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https://github.com/w7negreiros/pandas_challenge_2024

Pandas Challenge - UofT Data Analytics - Bootcamp
https://github.com/w7negreiros/pandas_challenge_2024

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Pandas Challenge - UofT Data Analytics - Bootcamp

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# Instructions

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.



# Hint: 

Check out the sample solution called PyCitySchools_starter.ipynb located in the .zip file to review the desired format for this assignment.



# District Summary

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)



# School Summary

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)



# Highest-Performing Schools (by % Overall Passing)

Sort the schools by % Overall Passing in descending order and display the top 5 rows.



Save the results in a DataFrame called "top_schools".



# Lowest-Performing Schools (by % Overall Passing)

Sort the schools by % Overall Passing in ascending order and display the top 5 rows.



Save the results in a DataFrame called "bottom_schools".



# Math Scores by Grade

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.



# Reading Scores by Grade

Create a DataFrame that lists the average reading score for students of each grade level (9th, 10th, 11th, 12th) at each school.



# Scores by School Spending

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.

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spending_bins = [0, 585, 630, 645, 680]

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labels = ["<$585", "$585-630", "$630-645", "$645-680"]

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Use pd.cut to categorize spending based on the bins.



Use the following code to then calculate mean scores per spending range.

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spending_math_scores = school_spending_df.groupby(["Spending Ranges (Per Student)"])["Average Math Score"].mean()

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spending_reading_scores = school_spending_df.groupby(["Spending Ranges (Per Student)"])["Average Reading Score"].mean()

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spending_passing_math = school_spending_df.groupby(["Spending Ranges (Per Student)"])["% Passing Math"].mean()

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spending_passing_reading = school_spending_df.groupby(["Spending Ranges (Per Student)"])["% Passing Reading"].mean()

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overall_passing_spending = school_spending_df.groupby(["Spending Ranges (Per Student)"])["% Overall Passing"].mean()

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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)



# Scores by School Size

Use the following code to bin the per_school_summary.

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size_bins = [0, 1000, 2000, 5000]

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labels = ["Small (<1000)", "Medium (1000-2000)", "Large (2000-5000)"]

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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).



# Scores by School Type

Use the per_school_summary DataFrame from the previous step to create a new DataFrame called type_summary.



This new DataFrame should show school performance based on the "School Type".