https://github.com/fbarffmann/pandas-challenge
Analyzed school budget and performance data using Python and Pandas to uncover trends by school type, size, and spending per student.
https://github.com/fbarffmann/pandas-challenge
Last synced: 1 day ago
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Analyzed school budget and performance data using Python and Pandas to uncover trends by school type, size, and spending per student.
- Host: GitHub
- URL: https://github.com/fbarffmann/pandas-challenge
- Owner: fbarffmann
- Created: 2024-05-16T18:49:47.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-04-01T11:52:11.000Z (24 days ago)
- Last Synced: 2025-04-01T12:34:02.291Z (24 days ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 470 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Pandas Challenge: PyCitySchools Analysis
## Overview
This project analyzes standardized testing data for a hypothetical school district to help inform strategic decision-making around budgets and academic priorities. Using Python and the Pandas library, the analysis explores student and school-level performance metrics, including average scores, pass rates, and the impact of variables like school type, size, and budget per student.
## Dataset
The data comes from two CSV files:
- `schools_complete.csv`: Information about each school, including name, type (District or Charter), budget, and size.
- `students_complete.csv`: Contains information for each student, including school attended, grade level, gender, and test scores in math and reading.
## Objectives
- Generate a **District Summary** of key metrics like average test scores and pass rates.
- Create a **School Summary** that breaks down performance by individual schools.
- Rank schools based on overall student performance.
- Analyze **grade-level** performance trends in math and reading.
- Explore the relationship between **spending per student** and test performance.
- Investigate how **school size** and **school type** correlate with academic outcomes.
## Key Findings
- **Charter schools** consistently outperformed district schools in both math and reading, with higher average scores and pass rates.
- Smaller schools (fewer than 1,000 students) performed better on average than larger schools.
- Spending more per student did **not** correlate with higher performance, suggesting more efficient use of funds might be more important than total expenditure.
- **9th graders** generally had the lowest average scores across subjects, while **10th and 11th graders** tended to perform better.
- The top five schools were all charter schools, reinforcing the trend of better performance among that school type.
## Technologies Used
- **Python**
- **Pandas**
- **Jupyter Notebook**
## How to Use
1. Clone the repository:
```
git clone https://github.com/fbarffmann/pandas-challenge.git
```
2. Navigate to the PyCitySchools directory:
```
cd pandas-challenge/PyCitySchools
```
3. Install pandas and jupyter libraries if need be:
```
pip install pandas jupyter
```
4. Launch the Jupyter Notebook:
```
jupyter notebook Analysis.ipynb
```
5. Run all cells to execute the full school budget and performance analysis.
---
👨💻 Developed by Finn Brennan Arffmann
📊 Learn more at [github.com/fbarffmann](https://github.com/fbarffmann)