DataParser

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  Manhattan's 20 most frequent violations (table made via FileReader.py)

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  Brooklyn's 20 most frequent violations (table made via FileReader.py)

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  Bronx's 20 most frequent violations (table made via FileReader.py)

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  Queens' 20 most frequent violations (table made via FileReader.py)

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  Staten Island's 20 most frequent violations (table made via FileReader.py)

Inspiration

We had the desire to help local NYC businesses avoid unnecessary fares by distilling the most common violation per burrow.

What it does

Parses NYC OATH csv files (downloaded locally on my computer) and creates a new csv file of the necessary data to read. From this point another python function analyzes this csv created file to display bar graphs of every borough's 20 most frequent violations.

How we built it

Python and matplotlib library

Challenges we ran into

Data analysis, analyzing all 20 million cases at once

Accomplishments that we're proud of

File creation/ writing and matplotlib graph displaying.

What we learned

time management, csv files, matplotlib utilization, api utilization (apparent in our outdated python file)

What's next for DataParser

A presentation can be made from this information compiling to small businesses urging them to clean the front of their stores, as that was the most frequent issue.

Built With

• python