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Using pandas to analyze school and student performance within a district
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raw_data
PyCity_Schools_Analysis.ipynb
README.md

README.md

Observed Trends:

  • Looking at all the reading scores and math scores in each table, students tend to do better in reading than in math.
  • Looking at the top performing schools, bottom performing schools, and passing rates by school type, students have a much higher passing rate in charter schools (~95%) than in district schools (~74%).
  • Looking at the summary of performance by spending ranges per student, it seems that students have a higher passing rate in the lower spending ranges (<$615).
# Import dependencies
import pandas as pd
import numpy as np

# Create paths to csv files
school_path = "raw_data/schools_complete.csv"
student_path = "raw_data/students_complete.csv"

# Read the csv files
schools = pd.read_csv(school_path)
schools = schools.rename(columns={"name": "school"}) # so we can merge later

students = pd.read_csv(student_path)
students = pd.DataFrame(students)

District Summary

# Calculate total schools, students, and budget of district
total_schools = schools['School ID'].count()
total_students = schools['size'].sum()
total_budget = schools['budget'].sum()

# Calculate average math and reading scores
avg_math = students['math_score'].mean()
avg_read = students['reading_score'].mean()

# Calculate percent passing rates
pass_math = students.loc[students['math_score'] >= 70]
per_math = len(pass_math)/ total_students * 100

pass_read = students.loc[students['reading_score'] >= 70]
per_read = len(pass_read)/ total_students * 100

overall = (per_math + per_read)/2 

# Create dataframe for district summary
district_summary = pd.DataFrame({"Total Schools": total_schools,
                                "Total Students": total_students,
                                "Total Budget": total_budget,
                                "Average Math Score": avg_math,
                                "Average Reading Score": avg_read,
                                "% Passing Math": per_math,
                                "% Passing Reading": per_read,
                                "% Overall Passing Rate": [overall]})

#Reorganize columns with double brackets
district_summary = district_summary[["Total Schools", "Total Students", "Total Budget", 
                                     "Average Math Score", "Average Reading Score", 
                                     "% Passing Math", "% Passing Reading", "% Overall Passing Rate"]]

# Format to "$0,000.00"
district_summary['Total Budget'] = district_summary['Total Budget'].map('${:,.2f}'.format)

district_summary
Total Schools Total Students Total Budget Average Math Score Average Reading Score % Passing Math % Passing Reading % Overall Passing Rate
0 15 39170 $24,649,428.00 78.985371 81.87784 74.980853 85.805463 80.393158

School Summary

# Merge schools and students
merge_df = pd.merge(schools, students, on="school")
merge_df = merge_df.drop(['School ID', 'Student ID'], axis=1)

# Set school as index, get type, total students, total budget and per student budget
school_index = schools.set_index('school')
school_type = school_index['type']
school_students = school_index['size']
school_budget = school_index['budget']
psb = school_budget/school_students

# Set school as index and groupby school for merge_df to get average scores by school
index = merge_df.set_index('school')
grouped = index.groupby(['school'])

# Get average math and reading score
school_avg_math = grouped['math_score'].mean()
school_avg_read = grouped['reading_score'].mean()

# Calculate percent that passed math
total_stu = grouped['name'].count()
grouped_math = pass_math.groupby('school')
school_pass_math = grouped_math['name'].count()/total_stu*100

# Calculate percent that passed reading
grouped_reading = pass_read.groupby('school')
school_pass_read = grouped_reading['name'].count()/total_stu*100
                             
# Calculate overall passing rate
overall_pass = (school_pass_math + school_pass_read)/2

# Create dataframe for school summary
school_summary = pd.DataFrame({"School Type": school_type,
                              "Total Students": school_students,
                              "Total School Budget": school_budget,
                              "Per Student Budget": psb,
                              "Average Math Score": school_avg_math,
                              "Average Reading Score": school_avg_read,
                              "% Passing Math": school_pass_math,
                              "% Passing Reading": school_pass_read,
                              "Overall Passing Rate": overall_pass})

# Reorganize school summary columns 
school_summary = school_summary[["School Type", "Total Students", "Total School Budget", 
                                 "Per Student Budget", "Average Math Score", "Average Reading Score", 
                                 "% Passing Math", "% Passing Reading", "Overall Passing Rate"]]

