Using Pandas DataFrames, I created a notebook that analyizes school and standardized test data.
This notebook will provide the following data points outputted in the code to provide information about patterns by district and by school:
The following outputs for the district summary:
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)
The following expected outputs for the 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)
From this code's outputs, you'll also be able to see which are the highest and lowest performing schools, math and reading scores by grade and school spending breakdowns.
##Conclusions
After analyzing the outputs from this notebook, we can infere several conclusions about the data.
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Charter schools have higher average scores in both math and reading compared to district schools. For example, the charter school data shows there was an student average score for 83.47% in math compared to the district school wheere the average score was only 76.96% (rounded). Some of this may be impacted by the size of the schools themselves, which leads me to my next potential conclusion:
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The percent of students who passed math, reading and passed overall was higher at medium schools compared to small or large schools. The same trend showed true to the average math and reeading scores at medium-size schools compared to the other sizes.