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Store Scraped Data Into a Pandas DataFrame | Automating Data Collection from Web Sources
Automating Data Collection from Web Sources
course content

Contenido del Curso

Automating Data Collection from Web Sources

bookStore Scraped Data Into a Pandas DataFrame

Storing scraped data in a pandas DataFrame is a convenient way to manipulate and work with the data. pandas is a powerful library in Python that provides easy-to-use data structures and data analysis tools.

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it as a spreadsheet, SQL table, or a dictionary of Series objects. It is generally the most commonly used pandas object.

Tarea

  1. Import pandas and initialize an empty DF;
  2. Scrape the country name (find all instances on the web page);
  3. Scrape the capital city (find all instances on the web page);
  4. Append the scraped values (country_name, item) in the df.

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Storing scraped data in a pandas DataFrame is a convenient way to manipulate and work with the data. pandas is a powerful library in Python that provides easy-to-use data structures and data analysis tools.

A DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. You can think of it as a spreadsheet, SQL table, or a dictionary of Series objects. It is generally the most commonly used pandas object.

Tarea

  1. Import pandas and initialize an empty DF;
  2. Scrape the country name (find all instances on the web page);
  3. Scrape the capital city (find all instances on the web page);
  4. Append the scraped values (country_name, item) in the df.

Mark tasks as Completed
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 5
AVAILABLE TO ULTIMATE ONLY
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