Build a Google News Aggregator in Power BI with Python

Build a Google News Aggregator in Power BI with Python

Table of Contents

1. Introduction
2. Building a Google News Aggregator with Python
   1. Gathering News Data with Google News Scraper
   2. Using the News Crawler
   3. Extracting Relevant Information with Loops
3. Installing the Required Libraries
4. Importing Data Manipulation Libraries
5. Getting the Google News Scraper
6. Specifying the Search Crawler
7. Extracting Titles with a Loop
8. Extracting Titles and Links with Nested Loops
9. Adding Keywords to the Search
10. Saving the Data as a CSV
11. Bringing Data into Power BI
12. Automating the Notebook (Optional)
13. Conclusion
14. Resources

Building a Google News Aggregator with Python

In this Tutorial, we will learn how to create a Google News aggregator using Python. We will use Python scripts to scrape news data from Google News and then display it in a dashboard created in Power BI. The end result will include stories from various sources such as Amazon, Apple, and Microsoft, along with a line graph showing the distribution of stories by day. By the end of this tutorial, you will have a fully functional Google News aggregator that can be customized to track specific items or companies.

Introduction

A Google News aggregator is a powerful tool that allows you to Gather news articles from various sources and display them in one place. It provides a convenient way to stay updated on the latest news related to specific topics or companies. In this tutorial, we will use Python to build our own Google News aggregator, giving us full control over the data sources and customization options.

1. Installing the Required Libraries

Before we start building our Google News aggregator, we need to install the necessary Python libraries. In this tutorial, we will be using the "Pi-Google-News" library, which provides a scraper for Google News. To install the library, open your Anaconda Prompt or command prompt and run the following command: !pip install pi-Google-News. Once the library is installed, we can proceed to the next step.

2. Importing Data Manipulation Libraries

In order to manipulate the data scraped from Google News, we need to import the necessary data manipulation libraries. In this tutorial, we will be using the Pandas library, which is a popular library for data manipulation in Python. To import Pandas, add the following line of code to your Python script: import pandas as pd. This will allow us to use Pandas functions to process and analyze the data.

3. Getting the Google News Scraper

To access the Google News scraper provided by the "pi-Google-News" library, we need to import the Google News module. To do this, add the following line of code to your script: from pi-Google-News import GoogleNews. The Google News module provides the functions we need to scrape news data from Google News.

4. Specifying the Search Crawler

Once we have imported the Google News module, we can create an instance of the Google News scraper by using the following line of code: gn = GoogleNews(). This will initialize the search crawler and allow us to specify the search parameters.

5. Extracting Titles with a Loop

To extract the titles of the news articles, we can use a loop to iterate through the search results. In Python, we can use a for loop to iterate through each individual element in a list. In this case, we want to iterate through the search entries and extract the title for each entry. To do this, add the following code to your script:

for entry in gn.search('chat GPT', num_results=10):
    print(entry['title'])

This code will print out the titles of the news articles containing the keyword "chat GPT". You can adjust the num_results parameter to specify the number of search results you want to retrieve.

6. Extracting Titles and Links with Nested Loops

In addition to extracting the titles, we can also extract the links of the news articles using nested loops. We can create an empty list called "stories" to store the title and link information for each search entry. Then, we can iterate through the search entries and create a dictionary for each entry containing the title, link, and other relevant information. Finally, we can append each dictionary to the "stories" list. To do this, add the following code to your script:

stories = []

for entry in gn.search('chat GPT', num_results=10):
    title = entry['title']
    link = entry['link']
    keyword = 'chat GPT'
    date_published = entry['published_date']

    story = {'title': title, 'link': link, 'keyword': keyword, 'date_published': date_published}
    stories.append(story)

df = pd.DataFrame(stories)
print(df)

This code will print out a Pandas dataframe containing the titles, links, keywords, and publication dates of the news articles. You can customize the search keyword and the number of search results to fit your requirements.

