https://github.com/fbarffmann/python-challenge
Automated financial and election data analysis using Python. Cleaned and transformed large CSV datasets, calculated key business metrics, and generated automated reports for stakeholders.
https://github.com/fbarffmann/python-challenge
automation csv data-analysis data-cleaning election-analysis financial-analysis python reporting
Last synced: about 21 hours ago
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Automated financial and election data analysis using Python. Cleaned and transformed large CSV datasets, calculated key business metrics, and generated automated reports for stakeholders.
- Host: GitHub
- URL: https://github.com/fbarffmann/python-challenge
- Owner: fbarffmann
- Created: 2024-05-13T22:53:03.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-04-13T13:01:24.000Z (11 days ago)
- Last Synced: 2025-04-13T14:19:32.988Z (11 days ago)
- Topics: automation, csv, data-analysis, data-cleaning, election-analysis, financial-analysis, python, reporting
- Language: Python
- Homepage:
- Size: 6.57 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Python Challenge Projects
This repository contains two Python-based data analysis projects focused on financial and election data. Both projects demonstrate the ability to import, clean, analyze, and summarize large datasets using core Python libraries.
## Projects Overview
### 1. PyBank: Financial Analysis
Analyzes budget data over a multi-year period to calculate key financial metrics.
#### Analysis Includes:
- Total months of data
- Net total amount of profit/losses
- Average change in profit/losses between months
- Greatest increase in profits (date & amount)
- Greatest decrease in losses (date & amount)
---
### 2. PyPoll: Election Analysis
Analyzes election data to calculate and report voting results.
#### Analysis Includes:
- Total number of votes cast
- List of candidates who received votes
- Total number of votes each candidate won
- Percentage of votes each candidate won
- Winner of the election based on popular vote
---
## Tools & Technologies Used
- Python 3.x
- CSV file handling
- Python built-in functions & loops
- Text file output for reporting results
---
## File Structure
```text
PyBank/
├── main.py - Analysis code for financial data
├── Resources/
│ └── budget_data.csv - Source dataset
└── analysis/
└── analysis.txt - Output results
PyPoll/
├── main.py - Analysis code for election data
├── Resources/
│ └── election_data.csv - Source dataset
└── analysis/
└── analysis.txt - Output results
```
---
## Skills Demonstrated
- Data Cleaning & Transformation
- CSV File Manipulation in Python
- Analysis Automation
- Report Writing to Text Files
- Problem-Solving with Python
---
## Key Takeaways
- Built Python scripts to automate data analysis workflows.
- Translated messy CSV files into clear business insights.
- Generated automated reports ready for stakeholders.