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 $(9$th$,10$th$,11$th$,12$th$)$ at each school.

Reading Scores by Grade

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

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$)$

Scores by School Size

Use the following code to bin the per$\_$school$\_$summary.

size$\_$bins $=[0,1000,2000,5000]$

labels $=["$Small "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$).$

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".