Title
Python to web scrape the Economy of the United States data from Wikipedia
Introduction
Web scraping is the process of extracting data from websites using automated tools or scripts. Python provides a number of libraries that can be used to scrape data from websites, including beautifulsoup4, requests, and pandas.
In the dataset provided, the pandas library is used to read a table from the Wikipedia page on the economy of the United States. This is a simple and efficient way to extract data from a webpage, as pandas provides a convenient function read_html() that can parse HTML tables into DataFrames.
The code I provided is relatively short and easy to understand, with only a few lines of code needed to extract the data. This is because pandas does much of the heavy lifting behind the scenes, automatically detecting and parsing HTML tables into DataFrames.
Overall, using pandas to scrape data from websites is a powerful and efficient technique that can save time and effort when working with large amounts of data. By providing a simple and convenient interface for reading HTML tables, pandas allows you to focus on analyzing the data rather than writing complex scraping scripts.
About Dataset
The economy of the United States is the largest and most technologically advanced economy in the world. It is a mixed economy, which means that it combines elements of capitalism and socialism, with a strong emphasis on private enterprise and individual initiative.
The United States has a highly diversified economy, with major industries including manufacturing, finance, healthcare, education, technology, and transportation. The country is also a major producer of oil, natural gas, and agricultural products.
The US economy is driven by consumer spending, which accounts for approximately two-thirds of the country's GDP. The government plays a significant role in the economy, providing public goods and services, regulating industries, and redistributing income through taxation and social welfare programs.
Over the years, the US economy has experienced periods of growth and recession, and has faced various challenges such as inflation, unemployment, and income inequality. However, it remains one of the most powerful and innovative economies in the world, with a high standard of living for its citizens.
The table shows the main economic indicators in 1980–2021 (with IMF staff estimates in 2022–2027).
We need to extract the full data table.
Processing of Data
This code imports the pandas library, assigns the Wikipedia page URL to page_url, and uses read_html() to read all tables on the page into a list of DataFrames called df_economy. We then select the 4th DataFrame from the list using df_economy[3] and assign it to df. Finally, we print df to display the contents of the DataFrame.
Output
The code provided is a Python script that uses the pandas library to read a table from the Wikipedia page on the economy of the United States. The script first imports pandas, then specifies the URL of the Wikipedia page to read. It then uses the read_html() function to read all the tables on the page into a list of DataFrames. Finally, it selects the 4th DataFrame from the list (assuming that's the one containing the desired table) and assigns it to a variable called df_economy.
In order to execute this code, you would need to have the pandas library installed in your Python environment. You can install pandas using pip, which is a package manager for Python. Once pandas is installed, you can run the code in a Python environment such as Jupyter Notebook or a Python console. The output will be the contents of the selected table from the Wikipedia page on the economy of the United States.
Overall, this code is a simple example of how pandas can be used to scrape data from a website and extract specific information from it.
We got the html table into our notebook. We can use this table by saving data frame into spread sheets, data analysing and do many many more things.Source
Github Link:Quick Webscraping
Website Link:Economy of the United States
