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# Project: Exploring NYC Public School Test Result Scores

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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.

```.mfe-app-workspace-11z5vno{font-family:JetBrainsMonoNL,Menlo,Monaco,'Courier New',monospace;font-size:13px;line-height:20px;}```# Re-run this cell
import pandas as pd

# Preview the data

# Start coding here...
# Add as many cells as you like...``````

#### .mfe-app-workspace-kj242g{position:absolute;top:-8px;}.mfe-app-workspace-11ezf91{display:inline-block;}.mfe-app-workspace-11ezf91:hover .Anchor__copyLink{visibility:visible;}Create a pandas DataFrame called best_math_schools containing the "school_name" and "average_math" score for all schools where the results are at least 80% of the maximum possible score, sorted by "average_math" in descending order.

``````# calculate best_math_schools
# the maximum score for each of the three SAT sections (math, reading, and writing) is 800, so you can use this to find the threshold of 80%.
_80pct_of_maximum_score = 800 * 0.8
_80pct_of_maximum_score``````
``````# Filtering the data
best_schools = schools[schools['average_math'] >= _80pct_of_maximum_score]
best_math_schools = best_schools[['school_name','average_math']].sort_values('average_math',ascending=False)
best_math_schools``````

#### Identify the top 10 performing schools based on scores across the three SAT sections, storing as a pandas DataFrame called top_10_schools containing the school name and a column named "total_SAT", with results sorted by total_SAT in descending order.

``````schools['total_SAT'] = schools['average_math'] + schools['average_reading'] + schools['average_writing']
top_10_schools = schools[['school_name','total_SAT']]
top_10_schools``````

#### Locate the NYC borough with the largest standard deviation for "total_SAT", storing as a DataFrame called largest_std_dev with "borough" as the index and three columns: "num_schools" for the number of schools in the borough, "average_SAT" for the mean of "total_SAT", and "std_SAT" for the standard deviation of "total_SAT". Round all numeric values to two decimal places.

``````# Grouping the data by borough

# Group the data by "borough" and find the number of schools, mean and standard deviation of "total_SAT"

# You can chain .groupby() with .agg() to calculate these values. The .agg() method accepts a list of statistics, as strings, to calculate, e.g., .agg(["min", "max", "median"]).

largest_std_dev = round(schools.groupby('borough')['total_SAT'].agg(['count','mean','std']),2)
largest_std_dev = largest_std_dev.rename(columns={'count': 'num_schools', 'mean': 'average_SAT', 'std': 'std_SAT'})
largest_std_dev
``````
``````# Filtering for the largest standard deviation
# You can subset for the row where "std" is equal to the largest value for that column across the DataFrame using boolean indexing.

largest_std_dev = largest_std_dev[largest_std_dev['std_SAT'] >= 230]
largest_std_dev``````
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