python-challenge

Repository Name: python-challenge

Description: The Python Challenge repository contains solutions to two ('Module 3 Challenge') Python challenges: PyBank and PyPoll. Both challenges simulate real-world scenarios where Python scripting skills are crucial for data analysis and automation tasks.

Project Structure:

• PyBank:

  • main.py: Python script for analyzing financial data.
  • Resources: Folder containing the financial dataset (budget_data.csv).
  • Analysis: Folder containing the analysis results text file (budget_analysis.txt).

• PyPoll:

  • main.py: Python script for analyzing poll data.
  • Resources: Folder containing the poll dataset (election_data.csv).
  • Analysis: Folder containing the analysis results text file (election_analysis.txt).

Instructions: Before running the scripts, follow these steps:

1. Clone Repository: Clone this repository to your local machine using git clone.
2. Navigate to Repository: Open your terminal and navigate to the cloned repository directory.
3. Run Scripts: Run each script separately by executing python main.py within the respective project directories (PyBank and PyPoll).
4. Verify Results: Check the analysis results in the corresponding Analysis folders.

Project Overview:

• PyBank:

  • Analyzes financial data to calculate total months, net total profit/loss, average change, and identifies the greatest increase and decrease in profits.
  • Utilizes Python's csv module for reading CSV files and handles data using variables, lists, and dictionaries.

• PyPoll:

  • Modernizes a small, rural town's vote-counting process by analyzing poll data.
  • Calculates total votes cast, lists of candidates who received votes, percentages of votes each candidate won, and determines the winner of the election based on popular vote.
  • Utilizes Python's file handling capabilities and data structures like lists and dictionaries.

Considerations:

• Use the csv module to handle CSV files effectively.
• Employ variables, lists, and dictionaries to store and manipulate data.
• Break down tasks into smaller objectives for efficient problem-solving.
• Debug code as needed to identify and fix errors.
• Ensure correct file paths for loading datasets and outputting analysis results.

Backup and Version Control:

• Regularly commit your work to maintain version history and back it up by pushing changes to GitHub.

Additional Notes:

• Each script operates independently, analyzing its respective dataset.
• Remember to review the README.md file within each project folder for specific instructions and details.

Code Source: The Python script development process heavily utilized the Xpert Learning Assistant platform, leveraging its vast knowledge base and interactive capabilities. Additionally, some cross-comparisons were made with ' ChatGPT to address specific challenges encountered during script development.

Contact Information: If you have any questions, suggestions, or encounter issues, feel free to reach out to me via email at ngalakevin@gmail.com.

Thank you for exploring my Python Challenge repository!