\\d+)', 'dose_2': 'ຮັບວັກຊິນເຂັມທີ 2 (\\d+)'}

source_url = 'https://www.covid19.gov.la/index.php'

cowidev.vax.incremental.laos.main()

cowidev.vax.incremental.macao

class cowidev.vax.incremental.macao.Macao

Bases: object

_parse_data(element)

_parse_date(element)

Get data from report file title.

_parse_pdf_table(url)

Extract table

export()

location = 'Macao'

read()

Create data.

source_url = 'https://www.ssm.gov.mo/apps1/covid19vaccine/en.aspx'

cowidev.vax.incremental.macao.main()

cowidev.vax.incremental.moldova

class cowidev.vax.incremental.moldova.Moldova

Bases: CountryVaxBase

_parse_data(soup: BeautifulSoup) → Series

enrich_location(ds: Series) → Series

enrich_source(ds: Series) → Series

enrich_vaccine(ds: Series) → Series

export()

format_date(ds: Series) → Series

location: str = 'Moldova'

pipeline(ds: Series) → Series

read() → Series

source_url = 'https://vaccinare.gov.md'

cowidev.vax.incremental.moldova.main()

cowidev.vax.incremental.monaco

class cowidev.vax.incremental.monaco.Monaco

Bases: CountryVaxBase

_base_url = 'https://www.gouv.mc'

_num_max_pages = 5

export()

Generalized.

get_elements(soup: BeautifulSoup) → list

location: str = 'Monaco'

parse_data(soup: BeautifulSoup, last_update: str) → tuple

parse_data_news_page(soup: BeautifulSoup)

parse_date(elem)

parse_link(elem)

pipe_drop_duplicates(df: DataFrame) → DataFrame

pipe_filter_nans(df: DataFrame) → DataFrame

pipe_location(df: DataFrame) → DataFrame

pipe_select_output_columns(df: DataFrame) → DataFrame

pipe_total_vaccinations(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: Series) → Series

read(last_update: str) → DataFrame

regex = {'date': 'voici les chiffres arrêtés au (\\d+ \\w+) inclus', 'people_fully_vaccinated': 'Nombre de personnes ayant reçu l’injection de rappel\\s:\\s([\\d\\.]+)', 'people_vaccinated': 'Nombre de personnes vaccinées en primo injection\\s:\\s([\\d\\.]+)', 'title': 'Covid-19 : .*'}

source_url = 'https://www.gouv.mc/Action-Gouvernementale/Coronavirus-Covid-19/Actualites/'

cowidev.vax.incremental.monaco.main()

cowidev.vax.incremental.mongolia

cowidev.vax.incremental.mongolia._check_vaccine_names(vaccine_names: list)

cowidev.vax.incremental.mongolia._get_vaccine_names(data: dict, translate: bool = False) → list

cowidev.vax.incremental.mongolia.add_totals(ds: Series) → Series

cowidev.vax.incremental.mongolia.enrich_location(ds: Series) → Series

cowidev.vax.incremental.mongolia.enrich_source(ds: Series) → Series

cowidev.vax.incremental.mongolia.main()

cowidev.vax.incremental.mongolia.parse_data(data: dict) → Series

cowidev.vax.incremental.mongolia.pipeline(ds: Series) → Series

cowidev.vax.incremental.mongolia.read(source: str) → Series

cowidev.vax.incremental.montenegro

cowidev.vax.incremental.montenegro.enrich_location(ds: Series) → Series

cowidev.vax.incremental.montenegro.enrich_source(ds: Series) → Series

cowidev.vax.incremental.montenegro.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.montenegro.main()

cowidev.vax.incremental.montenegro.parse_data(data: dict) → Series

cowidev.vax.incremental.montenegro.pipeline(ds: Series) → Series

cowidev.vax.incremental.montenegro.read(source: str) → Series

cowidev.vax.incremental.morocco

class cowidev.vax.incremental.morocco.Morocco

Bases: object

_parse_data(soup: BeautifulSoup) → Series

export()

location: str = 'Morocco'

pipe_date(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'http://www.covidmaroc.ma/pages/Accueilfr.aspx'

cowidev.vax.incremental.morocco.main()

cowidev.vax.incremental.myanmar

class cowidev.vax.incremental.myanmar.Myanmar

Bases: object

_base_url = 'https://mohs.gov.mm'

_get_link_and_date_from_element(elem: NavigableString) → tuple

Extracts link and date from relevant element.

