Pandas_Challenge

Using Pandas and Jupyter lab,Follwing analysis has been performed from the given CSV file in the resources folder. 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) output: Total Schools Total Students Total Budget Average math score Average reading score %passing math %passing reading %overall pass 0 15 39170 24649428 78.985371 81.87784 74.980853 85.805463 65.172326Include the following School Summary: 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) Output: School types Total Students Total Budget per student budget Average math score Average reading score %passing math %passing reading %overall pass Bailey High School District 4976 3124928.0 628.0 77.048432 81.033963 66.680064 81.933280 54.642283 Cabrera High School Charter 1858 1081356.0 582.0 83.061895 83.975780 94.133477 97.039828 91.334769 Figueroa High School District 2949 1884411.0 639.0 76.711767 81.158020 65.988471 80.739234 53.204476 Ford High School District 2739 1763916.0 644.0 77.102592 80.746258 68.309602 79.299014 54.289887 Griffin High School Charter 1468 917500.0 625.0 83.351499 83.816757 93.392371 97.138965 90.599455 Performed highest and lowest performing school based on the %overall pass calculations to create a DataFrame that lists the average math scoreand reading score for students of each grade level (9th, 10th, 11th, 12th) at each school. created four bins with reasonable cutoff values to group school spending Created a DataFrame called size_summary that breaks down school performance based on school size (small, medium, or large). Used the per_school_summary DataFrame from the previous step to create a new DataFrame called type_summary.