Data Wrangling and Visualization in Python

Overview

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.

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Table of Contents

• Overview
• Dataset
• Intended Learning Outcomes
• Installation
• Instructions
  • Task 1: Data Wrangling
  • Task 2: Data Visualization
• Results
  • Instru DataFrame
  • Mindy DataFrame
• Visualizations
• References

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Dataset

The 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.

The dataset includes the following features:

• Name: The name of the student
• Gender: Gender of the student
• Track: The academic track chosen by the student (e.g., Instrumentation, Communication)
• Math, GEAS, Electronics: Scores in respective subjects
• Hometown: Region of origin (e.g., Luzon, Visayas, Mindanao)

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Intended Learning Outcomes

1. Understand how to use Python libraries for data wrangling (i.e., pandas) and data visualization (i.e., matplotlib, seaborn).
2. Learn to clean, manipulate, and transform datasets into useful information.
3. Create visual representations that tell a story about the dataset.

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Instructions

Task 1: Data Wrangling

In this task, you need to create specific subsets of the dataset based on the following conditions:

1. Instru DataFrame: Extract rows where:

   • The Track is Instrumentation.
   • The Hometown is Luzon.
   • The Electronics score is greater than 70.

   Example Output:

   Name	GEAS	Electronics
   S6	90	82
   S10	70	79

2. Mindy DataFrame: Extract rows where:

   • The Gender is Female.
   • The Hometown is Mindanao.
   • The Average score is 55 or higher.

   To calculate the Average, use the following formula:

   data['Average'] = data[['Math', 'GEAS', 'Electronics']].mean(axis=1)

Data Visualization

In this task, you need to create visualizations to analyze how different features contribute to the average grade:

1. Track vs. Average Grade: Create a plot to show how the average grade varies with different academic tracks.
2. Gender vs. Average Grade: Create a plot to compare the average grades of male and female students.
3. Hometown vs. Average Grade: Create a plot to compare the average grades of students from different hometowns (e.g., Luzon, Visayas, Mindanao).

The 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.

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Results

Instru DataFrame

Filtered results where:

• Track is "Instrumentation"
• Hometown is "Luzon"
• Electronics score > 70

Name	GEAS	Electronics
S6	90	82
S10	70	79

Mindy DataFrame

Filtered results where:

• Gender is "Female"
• Hometown is "Mindanao"
• Average score ≥ 55

Name	Track	Electronics	Average
S5	Communication	69	79.33
S7	Communication	73	68.67

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Changelogs

1. Added a readme file
2. Edited readme file
3. Reworked on the phython code
4. Added code for the graph for part 2
5. Updated the graph to fit in 1 code

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