Зміст курсу
Web Scraping with Python (res)
Web Scraping with Python (res)
Simple Solution for Scraping
The library pandas provide a quick and convenient solution for converting HTML tables to the DataFrame. The function read_html() can be useful for scraping tables from various websites without figuring out how to get the website’s HTML. You can use read_html() to work with tables whose structure is not complicated, for example, tables on Wikipedia pages.
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
In the code above, the function read_html() got all tables from Wiki about Florida. table is a list of all the tables on the page already converted to DataFrames.
With a large number of tables on the page, it can be challenging to find the one you need. To make the table selection easier, use the match parameter to select the table you want. For example:
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida', match='State University System of Florida')
Завдання
Get the table from the Wikipedia page about Florida and convert it to the DataFrame.
- Import
pandaslibrary with thepdalias. - Get the table 'Largest cities or towns in Florida' from the page.
- Print the DataFrame df.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Simple Solution for Scraping
The library pandas provide a quick and convenient solution for converting HTML tables to the DataFrame. The function read_html() can be useful for scraping tables from various websites without figuring out how to get the website’s HTML. You can use read_html() to work with tables whose structure is not complicated, for example, tables on Wikipedia pages.
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
In the code above, the function read_html() got all tables from Wiki about Florida. table is a list of all the tables on the page already converted to DataFrames.
With a large number of tables on the page, it can be challenging to find the one you need. To make the table selection easier, use the match parameter to select the table you want. For example:
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida', match='State University System of Florida')
Завдання
Get the table from the Wikipedia page about Florida and convert it to the DataFrame.
- Import
pandaslibrary with thepdalias. - Get the table 'Largest cities or towns in Florida' from the page.
- Print the DataFrame df.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Simple Solution for Scraping
The library pandas provide a quick and convenient solution for converting HTML tables to the DataFrame. The function read_html() can be useful for scraping tables from various websites without figuring out how to get the website’s HTML. You can use read_html() to work with tables whose structure is not complicated, for example, tables on Wikipedia pages.
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
In the code above, the function read_html() got all tables from Wiki about Florida. table is a list of all the tables on the page already converted to DataFrames.
With a large number of tables on the page, it can be challenging to find the one you need. To make the table selection easier, use the match parameter to select the table you want. For example:
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida', match='State University System of Florida')
Завдання
Get the table from the Wikipedia page about Florida and convert it to the DataFrame.
- Import
pandaslibrary with thepdalias. - Get the table 'Largest cities or towns in Florida' from the page.
- Print the DataFrame df.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
The library pandas provide a quick and convenient solution for converting HTML tables to the DataFrame. The function read_html() can be useful for scraping tables from various websites without figuring out how to get the website’s HTML. You can use read_html() to work with tables whose structure is not complicated, for example, tables on Wikipedia pages.
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
In the code above, the function read_html() got all tables from Wiki about Florida. table is a list of all the tables on the page already converted to DataFrames.
With a large number of tables on the page, it can be challenging to find the one you need. To make the table selection easier, use the match parameter to select the table you want. For example:
import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida', match='State University System of Florida')
Завдання
Get the table from the Wikipedia page about Florida and convert it to the DataFrame.
- Import
pandaslibrary with thepdalias. - Get the table 'Largest cities or towns in Florida' from the page.
- Print the DataFrame df.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів