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Use Python and Pandas library to analyze school distrct data and showcase trends in school performance based on key metrics.
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Use Jupyter Notebook to visualize data outcomes as table format.
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This analysis assists the school board and district level in making desicions of budgets and priorities.
- Read raw data in csv file.
- Clean and inspect data, correct inappropriate data.
- Merge datasets to create new DataFrame gathering more information.
- Perform calculations for key metrics use groupby() function.
- Visualize data with tables to tell story and showcase trends.
Goal *Comparing two analysis results after removing the ninth-grade math and reading scores from Thomas High School, make cohesive conclusions in Jupyter Notebook.
After removing the ninth-grade math and reading scores from Thomas High School, it affacts summary tables by slightly reducing the average scores and enomous decreasing for the passing percentage rate, including both math and reading passing percentage as well as overall passing percentage.
There are 20 Analysis tables are in the picture folder. Attached are two examples.
- The Thomas_9th_info is information about descriptive statistic before removing the ninth-grade math and reading scores from Thomas High School.
- The final_compare_summary is the two summary tables groupby each school, comparing between before and after removing incorrect data.