_get_relevant_element(soup: BeautifulSoup) → NavigableString

Gets the relevant element in news feed.

_get_text_from_url(url: str) → str

Extracts text from the url.

_num_max_pages = 3

_parse_data(soup: BeautifulSoup) → tuple

Get data from the source page.

_parse_date_from_element(elem: NavigableString) → str

Gets date from relevant element.

_parse_link_from_element(elem: NavigableString) → str

Gets link from relevant element.

_parse_metrics(text: str) → dict

Gets metrics from news text.

_url_subdirectory = '/main/content/new/list?pagesize=9&pagenumber='

export()

Exports data to csv.

location = 'Myanmar'

pipe_location(data_series: Series) → Series

Pipes location.

pipe_vaccine(data_series: Series) → Series

Pipes vaccine names.

pipeline(data_series: Series) → Series

Pipeline for data.

read() → Series

Reads data from source.

regex = {'date': '(\\d{1,2}\\-\\d{1,2}\\-20\\d{2})', 'people_fully_vaccinated': '(\\d+) \\(Cumulative fully vaccinated people\\)', 'people_vaccinated': '(\\d+) \\(Cumulative vaccinated people\\)', 'title': 'ကိုဗစ်-19 ရောဂါ ကာကွယ်ဆေး ထိုးနှံပြီးစီးမှု', 'total_vaccinations': '(\\d+) \\(Cumulative vaccinated doses\\)'}

cowidev.vax.incremental.myanmar.main()

cowidev.vax.incremental.nepal

class cowidev.vax.incremental.nepal.Nepal

Bases: CountryVaxBase

_parse_data(links: dict) → tuple

Parses data from link.

_parse_date(link: str) → str

Get date from link.

_parse_metrics(ds: Series) → tuple

Parses metrics from data.

_parse_pdf_table() → Series

Extract table from pdf url

export()

Exports data to CSV.

extract_clean_count_series(df: DataFrame, regex: str) → list

Extracts clean count from series using regex.

location: str = 'Nepal'

pipe_date(df: DataFrame) → DataFrame

Pipes date for main data.

pipe_location(df: DataFrame) → DataFrame

Pipes location for main data.

pipe_manufacturer_date(df: DataFrame) → DataFrame

Pipes date for manufacturer data.

pipe_manufacturer_location(df: DataFrame) → DataFrame

Pipes location for manufacturer data.

pipe_manufacturer_vaccine(df: DataFrame) → DataFrame

Pipes vaccine names for manufacturer data.

pipe_source(df: DataFrame) → DataFrame

Pipes source for main data.

pipe_vaccine(df: DataFrame) → DataFrame

Pipes vaccine names for main data.

pipeline(df: DataFrame) → DataFrame

Pipeline for main data.

pipeline_manufacturer(df: DataFrame) → DataFrame

Pipeline for manufacturer data.

read() → tuple

Reads data from source.

