NuffSaid Backend Programming Project

For this project, you'll be using a publically available data source of schools in a CSV format. You'll download the data file and write two Python programs to manipulate the data.

• We expect this to take around 2 hours to complete. If it seems like it will take significantly longer than that to complete, please ask us first to make sure you aren't over-complicating the project.

Please commit all code to this Github repo, and press the “Finish” button in the top-right corner in Py once you’ve completed the challenge to let us know you’re done.

Part 0: Get the data

• Go to this page and download the dataset described as:
  • Year 2005-2006 (v.1b), States A-I, ZIP (769 KB) CSV File. This is the only file you will be using. You can ignore the other files on this page.
  • You may also find it helpful to consult the documentation for this dataset.
  • Unzip the file, rename it to school_data.csv, and put it in the directory where you'll write your Python program.
  • Now you have the data you need to get started.

Part 1: Load data from CSV and compute stats.

Write a method (or a method per question if you'd prefer) that loads the data out of the CSV file you downloaded and computes answers to the following questions:

• How many total schools are in this data set?
• How many schools are in each state?
• How many schools are in each Metro-centric locale?
• What city has the most schools in it? How many schools does it have in it?
• How many unique cities have at least one school in it?

Guidelines

• Create a file called count_schools.py, and write a method called print_counts.
• Please implement all of these features using pure python.
  • You may use the following libraries: csv, time, itertools
  • No other libraries may be used.

Example output (this is just a sample, the lists have been truncated and counts aren't correct)

>>> count_schools.print_counts()
Total Schools: 10000
Schools by State:
CO: 1000
DC: 200
AK: 600
DE: 300
AL: 1500
AR: 1100
...
Schools by Metro-centric locale:
1: 3000
3: 2000
2: 5000
5: 300
...
City with most schools: CHICAGO (50 schools)
Unique cities with at least one school: 1000

Part 2: Search over school data

We'd like teachers to be able to easily find the school they teach at. In order to do this, we'd like to offer a search feature that lets them search for their school using plain text.

• This feature should search over school name, city name, and state name.
• The top 3 matching results should be returned (see below for examples).

Guidelines

• When a query doesn't match exactly, you'll need to come up with a set of rules to rank results. In particular, make sure more precise matches show up at the top of the list, and if there isn't an exact match, but there is a close match, some results are returned. There is no perfect set of rules, but you should come up with a set that improve the end user search experience as much as possible.
• Searches should run in real-time, meaning that they should return results to the user in less than 200ms. It's ok to perform data loading and processing up front that takes longer than this if you'd like.
• Create a file called school_search.py, and write a method called search_schools.
• Please implement all of these features using pure python.
  • You may use the following libraries: csv, time, itertools
  • No other libraries may be used.

Evaluation

• Accuracy
  • We'll evaluate the accuracy of your search using sample queries. We have included a few below in Test Cases that you can test with (we'll also test with others).
  • The following queries should return the results shown below as the top hits. If multiple results are shown below, it doesn't matter if one appears before the other, but they must be the first results returned.
  • If you see [Next Best Hit], that means that you should include a reasonable hit for the search query, but that there isn't a specific hit that we'll be looking for.
• Performance
  • All results should return within 200ms.
  • EXTRA CREDIT: All results are returned within 10ms.
• Code Quality
  • We're looking for clear, easy to understand code. Write code that makes your thinking algorithm as obvious as possible.
  • Prioritize readability over cleverness. We care less that you can code a solution to this problem in one line, and more that we are able to easily follow your code.
  • Don't worry about documentation for this project. Do your best to make the code readable without requiring reading lots of documentation.

Test Cases

elementary school highland park
>>> school_search.search_schools("elementary school highland park")
Results for "elementary school highland park" (search took: 0.009s)
1. HIGHLAND PARK ELEMENTARY SCHOOL
MUSCLE SHOALS, AL
2. HIGHLAND PARK ELEMENTARY SCHOOL
PUEBLO, CO
3. [Next Best Hit]

jefferson belleville
>>> school_search.search_schools("jefferson belleville")
Results for "jefferson belleville" (search took: 0.000s)
1. JEFFERSON ELEM SCHOOL
BELLEVILLE, IL
2. [Next Best Hit]
3. [Next Best Hit]

riverside school 44
>>> school_search.search_schools("riverside school 44")
Results for "riverside school 44" (search took: 0.002s)
1. RIVERSIDE SCHOOL 44
INDIANAPOLIS, IN
2. [Next Best Hit]
3. [Next Best Hit]

granada charter school
>>> school_search.search_schools("granada charter school")
Results for "granada charter school" (search took: 0.001s)
1. NORTH VALLEY CHARTER ACADEMY
GRANADA HILLS, CA
2. GRANADA HILLS CHARTER HIGH
GRANADA HILLS, CA
3. [Next Best Hit]

foley high alabama
>>> school_search.search_schools("foley high alabama")
Results for "foley high alabama" (search took: 0.001s)
1. FOLEY HIGH SCHOOL
FOLEY, AL
2. [Next Best Hit]
3. [Next Best Hit]

KUSKOKWIM
>>> school_search.search_schools("KUSKOKWIM")
Results for "KUSKOKWIM" (search took: 0.001s)
1. TOP OF THE KUSKOKWIM SCHOOL
NIKOLAI, AK
(No additional results should be returned)