{"id":22353977,"url":"https://github.com/ckyle30/pa4","last_synced_at":"2025-03-26T12:28:29.146Z","repository":{"id":275145533,"uuid":"858492115","full_name":"Ckyle30/PA4","owner":"Ckyle30","description":"Programming Assignment #4 - De Guzman, Ckyle Ewxel Olrick B. - 2ECE-A","archived":false,"fork":false,"pushed_at":"2024-09-17T11:40:58.000Z","size":152,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-31T13:43:41.123Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ckyle30.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-17T01:50:09.000Z","updated_at":"2024-09-17T11:41:02.000Z","dependencies_parsed_at":"2025-01-31T13:43:45.672Z","dependency_job_id":"69664e5c-0857-4caa-9cfd-a78f13c0f6cb","html_url":"https://github.com/Ckyle30/PA4","commit_stats":null,"previous_names":["ckyle30/pa4"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ckyle30%2FPA4","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ckyle30%2FPA4/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ckyle30%2FPA4/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ckyle30%2FPA4/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ckyle30","download_url":"https://codeload.github.com/Ckyle30/PA4/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245652977,"owners_count":20650607,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-04T13:10:40.182Z","updated_at":"2025-03-26T12:28:29.138Z","avatar_url":"https://github.com/Ckyle30.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Wrangling and Visualization in Python\n\n## Overview\n The goal of the project is to perform **data wrangling** and **data visualization** using the provided **ECE Board Exam dataset**. The analysis is carried out using Python and the following libraries: `pandas`, `matplotlib`, and `seaborn`.\n\n---\n\n## Table of Contents\n\n- [Overview](#overview)\n- [Dataset](#dataset)\n- [Intended Learning Outcomes](#intended-learning-outcomes)\n- [Installation](#installation)\n- [Instructions](#instructions)\n  - [Task 1: Data Wrangling](#task-1-data-wrangling)\n  - [Task 2: Data Visualization](#task-2-data-visualization)\n- [Results](#results)\n  - [Instru DataFrame](#instru-dataframe)\n  - [Mindy DataFrame](#mindy-dataframe)\n- [Visualizations](#visualizations)\n- [References](#references)\n\n---\n\n## Dataset\n\nThe dataset used for this project contains student records from **ECE Board Exams**. You can download the dataset from the provided link: [ECE Board Exam Dataset](bit.ly/ECEBoardExamDataset).\n\nThe dataset includes the following features:\n- `Name`: The name of the student\n- `Gender`: Gender of the student\n- `Track`: The academic track chosen by the student (e.g., Instrumentation, Communication)\n- `Math`, `GEAS`, `Electronics`: Scores in respective subjects\n- `Hometown`: Region of origin (e.g., Luzon, Visayas, Mindanao)\n\n---\n\n## Intended Learning Outcomes\n\n1. Understand how to use Python libraries for data wrangling (i.e., `pandas`) and data visualization (i.e., `matplotlib`, `seaborn`).\n2. Learn to clean, manipulate, and transform datasets into useful information.\n3. Create visual representations that tell a story about the dataset.\n\n---\n\n## Instructions\n\n### Task 1: Data Wrangling\n\nIn this task, you need to create specific subsets of the dataset based on the following conditions:\n\n1. **Instru DataFrame**: Extract rows where:\n   - The `Track` is **Instrumentation**.\n   - The `Hometown` is **Luzon**.\n   - The `Electronics` score is greater than 70.\n\n   **Example Output:**\n   | Name  | GEAS | Electronics |\n   |-------|------|-------------|\n   | S6    | 90   | 82          |\n   | S10   | 70   | 79          |\n\n2. **Mindy DataFrame**: Extract rows where:\n   - The `Gender` is **Female**.\n   - The `Hometown` is **Mindanao**.\n   - The `Average` score is 55 or higher.\n\n   To calculate the `Average`, use the following formula:\n   ```python\n   data['Average'] = data[['Math', 'GEAS', 'Electronics']].mean(axis=1)\n## Data Visualization\n\nIn this task, you need to create visualizations to analyze how different features contribute to the average grade:\n\n1. **Track vs. Average Grade**: Create a plot to show how the average grade varies with different academic tracks.\n2. **Gender vs. Average Grade**: Create a plot to compare the average grades of male and female students.\n3. **Hometown vs. Average Grade**: Create a plot to compare the average grades of students from different hometowns (e.g., Luzon, Visayas, Mindanao).\n\nThe visualizations should be created using `matplotlib` and `seaborn` libraries. Save the visualizations as image files (e.g., PNG) and place them in the `images/` directory.\n\n---\n\n## Results\n\n### Instru DataFrame\n\nFiltered results where:\n- Track is \"Instrumentation\"\n- Hometown is \"Luzon\"\n- Electronics score \u003e 70\n\n| Name  | GEAS | Electronics |\n|-------|------|-------------|\n| S6    | 90   | 82          |\n| S10   | 70   | 79          |\n\n### Mindy DataFrame\n\nFiltered results where:\n- Gender is \"Female\"\n- Hometown is \"Mindanao\"\n- Average score ≥ 55\n\n| Name | Track          | Electronics | Average |\n|------|----------------|-------------|---------|\n| S5   | Communication  | 69          | 79.33   |\n| S7   | Communication  | 73          | 68.67   |\n\n---\n## Changelogs\n1. Added a readme file\n2. Edited readme file\n3. Reworked on the phython code\n4. Added code for the graph for part 2\n5. Updated the graph to fit in 1 code\n---\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fckyle30%2Fpa4","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fckyle30%2Fpa4","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fckyle30%2Fpa4/lists"}