regex: dict = {'count': '\\d+', 'date': '(\\d{1,2}\\-\\d{1,2}\\-20\\d{2})'}

source_url: dict = {'api': 'https://covid19.mohp.gov.np/covid/api/ministryrelease', 'base': 'https://covid19.mohp.gov.np/covid/englishSituationReport/'}

source_url_ref: dict = {'main': 'https://covid19.mohp.gov.np/situation-report'}

cowidev.vax.incremental.nepal.main()

cowidev.vax.incremental.north_macedonia

cowidev.vax.incremental.north_macedonia.connect_parse_data(source: str) → Series

cowidev.vax.incremental.north_macedonia.enrich_date(ds: Series) → Series

cowidev.vax.incremental.north_macedonia.enrich_location(ds: Series) → Series

cowidev.vax.incremental.north_macedonia.enrich_source(ds: Series, source: str) → Series

cowidev.vax.incremental.north_macedonia.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.north_macedonia.main()

cowidev.vax.incremental.north_macedonia.pipeline(ds: Series, source: str) → Series

cowidev.vax.incremental.north_macedonia.read(source: str) → Series

cowidev.vax.incremental.northern_cyprus

cowidev.vax.incremental.northern_cyprus.enrich_location(ds: Series) → Series

cowidev.vax.incremental.northern_cyprus.enrich_source(ds: Series, source: str) → Series

cowidev.vax.incremental.northern_cyprus.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.northern_cyprus.main()

cowidev.vax.incremental.northern_cyprus.parse_data(soup: BeautifulSoup) → Series

cowidev.vax.incremental.northern_cyprus.pipeline(ds: Series, source: str) → Series

cowidev.vax.incremental.northern_cyprus.read(source: str) → Series

cowidev.vax.incremental.paho

class cowidev.vax.incremental.paho.PAHO

Bases: object

_download_csv(driver, option: str, filename: str)

_download_path = '/tmp'

_get_downloaded_filename()

_parse_data(url: str)

_parse_date(driver)

_parse_iframe_link()

columns_mapping = {'1st additional dose': 'total_boosters_1', '2nd additional dose': 'total_boosters_2', 'Country code': 'country_code', 'Country/ Territory': 'location', 'First dose': 'dose_1', 'Second dose': 'dose_2', 'Single dose': 'single_dose', 'Total doses': 'total_vaccinations', 'date': 'date'}

export()

increment_countries(df: DataFrame)

pipe_check_columns(df: DataFrame) → DataFrame

pipe_check_countries(df: DataFrame) → DataFrame

pipe_filter_countries(df: DataFrame) → DataFrame

Get rows from selected countries.

pipe_metadata(df: DataFrame) → DataFrame

pipe_metrics(df: DataFrame) → DataFrame

pipe_rename_columns(df: DataFrame) → DataFrame

pipe_vaccine(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read()

source_url = 'https://ais.paho.org/imm/IM_DosisAdmin-Vacunacion.asp'

cowidev.vax.incremental.paho.main()

cowidev.vax.incremental.pakistan

class cowidev.vax.incremental.pakistan.Pakistan

Bases: CountryVaxBase

_parse_data(soup)

export()

location: str = 'Pakistan'

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read()

source_url = 'https://covid.gov.pk/vaccine-details'

source_url_ref = 'https://covid.gov.pk/vaccine-details'

cowidev.vax.incremental.pakistan.main()

cowidev.vax.incremental.philippines

class cowidev.vax.incremental.philippines.Philippines

Bases: CountryVaxBase

_get_json_data(soup: BeautifulSoup) → dict

Gets JSON from Soup

_parse_data(json_data: dict) → dict

Parses data from JSON

_parse_date(json_data: dict) → str

Parses date from JSON

_parse_metrics(json_data: dict) → dict

Parses metrics from JSON

_print_entitiy_ids()

date_entity: str = '01ff1d02-e027-4eee-9de1-5e19f7fdd5e8'

export()

