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Web Scraping with Python (res)
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Course Content

Web Scraping with Python (res)

Web Scraping with Python (res)

1. HTML Files and DevTools
Intro to Web ScrapingFirst StepsHTML File StructureHierarchy in the HTMLString MethodsAttributesDevTools
2. Beautiful Soup
Work with the LibraryWork with SoupFind/Find_allFind All!
3. CSS Selectors/XPaths
Intro to XPathAttributes in XPathSelectorsCSS SelectorsCSS Selectors in BeautifulSoupXPath vs CSS Selector
4. Tables
Intro to TablesWays to Scrape TableSimple Solution for ScrapingChallenge

bookSimple 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.


              
12

            
import pandas as pd
tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
copy

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:


              
12

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

Task

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.

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Section 4. Chapter 3
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bookSimple 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.


              
12

            
import pandas as pd
tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
copy

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:


              
12

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

Task

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 3
toggle bottom row

bookSimple 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.


              
12

            
import pandas as pd
tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
copy

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:


              
12

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

Task

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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.


              
12

            
import pandas as pd
tables = pd.read_html('https://en.wikipedia.org/wiki/Florida')
copy

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:


              
12

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

Task

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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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