Read Table

Extract tables from an HTML page.

Example

This is our table:

Source: https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)

frompprintimportpprintimportrequestsfromread_tableimportread_table, to_dicturl="https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)"tables=read_table(requests.get(url).text, attrs={"class": "wikitable"})
table=tables[0]
# As a list of listspprint(table)
"""[ [ "Country / Area", "UN continental region [4]", "UN statistical subregion [4]", "Population (1 July 2018)", "Population (1 July 2019)", "Change", ], ["China [a]", "Asia", "Eastern Asia", "1,427,647,786", "1,433,783,686", "+0.43%"], ["India", "Asia", "Southern Asia", "1,352,642,280", "1,366,417,754", "+1.02%"], [ "United States", "Americas", "Northern America", "327,096,265", "329,064,917", "+0.60%", ], ...]"""# As a list of dictspprint(to_dict(table))
"""[ { "Change": "+0.43%", "Country / Area": "China [a]", "Population (1 July 2018)": "1,427,647,786", "Population (1 July 2019)": "1,433,783,686", "UN continental region [4]": "Asia", "UN statistical subregion [4]": "Eastern Asia", }, { "Change": "+1.02%", "Country / Area": "India", "Population (1 July 2018)": "1,352,642,280", "Population (1 July 2019)": "1,366,417,754", "UN continental region [4]": "Asia", "UN statistical subregion [4]": "Southern Asia", }, { "Change": "+0.60%", "Country / Area": "United States", "Population (1 July 2018)": "327,096,265", "Population (1 July 2019)": "329,064,917", "UN continental region [4]": "Americas", "UN statistical subregion [4]": "Northern America", }, ...]"""