# Format to "$0,000.00"
school_summary['Total School Budget'] = school_summary['Total School Budget'].map('${:,.2f}'.format)
school_summary['Per Student Budget'] = school_summary['Per Student Budget'].map('${:,.2f}'.format)

school_summary.head()
School Type Total Students Total School Budget Per Student Budget Average Math Score Average Reading Score % Passing Math % Passing Reading Overall Passing Rate
Bailey High School District 4976 $3,124,928.00 $628.00 77.048432 81.033963 66.680064 81.933280 74.306672
Cabrera High School Charter 1858 $1,081,356.00 $582.00 83.061895 83.975780 94.133477 97.039828 95.586652
Figueroa High School District 2949 $1,884,411.00 $639.00 76.711767 81.158020 65.988471 80.739234 73.363852
Ford High School District 2739 $1,763,916.00 $644.00 77.102592 80.746258 68.309602 79.299014 73.804308
Griffin High School Charter 1468 $917,500.00 $625.00 83.351499 83.816757 93.392371 97.138965 95.265668

Top Performing Schools (By Passing Rate)

# Create top performing schools summary by passing rate
top_summary = school_summary.loc[school_summary['Overall Passing Rate'] > 90]
top_summary.sort_values(['Overall Passing Rate'], ascending=False).head()
School Type Total Students Total School Budget Per Student Budget Average Math Score Average Reading Score % Passing Math % Passing Reading Overall Passing Rate
Cabrera High School Charter 1858 $1,081,356.00 $582.00 83.061895 83.975780 94.133477 97.039828 95.586652
Thomas High School Charter 1635 $1,043,130.00 $638.00 83.418349 83.848930 93.272171 97.308869 95.290520
Pena High School Charter 962 $585,858.00 $609.00 83.839917 84.044699 94.594595 95.945946 95.270270
Griffin High School Charter 1468 $917,500.00 $625.00 83.351499 83.816757 93.392371 97.138965 95.265668
Wilson High School Charter 2283 $1,319,574.00 $578.00 83.274201 83.989488 93.867718 96.539641 95.203679

Bottom Performing Schools (By Passing Rate)

# Create bottom performing schools summary by passing rate
bottom_summary = school_summary.loc[school_summary['Overall Passing Rate'] < 75]
bottom_summary.sort_values(['Overall Passing Rate'], ascending=True).head()
School Type Total Students Total School Budget Per Student Budget Average Math Score Average Reading Score % Passing Math % Passing Reading Overall Passing Rate
Rodriguez High School District 3999 $2,547,363.00 $637.00 76.842711 80.744686 66.366592 80.220055 73.293323
Figueroa High School District 2949 $1,884,411.00 $639.00 76.711767 81.158020 65.988471 80.739234 73.363852
Huang High School District 2917 $1,910,635.00 $655.00 76.629414 81.182722 65.683922 81.316421 73.500171
Johnson High School District 4761 $3,094,650.00 $650.00 77.072464 80.966394 66.057551 81.222432 73.639992
Ford High School District 2739 $1,763,916.00 $644.00 77.102592 80.746258 68.309602 79.299014 73.804308

Math Scores by Grade

# Math scores by grade
ninth = students.loc[students['grade']=='9th'].groupby("school")
ninth_math = ninth['math_score'].mean()
tenth = students.loc[students['grade']=='10th'].groupby("school")
tenth_math = tenth['math_score'].mean()
eleventh = students.loc[students['grade']=='11th'].groupby("school")
eleventh_math = eleventh['math_score'].mean()
twelfth = students.loc[students['grade']=='12th'].groupby("school")
twelfth_math = twelfth['math_score'].mean()

# Create dataframe for math scores summary
math_summary = pd.DataFrame({"9th": ninth_math,
                            "10th": tenth_math,
                            "11th": eleventh_math,
                            "12th": twelfth_math})
math_summary = math_summary[["9th","10th","11th","12th"]]
del math_summary.index.name

math_summary.head()
9th 10th 11th 12th
Bailey High School 77.083676 76.996772 77.515588 76.492218
Cabrera High School 83.094697 83.154506 82.765560 83.277487
Figueroa High School 76.403037 76.539974 76.884344 77.151369
Ford High School 77.361345 77.672316 76.918058 76.179963
Griffin High School 82.044010 84.229064 83.842105 83.356164

