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Photo by Jannis Lucas on Unsplash.

Every year, American high school students take SATs, which are standardized tests intended to measure literacy, numeracy, and writing skills. There are three sections - reading, math, and writing, each with a maximum score of 800 points. These tests are extremely important for students and colleges, as they play a pivotal role in the admissions process.

Analyzing the performance of schools is important for a variety of stakeholders, including policy and education professionals, researchers, government, and even parents considering which school their children should attend.

You have been provided with a dataset called schools.csv, which is previewed below.

You have been tasked with answering three key questions about New York City (NYC) public school SAT performance.

# Re-run this cell 
import pandas as pd

# Read in the data
schools = pd.read_csv("schools.csv")

# Preview the data
schools.head()

Which NYC schools have the best math results?

  • The best math results are at least 80% of the maximum possible score of 800 for math.
  • Save your results in a pandas DataFrame called best_math_schools, including "school_name" and "average_math" columns, sorted by "average_math" in descending order.
import pandas as pd
schools = pd.read_csv('schools.csv')
#Filter dataframe
best_math_schools = schools[schools['average_math']>=800*.80].sort_values(['average_math'],ascending=False)
#Select columns
best_math_schools = best_math_schools[['school_name', 'average_math']]
best_math_schools

What are the top 10 performing schools based on the combined SAT scores?

  • Save your results as a pandas DataFrame called top_10_schools containing the "school_name" and a new column named "total_SAT", with results ordered by "total_SAT" in descending order ("total_SAT" being the sum of math, reading, and writing scores).
import pandas as pd
help(pd.DataFrame.sum)
import pandas as pd
schools = pd.read_csv('schools.csv')
#add new column total_SAT *sum(axis=1) to apply the sum to columns
schools['total_SAT'] = schools[['average_math','average_reading','average_writing']].sum(axis=1)
#Dataframe top_10_schools
top_10_schools = schools[['school_name','total_SAT']].sort_values(['total_SAT'], ascending=False).head(10)
top_10_schools

Which single borough has the largest standard deviation in the combined SAT score?

  • Save your results as a pandas DataFrame called largest_std_dev.
  • The DataFrame should contain one row, with:
    • "borough" - the name of the NYC borough with the largest standard deviation of "total_SAT".
    • "num_schools" - the number of schools in the borough.
    • "average_SAT" - the mean of "total_SAT".
    • "std_SAT" - the standard deviation of "total_SAT".
  • Round all numeric values to two decimal places.
import pandas as pd
schools = pd.read_csv('schools.csv')
schools['total_SAT'] = schools[['average_math','average_reading','average_writing']].sum(axis=1)
#Grouping by district and calculating aggregation functions
largest_std_dev = schools.groupby('borough').agg(num_schools=('school_name','count'),
                                                 average_SAT=('total_SAT','mean'),
                                                 std_SAT=('total_SAT','std')).round(2).sort_values(['std_SAT'],ascending=False).head(1)
largest_std_dev