Use GithibRepo2ProjectDataset to scrap github org statistics

To extract and process data from Github organizations using the GithibRepo2ProjectDataset, we need to modify the code in the main.py file to call the functions in github_api.py and data_processing.py. We can start by importing the necessary functions from the other two files and then calling them in the main function. In github_api.py, we need to create a new function to extract data from Github organizations using the Github API. This function should take the organization name as input and return the relevant data. Once we have extracted the data, we can return it in a format that can be processed by the data_processing.py file. In data_processing.py, we need to write the code to process the data extracted from Github organizations. The specific requirements for data processing are not mentioned in the original user input, so we need to determine the processing steps based on the needs of the project. We can start by importing the necessary libraries and defining functions for each processing step. Once we have a clear understanding of the data processing requirements, we can write the code to implement those steps.

Here's an example of how we can modify the main.py file to call the necessary functions:

from github_api import extract_org_data
from data_processing import clean_data, calculate_metrics, visualize_data

def main():
    org_name = "example_org"
    org_data = extract_org_data(org_name)
    cleaned_data = clean_data(org_data)
    metrics = calculate_metrics(cleaned_data)
    visualize_data(metrics)

if __name__ == "__main__":
    main()

In this example, we first import the extract_org_data function from github_api.py and the clean_data, calculate_metrics, and visualize_data functions from data_processing.py. We then define the main function, which takes the organization name as input, extracts the relevant data using the extract_org_data function, cleans the data using the clean_data function, calculates metrics using the calculate_metrics function, and visualizes the data using the visualize_data function. Finally, we call the main function if the script is being run as the main program.

Note that the specific functions and processing steps will depend on the needs of the project, so you may need to modify the code accordingly.

Changes on src/main.py

As per the task, we need to modify the code in this file to call the functions in github_api.py and data_processing.py to extract and process data from Github organizations. We can start by importing the necessary functions from the other two files and then calling them in the main function. The exact modifications will depend on the specific functions and data we want to extract and process.

Changes on src/github_api.py

As this is a new file, there is no need to modify any code in it. However, we need to create a new function in this file to extract data from Github organizations. This function should take the organization name as input and return the relevant data. We can use the Github API to extract data from organizations. We can refer to the Github API documentation to understand how to extract data from organizations. Once we have extracted the data, we can return it in a format that can be processed by the data_processing.py file.

Changes on src/data_processing.py

As this is a new file, we need to write the code to process the data extracted from Github organizations. The specific requirements for data processing are not mentioned in the original user input, so we need to determine the processing steps based on the needs of the project. Some possible data processing steps could include cleaning and formatting the data, calculating statistics and metrics, and visualizing the data. We can start by importing the necessary libraries and defining functions for each processing step. Once we have a clear understanding of the data processing requirements, we can write the code to implement those steps.

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Use GithibRepo2ProjectDataset to scrap github org statistics #1