Reading Scores by Grade

# Reading scores by grade
r_ninth = students.loc[students['grade'] == '9th'].groupby("school")
ninth_read = r_ninth['reading_score'].mean()
r_tenth = students.loc[students['grade'] == '10th'].groupby("school")
tenth_read = r_tenth['reading_score'].mean()
r_eleventh = students.loc[students['grade'] == '11th'].groupby("school")
eleventh_read = r_eleventh['reading_score'].mean()
r_twelfth = students.loc[students['grade'] == '12th'].groupby("school")
twelfth_read = r_twelfth['reading_score'].mean()

# Create dataframe for reading scores summary
read_summary = pd.DataFrame({"9th": ninth_read,
                            "10th": tenth_read,
                            "11th": eleventh_read,
                            "12th": twelfth_read})
read_summary = read_summary[["9th","10th","11th","12th"]]
del read_summary.index.name

read_summary.head()
9th 10th 11th 12th
Bailey High School 81.303155 80.907183 80.945643 80.912451
Cabrera High School 83.676136 84.253219 83.788382 84.287958
Figueroa High School 81.198598 81.408912 80.640339 81.384863
Ford High School 80.632653 81.262712 80.403642 80.662338
Griffin High School 83.369193 83.706897 84.288089 84.013699

Scores by School Spending

# Create bins
bins = [0,585,615,645,675]

# Create names for the bins
spending_range = ['<$585','$585-615','$615-645','$645-675']

# Change formatting of per student budget in school_summary from string back to float so it can be binned 
school_summary['Per Student Budget'] = school_summary['Per Student Budget'].str.replace('$', '')
school_summary['Per Student Budget'] = school_summary['Per Student Budget'].astype(float)
                                                      
school_summary["Spending Ranges (Per Student)"] = pd.cut(school_summary["Per Student Budget"], 
                                                         bins, labels=spending_range)
spend_summary = school_summary.groupby("Spending Ranges (Per Student)")
spend_summary = spend_summary[["Average Math Score", "Average Reading Score", "% Passing Math", 
                               "% Passing Reading", "Overall Passing Rate"]]
spend_summary.mean()
Average Math Score Average Reading Score % Passing Math % Passing Reading Overall Passing Rate
Spending Ranges (Per Student)
<$585 83.455399 83.933814 93.460096 96.610877 95.035486
$585-615 83.599686 83.885211 94.230858 95.900287 95.065572
$615-645 79.079225 81.891436 75.668212 86.106569 80.887391
$645-675 76.997210 81.027843 66.164813 81.133951 73.649382

Scores by School Size

# Create bins
bins2 = [0, 1000, 2000, 5000]

# Create names for bins
size_range = ['Small', 'Medium', 'Large']

school_summary["School Size"] = pd.cut(school_summary["Total Students"], 
                                                         bins2, labels=size_range)
size_summary = school_summary.groupby("School Size")
size_summary = size_summary[["Average Math Score", "Average Reading Score", "% Passing Math", 
                             "% Passing Reading", "Overall Passing Rate"]]
size_summary.mean()
</style>
Average Math Score Average Reading Score % Passing Math % Passing Reading Overall Passing Rate
School Size
Small 83.821598 83.929843 93.550225 96.099437 94.824831
Medium 83.374684 83.864438 93.599695 96.790680 95.195187
Large 77.746417 81.344493 69.963361 82.766634 76.364998

Scores by School Type

type_summary = school_summary.groupby("School Type")
type_summary = type_summary[["Average Math Score", "Average Reading Score", "% Passing Math", 
                             "% Passing Reading", "Overall Passing Rate"]]
type_summary.mean()
Average Math Score Average Reading Score % Passing Math % Passing Reading Overall Passing Rate
School Type
Charter 83.473852 83.896421 93.620830 96.586489 95.103660
District 76.956733 80.966636 66.548453 80.799062 73.673757
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