PyCitySchools Analysis

Overview

This project analyzes school performance across a district using Python and Pandas. By aggregating and manipulating standardized test data, we identify trends in student achievement based on factors such as school type, budget, and student demographics.

Skills Demonstrated

• Data Wrangling: Cleaning and organizing large datasets using Pandas
• Data Aggregation: Computing summary statistics with .groupby()
• Conditional Analysis: Using logic to segment performance data
• Visualization: Presenting trends using DataFrames and Matplotlib
• Critical Thinking: Identifying key patterns in education data

Key Findings

Spending per Student

• Schools with lower spending per student tend to achieve higher average scores and better passing rates.
• Schools spending less than $585 per student had the highest passing rates:
  • Reading: 96.61%
  • Math: 93.46%
  • Overall: 90.36%
• Schools spending $645–$680 per student had significantly lower passing rates:
  • Reading: 81.13%
  • Math: 66.16%
  • Overall: 53.52%
• This suggests that higher spending does not guarantee better academic outcomes and raises questions about resource efficiency.

School Size

• Smaller and medium-sized schools generally perform better than large schools.
• Average Math Scores:
  • Small schools (<1,000 students): 83.82
  • Medium schools (1,000–2,000 students): 83.37
  • Large schools (>2,000 students): Lower scores
• Average Reading Scores:
  • Medium-sized schools performed slightly better than both small and large schools (83.86).
• This pattern suggests that smaller schools provide more personalized instruction, improving student outcomes.

School Type

• Charter schools consistently outperform district schools.
• Average Scores:
  • Math: Charter (83.47) vs. District (76.96)
  • Reading: Charter (83.90) vs. District (80.97)
• Passing Rates:
  • Charter Schools: 90.43% overall
  • District Schools: 53.67% overall
• The data suggests that charter schools may have more effective academic programs and resource allocation.

Code Implementation

The analysis follows these key steps:

1. Load and Merge Data

import pandas as pd
from pathlib import Path

# Load datasets
school_data = pd.read_csv("Resources/schools_complete.csv")
student_data = pd.read_csv("Resources/students_complete.csv")

# Merge datasets
school_data_complete = pd.merge(student_data, school_data, how="left", on=["school_name"])

2. District-Level Analysis

# Total number of schools
school_count = len(school_data_complete["school_name"].unique())

# Total number of students
student_count = len(school_data_complete["student_name"].unique())

# Total Budget
total_budget = school_data_complete.groupby("school_name")["budget"].first().sum()

# Avg math and reading scores
average_math_score = school_data_complete["math_score"].mean()
average_reading_score = school_data_complete["reading_score"].mean()

# Percentage of students passing math and reading
passing_math_reading_count = school_data_complete[
    (school_data_complete["math_score"] >= 70) & (school_data_complete["reading_score"] >= 70)
].count()["student_name"]
overall_passing_rate = passing_math_reading_count /  float(student_count) * 100
overall_passing_rate

School-Level Analysis

# School type, total students, total budget, per-student budget
school_types = school_data.set_index("school_name")["type"]
per_school_counts = school_data.set_index("school_name")["size"]
per_school_budget = school_data.set_index("school_name")["budget"]
per_school_capita = per_school_budget / per_school_counts

# Avg math and reading scores
per_school_math = school_data_complete.groupby('school_name')['math_score'].mean()
per_school_reading = school_data_complete.groupby('school_name')['reading_score'].mean()

# Students passing math and reading
students_passing_math_and_reading = school_data_complete[
    (school_data_complete["reading_score"] >= 70) & (school_data_complete["math_score"] >= 70)
]
school_students_passing_math_and_reading = students_passing_math_and_reading.groupby(["school_name"]).size()
print(school_students_passing_math_and_reading)

Technologies Used

• Python
• Pandas (Data manipulation)
• Jupyter Notebook (Interactive analysis)

References

• Chat GPT