In this assignment, you’ll create and manipulate Pandas DataFrames to analyze school and standardized test data.
You are the new Chief Data Scientist for your city's school district. In this capacity, you'll be helping the school board and mayor make strategic decisions regarding future school budgets and priorities.
As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your task is to aggregate the data to showcase obvious trends in school performance.
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Create a new repository for this project called
pandas-challenge. Do not add this homework to an existing repository. -
Clone the new repository to your computer.
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Inside your local Git repository, create a folder for this homework assignment and name the folder
PyCitySchools. -
Add your Jupyter notebook to this folder. This will be the main script to run for analysis.
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Push these changes to GitHub or GitLab.
Having spent years analyzing financial records for big banks, you've finally scratched your idealistic itch and joined the education sector. Your latest role is Chief Data Scientist for your city's school district. In this capacity, you'll be helping the school board and mayor make strategic decisions regarding future school budgets and priorities.
As a first task, you've been asked to analyze the district-wide standardized test results. You'll be given access to every student's math and reading scores, as well as various information on the schools they attend. Your task is to aggregate the data to showcase obvious trends in school performance.
Using Pandas and Jupyter Notebook, create a report that includes the following data. Your report must include a written description of at least two observable trends based on the data.
Perform the necessary calculations and then create a high-level snapshot of the district's key metrics in a DataFrame.
Include the following:
Total number of unique schools
Total students
Total budget
Average math score
Average reading score
% passing math (the percentage of students who passed math)
% passing reading (the percentage of students who passed reading)
% overall passing (the percentage of students who passed math AND reading)
Perform the necessary calculations and then create a DataFrame that summarizes key metrics about each school.
Include the following:
- School name
- School type
- Total students
- Total school budget
- Per student budget
- Average math score
- Average reading score
- % passing math (the percentage of students who passed math)
- % passing reading (the percentage of students who passed reading)
- % overall passing (the percentage of students who passed math AND reading)
Sort the schools by % Overall Passing in descending order and display the top 5 rows.
Save the results in a DataFrame called "top_schools".
Sort the schools by % Overall Passing in ascending order and display the top 5 rows.
Save the results in a DataFrame called "bottom_schools".
Perform the necessary calculations to create a DataFrame that lists the average math score for students of each grade level (9th, 10th, 11th, 12th) at each school.
Create a DataFrame that lists the average reading score for students of each grade level (9th, 10th, 11th, 12th) at each school.
Create a table that breaks down school performance based on average spending ranges (per student).
Use the code provided below to create four bins with reasonable cutoff values to group school spending.
spending_bins = [0, 585, 630, 645, 680] labels = ["<$585", "$585-630", "$630-645", "$645-680"]
Use pd.cut to categorize spending based on the bins.
Use the following code to then calculate mean scores per spending range.
spending_math_scores = school_spending_df.groupby(["Spending Ranges (Per Student)"])["Average Math Score"].mean()
spending_reading_scores = school_spending_df.groupby(["Spending Ranges (Per Student)"])["Average Reading Score"].mean()
spending_passing_math = school_spending_df.groupby(["Spending Ranges (Per Student)"])["% Passing Math"].mean()
spending_passing_reading = school_spending_df.groupby(["Spending Ranges (Per Student)"])["% Passing Reading"].mean()
overall_passing_spending = school_spending_df.groupby(["Spending Ranges (Per Student)"])["% Overall Passing"].mean()
Use the scores above to create a DataFrame called spending_summary.
Include the following metrics in the table:
Average math score
Average reading score
% passing math (the percentage of students who passed math)
% passing reading (the percentage of students who passed reading)
% overall passing (the percentage of students who passed math AND reading)
Use the following code to bin the per_school_summary.
size_bins = [0, 1000, 2000, 5000] labels = ["Small (<1000)", "Medium (1000-2000)", "Large (2000-5000)"]
Use pd.cut on the "Total Students" column of the per_school_summary DataFrame.
Create a DataFrame called size_summary that breaks down school performance based on school size (small, medium, or large).