Exports data to CSV

location: str = 'Philippines'

metric_entities: dict = {'people_fully_vaccinated': 'a4c3cd88-85f7-44ea-b48f-1c97618f1e48', 'people_vaccinated': '32ae0a31-293e-48ea-91cf-e4518496d6bdc9fe1875-6600-4e45-ae6d-a48d9b8a1eae', 'total_boosters': '2c3bf26f-5d71-4793-b6de-4f6b0f1735626ba8b43e-d7c0-4f38-91ff-61d7d8770432', 'total_vaccinations': '4b9e949e-2990-4349-aa85-5aff8501068a'}

pipe_boosters(ds: Series) → Series

Pipes source url

pipe_location(ds: Series) → Series

Pipes location

pipe_source(ds: Series) → Series

Pipes source url

pipe_vaccine(ds: Series) → Series

Pipes vaccine names

pipeline(ds: Series) → Series

Pipeline for data

read() → Series

Reada data from source

source_url: str = 'https://e.infogram.com/_/yFVE69R1WlSdqY3aCsBF'

source_url_ref: str = 'https://news.abs-cbn.com/spotlight/multimedia/infographic/03/23/21/philippines-covid-19-vaccine-tracker'

cowidev.vax.incremental.philippines.main()

cowidev.vax.incremental.poland

class cowidev.vax.incremental.poland.Poland

Bases: object

columns_rename: dict = {'DAWKA_1_SUMA': 'people_vaccinated', 'Data': 'date', 'SZCZEPIENIA_SUMA': 'total_vaccinations', 'dawka_3_suma': 'dose_3', 'dawka_przypominajaca': 'total_boosters', 'zaszczepieni_finalnie': 'people_fully_vaccinated'}

export()

Generalized.

location: str = 'Poland'

pipe_boosters(ds: Series) → Series

pipe_date(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_rename_columns(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

source_url: str = 'https://services-eu1.arcgis.com/zk7YlClTgerl62BY/ArcGIS/rest/services/widok_global_szczepienia_actual/FeatureServer/0/query'

source_url_ref: str = 'https://www.gov.pl/web/szczepimysie/raport-szczepien-przeciwko-covid-19'

cowidev.vax.incremental.poland.main()

cowidev.vax.incremental.qatar

class cowidev.vax.incremental.qatar.Qatar

Bases: CountryVaxBase

connect_parse_data() → Series

enrich_location(ds: Series) → Series

enrich_source(ds: Series) → Series

enrich_vaccine(ds: Series) → Series

export()

location: str = 'Qatar'

pipeline(ds: Series) → Series

read() → Series

source_url = 'https://covid19.moph.gov.qa/EN/Pages/Vaccination-Program-Data.aspx'

cowidev.vax.incremental.qatar.main()

cowidev.vax.incremental.russia

class cowidev.vax.incremental.russia.Russia

Bases: object

export()

cowidev.vax.incremental.russia.enrich_location(ds: Series) → Series

cowidev.vax.incremental.russia.enrich_source(ds: Series) → Series

cowidev.vax.incremental.russia.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.russia.main()

cowidev.vax.incremental.russia.pipeline(ds: Series) → Series

cowidev.vax.incremental.russia.read(source: str) → Series

cowidev.vax.incremental.saint_lucia

cowidev.vax.incremental.saint_lucia.connect_parse_data(source: str) → Series

cowidev.vax.incremental.saint_lucia.enrich_location(ds: Series) → Series

cowidev.vax.incremental.saint_lucia.enrich_source(ds: Series) → Series

cowidev.vax.incremental.saint_lucia.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.saint_lucia.main()

cowidev.vax.incremental.saint_lucia.pipeline(ds: Series) → Series

cowidev.vax.incremental.saint_lucia.read(source: str) → Series

cowidev.vax.incremental.serbia

class cowidev.vax.incremental.serbia.Serbia

Bases: object

_parse_date(soup: BeautifulSoup) → str

_parse_metrics(soup: BeautifulSoup)

export()

pipe_location(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

cowidev.vax.incremental.serbia.main()

cowidev.vax.incremental.south_africa

class cowidev.vax.incremental.south_africa.SouthAfrica

Bases: CountryVaxBase

_build_df(metrics: list, date: str) → tuple

Builds DataFrame from metrics.

