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Вивчайте Simple Solution for Scraping | Tables
Web Scraping with Python

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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')
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import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
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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')
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import pandas as pd tables = pd.read_html('https://en.wikipedia.org/wiki/Florida', match='State University System of Florida')
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Завдання

Swipe to start coding

Get the table from the Wikipedia page about Florida and convert it to the DataFrame.

  1. Import pandas library with the pd alias.
  2. Get the table 'Largest cities or towns in Florida' from the page.
  3. Print the DataFrame df.

Рішення

# Import libraries
import pandas as pd

# Convert table to the DataFrame and print it
df = pd.read_html('https://en.wikipedia.org/wiki/Florida', match = 'Largest cities or towns in Florida')
print(df)

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# Import libraries
import ___ as ___

# Convert table to the DataFrame and print it
df = pd.read_html('https://en.wikipedia.org/wiki/Florida', ___)
___

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