Programming-Assignment-4

README: Experiment 4 - Data Wrangling and Data Visualization

Intended Learning Outcomes

1. To identify the codes and functions needed in cleaning and visualizing data.
2. To be able to apply and use the different codes and functions in creating a Python program for data wrangling and data visualization.

Files Included:

• Mindy.csv
• Instru.csv
• DEL CARMEN_PA#4.ipynb

Problem Statement

Using data wrangling and data visualization techniques with storytelling, analyze the data and present different:

• (i) Data frames
• (ii) Visuals

Load ECE Board Exam

• Code:

• Output:

Task 1: Create Data Frames

a. Filename: Instru

• Columns: ["Name", "GEAS", "Electronics >70"]
• Conditions: Track is constant as Instrumentation, Hometown is constant as Luzon
• Code that will locate students from Luzon and their track is Instrumentation and has a score greater than 70 in Electronics.
• Code:

• Output:

b. Filename: Mindy

• Columns: ["Name", "Track", "Electronics", "Average >=55"]
• Conditions: Hometown is constant as Mindanao, Gender is constant as Female
• Code that will show students from Mindanao who are female. It also shows the students whose average is greater than or equal to 55.
• Code:

• Output:

Task 2: Create a Visualization

• Visualize how different features (chosen track in college, gender, hometown) contribute to the average grade.
• Analyze whether chosen track, gender, or hometown contributes to a higher average score.
• Codes:
• By Track:
• Code that will display a graph that shows the average for each Track.

• Output:

• By Gender:
• Code that will display a graph that shows the average for each Gender.

• Output:

• By Hometown:
• Code that will display a graph that shows the average for each Hometown.

• Output:

Conclusion:

• This experiment successfully applied data wrangling and visualization techniques to analyze ECE board exam data. We created filtered dataframes and visualizations to examine how track, gender, and hometown influence academic performance. The exercise demonstrated the power of Python's pandas and matplotlib libraries for transforming raw data into meaningful insights, highlighting the importance of data-driven analysis in identifying trends and patterns for informed decision-making.