_parse_data() → tuple

Parses the data from the source.

_parse_date(date: str) → str

Parses the date from the list.

_payload(payload_var: str = 'Pfizer_first') → dict

Request payload for the source.

Parameters:

payload_var (str): A key in payload_vars (e.g. “Pfizer_first”)

Returns:

dict: A payload for the request

export()

Exports data to CSV.

property headers

Headers for the request

location: str = 'South Africa'

payload_vars = {'Johnson_booster': {'Entity': 'Boosters Measures', 'Name': 'Boosters Measures.Booster Totals', 'Property': 'Booster Totals', 'Value': "'Johnson & Johnson'"}, 'Johnson_first': {'Entity': 'Vaccinations Administered Measures', 'Name': 'Vaccinations Administered Measures.First Dose Total', 'Property': 'First Dose Total', 'Value': "'Johnson & Johnson'"}, 'Pfizer_booster': {'Entity': 'Boosters Measures', 'Name': 'Boosters Measures.Booster Totals', 'Property': 'Booster Totals', 'Value': "'Pfizer'"}, 'Pfizer_first': {'Entity': 'Vaccinations Administered Measures', 'Name': 'Vaccinations Administered Measures.First Dose Total', 'Property': 'First Dose Total', 'Value': "'Pfizer'"}, 'Pfizer_second': {'Entity': 'Vaccinations Administered Measures', 'Name': 'Vaccinations Administered Measures.Second Dose Total', 'Property': 'Second Dose Total', 'Value': "'Pfizer'"}}

pipe_manufacturer_location(df: DataFrame) → DataFrame

Pipes location for manufacturer data.

pipe_manufacturer_vaccine(df: DataFrame) → DataFrame

Pipes vaccine names for manufacturer data.

pipe_vaccine(df: DataFrame) → DataFrame

Pipes vaccine names for main data.

pipeline(df: DataFrame) → DataFrame

Pipeline for main data.

pipeline_manufacturer(df: DataFrame) → DataFrame

Pipeline for manufacturer data.

read() → tuple

Reads the data from the source

source_url: str = 'https://wabi-west-europe-api.analysis.windows.net/public/reports/querydata?synchronous=true'

source_url_ref: str = 'https://sacoronavirus.co.za/latest-vaccine-statistics/'

cowidev.vax.incremental.south_africa.main()

cowidev.vax.incremental.spain

class cowidev.vax.incremental.spain.Spain

Bases: CountryVaxBase

_check_vaccine_names(df: DataFrame)

_date_field_raw = 'Fecha de la última vacuna registrada (2)'

_get_source_url(dt_str)

_get_vaccine_names(df: DataFrame, translate: bool = False)

_max_days_back = 20

_parse_data(last_update: str)

Goes back _max_days_back days to retrieve data.

Does not exceed last_update date.

_parse_data_day(df: DataFrame, source: str) → Series

Parse data for a single day

export()

location: str = 'Spain'

pipe_date(df: DataFrame) → DataFrame

pipe_location(df: DataFrame) → DataFrame

pipeline(df: DataFrame) → DataFrame

read(last_update: str) → Series

vaccine_mapping = {'AstraZeneca': 'Oxford/AstraZeneca', 'Janssen': 'Johnson&Johnson', 'Moderna': 'Moderna', 'Pfizer': 'Pfizer/BioNTech'}

cowidev.vax.incremental.spain.main()

cowidev.vax.incremental.sri_lanka

class cowidev.vax.incremental.sri_lanka.SriLanka

Bases: CountryVaxBase

_fix_header(df)

