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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()

Exploring DataFrame

# with .shape
schools.shape
# with .info()
schools.info()
# with .describe()
schools.describe()
# with .columns
schools.columns
# with .index
schools.index

Question N1: Which NYC schools have the best math results?

## Creating a list of columns to subset
cols_to_subset = ["school_name","average_math"]

## Subsetting math score by school
math_score_by_school=schools[cols_to_subset]

## Filtering school with best result in Math( 80% of the maximum score 800)
best_math_score_greater_80Percent = math_score_by_school[math_score_by_school["average_math"] >= (800*80/100)]

## Sorting the best Math school in descending order 
best_math_schools = best_math_score_greater_80Percent.sort_values("average_math", ascending=False)

## print (best_math_schools)

Question N2: What are the top 10 performing schools based on the combined SAT scores?

## Calculating the Total SAT in new columns
schools["total_SAT"]=schools["average_math"] + schools["average_reading"] \
                        + schools["average_writing"]

## Creating a list of columns to subset
cols_to_subset_total_SAT = ["school_name","total_SAT"]

## Subsetting math score by school
school_total_SAT = schools[cols_to_subset_total_SAT]

## Sorting the best total_SAT school in descending order 
school_total_SAT_sorted = school_total_SAT.sort_values("total_SAT", ascending=False)

## top 10 schools with total_SAT
top_10_schools = school_total_SAT_sorted.head(10)

top_10_schools

Question N3:Which single borough has the largest standard deviation in the combined SAT score?

## Group data by borough and calculating number of schools, average SAT and standard deviation for each group
group_by_borough = schools.groupby("borough")["total_SAT"].agg(num_schools = "count", average_SAT = "mean", std_SAT = "std")

## Rounding  all numerical value to two decimal places 
group_by_boroug_rounded = group_by_borough.round(2)

## transforming result into dataframe
df_borough = group_by_boroug_rounded.reset_index()

## Renaming the columns
## df_borough = df_borough.rename(columns = {"count":"num_schools","mean": "average_SAT","std": "std_SAT"})

## Sorting by  std_SAT
std_SAT_sorted = df_borough.sort_values("std_SAT", ascending=False)

# Filtering the largest standard deviation in Borough
largest_std_dev = std_SAT_sorted.head(1)

largest_std_dev

Question N3: Second Solution

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