7. Adding Keywords to the Search

To make the Google News aggregator more comprehensive, we can add multiple keywords to the search. This can be useful when tracking news related to specific items or companies. We can create a list of keywords and use a nested loop to iterate through each keyword and search for news articles containing that keyword. To do this, add the following code to your script:

keywords = ['Microsoft', 'Apple', 'Amazon']
stories = []

for keyword in keywords:
    for entry in gn.search(keyword, num_results=10):
        title = entry['title']
        link = entry['link']
        date_published = entry['published_date']

        story = {'title': title, 'link': link, 'keyword': keyword, 'date_published': date_published}
        stories.append(story)

df = pd.DataFrame(stories)
print(df)

This code will print out a Pandas dataframe containing the titles, links, keywords, and publication dates of the news articles related to Microsoft, Apple, and Amazon. You can customize the list of keywords and the number of search results to fit your requirements.

8. Saving the Data as a CSV

Once we have gathered the news data, we can save it as a CSV file for further analysis or visualization. To save the data as a CSV file, we can use the to_csv function provided by the Pandas library. Add the following code to your script:

df.to_csv('news_data.csv', index=False)

This code will save the data stored in the Pandas dataframe as a CSV file named "news_data.csv". You can change the file name to fit your requirements. The index=False parameter ensures that the index column is not saved in the CSV file.

9. Bringing Data into Power BI

Now that we have the news data saved as a CSV file, we can bring it into Power BI to create a dashboard for visualizing the data. To do this, open Power BI and click on "Get Data". Select "Text/CSV" from the list of data sources and browse for the CSV file that we saved in the previous step. Once the data is loaded into Power BI, you can create visualizations and analyze the news data in a dynamic and interactive dashboard.

10. Automating the Notebook (Optional)

If you want to automate the process of running the Python notebook and updating the dashboard, you can set up a task scheduler or use other automation tools available in Python. This will allow you to regularly update the news data and keep your dashboard up to date without manual intervention.

11. Conclusion

In this tutorial, we have learned how to build a Google News aggregator using Python. We have seen how to scrape news data from Google News, extract relevant information using loops, and save the data for further analysis. By bringing the data into Power BI, we can create interactive dashboards for visualizing and analyzing the news data. This tutorial provides a starting point for building your own customized news aggregator and staying updated on the latest news in your area of interest.

12. Resources

• "pi-Google-News" library: https://pypi.org/project/pi-Google-News/
• Python Pandas library: https://pandas.pydata.org/
• Power BI: https://powerbi.microsoft.com/

FAQ

Q: Can I use a different news scraper instead of pi-Google-News?

A: Yes, there are many different news scrapers available for Python. pi-Google-News was used in this tutorial as an example, but you can explore other options based on your requirements.

Q: Can I scrape news data in languages other than English?

A: Yes, the pi-Google-News library allows you to specify the location and language for the news data. You can customize the search parameters to retrieve news articles in any language.

Q: How often should I update the news data in my dashboard?

A: The frequency of updating the news data depends on your specific needs. You can set up a schedule to update the data daily, weekly, or at any desired interval. The choice depends on how frequently you want the dashboard to reflect the latest news.

Q: Can I add more customizations to the dashboard in Power BI?

A: Yes, Power BI provides a wide range of options for customizing and enhancing your dashboard. You can add filters, slicers, calculated columns, and various visualizations to analyze the news data in different ways.

Q: Are there any other Python libraries that can be useful for building a news aggregator?

A: Yes, there are other Python libraries such as BeautifulSoup and Scrapy that can be used for Web Scraping. These libraries provide more flexibility and advanced features for scraping web data. You can explore them based on your specific requirements.

Q: Can I automate the process of running the Python notebook?

A: Yes, you can use tools like task schedulers or cron jobs to automate the process of running the Python notebook. This will allow you to regularly update the news data without manual intervention. You can schedule the notebook to run at specific intervals and update the dashboard accordingly.