_get_elems() → list

_parse_data(date, elem) → dict

export()

parse_metrics_from_pdf(pdf_path)

read() → DataFrame

cowidev.vax.incremental.sri_lanka.main()

cowidev.vax.incremental.suriname

cowidev.vax.incremental.suriname.enrich_location(ds: Series) → Series

cowidev.vax.incremental.suriname.enrich_source(ds: Series) → Series

cowidev.vax.incremental.suriname.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.suriname.main()

cowidev.vax.incremental.suriname.pipeline(ds: Series) → Series

cowidev.vax.incremental.suriname.read(source: str) → Series

cowidev.vax.incremental.taiwan

class cowidev.vax.incremental.taiwan.Taiwan

Bases: object

_parse_date(soup) → str

_parse_pdf_link(soup) → str

_parse_stats(df: DataFrame) → int

_parse_table(url_pdf: str)

_parse_tables_all(url_pdf: str) → int

_parse_vaccines(df: DataFrame) → str

export()

location = 'Taiwan'

parse_data(df: DataFrame, soup)

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

property source_data_url

source_url = 'https://www.cdc.gov.tw'

vaccines_mapping = {'AstraZeneca': 'Oxford/AstraZeneca', 'BioNTech': 'Pfizer/BioNTech', 'Moderna': 'Moderna', 'Moderna 雙價\rBA.1': 'Moderna', 'Novavax': 'Novavax', '高端': 'Medigen'}

cowidev.vax.incremental.taiwan.main()

cowidev.vax.incremental.thailand

class cowidev.vax.incremental.thailand.Thailand

Bases: CountryVaxBase

_get_abailable_worksheets()

_parse_data() → DataFrame

Parse metrics from source

export()

Export data to CSV

location: str = 'Thailand'

pipe_date(df: DataFrame) → DataFrame

pipe_metadata(df: DataFrame) → DataFrame

Add metadata

pipe_metrics(df: DataFrame) → DataFrame

pipe_vaccines(df: DataFrame) → DataFrame

Add vaccine information

pipeline(df: DataFrame) → DataFrame

Pipeline for data

read() → DataFrame

Read data from source

source_url = 'https://public.tableau.com/views/SATCOVIDDashboard/1-dash-tiles-w'

source_url_ref = 'https://ddc.moph.go.th/covid19-dashboard/'

cowidev.vax.incremental.thailand.main()

cowidev.vax.incremental.turkey

cowidev.vax.incremental.turkey.enrich_location(ds: Series) → Series

cowidev.vax.incremental.turkey.enrich_source(ds: Series) → Series

cowidev.vax.incremental.turkey.enrich_vaccine(ds: Series) → Series

cowidev.vax.incremental.turkey.main()

cowidev.vax.incremental.turkey.parse_data(soup: BeautifulSoup) → Series

cowidev.vax.incremental.turkey.parse_date(soup: BeautifulSoup) → str

cowidev.vax.incremental.turkey.parse_metric(soup: BeautifulSoup, metric_name: str) → int

cowidev.vax.incremental.turkey.pipeline(ds: Series) → Series

cowidev.vax.incremental.turkey.read(source: str) → Series

cowidev.vax.incremental.united_arab_emirates

class cowidev.vax.incremental.united_arab_emirates.UnitedArabEmirates

Bases: object

_estimate_population(elem, total_vaccinations) → Series

_parse_data() → Series

_parse_date(driver) → Series

_parse_people_fully_vaccinated(elem, population) → Series

_parse_people_vaccinated(elem, population) → Series

_parse_relative_metric(elem, class_name: str, regex: str)

_parse_total_vaccinations(elem) → Series

export()

pipe_calculate_boosters(ds: Series) → Series

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read() → Series

cowidev.vax.incremental.united_arab_emirates.main()

cowidev.vax.incremental.vietnam

class cowidev.vax.incremental.vietnam.Vietnam

Bases: object

_get_relevant_link(soup: BeautifulSoup) → str

Get the relevant URL from the source page.

_get_text_from_url(url: str) → str

Extract text from URL.

_parse_data(soup: BeautifulSoup) → dict

Get data from the source page.

_parse_date_from_text(text: str) → str

Get date from text.

_parse_metrics(text: str) → tuple

Get metrics from text.

base_url = 'https://covid19.gov.vn'

export()

Export data to CSV.

location = 'Vietnam'

pipe_location(ds: Series) → Series

Pipe location.

pipe_vaccine(ds: Series) → Series

Pipe vaccine name.

pipeline(ds: Series) → Series

Pipeline for data.

read() → Series

Read data from source.

regex = {'date': '(\\d{2}/\\d{2}/\\d{4})', 'metrics': {'adolescent': '\\+ Số liều tiêm cho trẻ từ 12\\-17 tuổi là ([\\d\\.]+) liều: Mũi 1 là ([\\d\\.]+) liều; Mũi 2 là ([\\d\\.]+) liều.', 'adult': 'Số liều tiêm cho người từ 18 tuổi trở lên là ([\\d\\.]+) liều: Mũi 1 là ([\\d\\.]+) liều; Mũi 2 là ([\\d\\.]+) liều; Mũi 3 là ([\\d\\.]+) liều; Mũi bổ sung là ([\\d\\.]+) liều; Mũi nhắc lại lần 1 là ([\\d\\.]+) liều; Mũi nhắc lại lần 2 là ([\\d\\.]+) liều.', 'all': 'Trong ngày (\\d\\d?\\/\\d) có (?:[\\d\\.]+) liều vacc?cine phòng COVID-19 đ?ược tiêm(?: chủng)?. Như vậy, tổng số liều (?:vắc xin|vaccine) đã được tiêm là ([\\d\\.]+) liều, trong đó:', 'children': '\\+ Số liều tiêm cho trẻ từ 5\\-11 tuổi là ([\\d\\.]+) liều: Mũi 1 là ([\\d\\.]+) liều; Mũi 2 là ([\\d\\.]+) liều.'}, 'title': 'Ngày'}

source_url = 'https://covid19.gov.vn/ban-tin-covid-19.htm'

cowidev.vax.incremental.vietnam.main()

cowidev.vax.incremental.who

class cowidev.vax.incremental.who.WHO

Bases: CountryVaxBase

_map_vaccines_func(row) → tuple

Replace vaccine names and create column only_2_doses.

export()

increment_countries(df: DataFrame)

location: str = 'WHO'

pipe_add_boosters(df: DataFrame) → DataFrame

pipe_calculate_metrics(df: DataFrame) → DataFrame

pipe_checks(df: DataFrame) → DataFrame

pipe_filter_entries(df: DataFrame) → DataFrame

Get valid entries:

• Countries not coming from OWID (avoid loop)

• Rows with total_vaccinations >= people_vaccinated >= people_fully_vaccinated

pipe_map_vaccines(df: DataFrame) → DataFrame

Based on the list of known vaccines, identifies whether each country is using only 2-dose vaccines or also some 1-dose vaccines. This determines whether people_fully_vaccinated can be calculated as total_vaccinations - people_vaccinated. Vaccines check

pipe_rename_countries(df: DataFrame) → DataFrame

pipe_vaccine_checks(df: DataFrame) → DataFrame

pipeline(df: DataFrame)

read() → DataFrame

rename_columns = {'COUNTRY': 'location', 'DATE_UPDATED': 'date', 'VACCINES_USED': 'vaccine'}

source_url = 'https://covid19.who.int/who-data/vaccination-data.csv'

source_url_ref = 'https://covid19.who.int/'

cowidev.vax.incremental.who.main()

cowidev.vax.incremental.zambia

class cowidev.vax.incremental.zambia.Zambia

Bases: CountryVaxBase

export()

pipe_location(ds: Series) → Series

pipe_source(ds: Series) → Series

pipe_vaccine(ds: Series) → Series

pipeline(ds: Series) → Series

read()

cowidev.vax.incremental.zambia.main()