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Writer's pictureenriqueov10

Data Explanations

Csv Files:


Quarterly Census of Employment and Wages (QCEW) you must fill this out (I use NYC Region, NAICS Sector: 71, and all 4 Industries)


All four of the datasets just show the amount of employees the entertainment sector had and their wages. I mainly used months 1-3 of all quarters of the year as well as total wages for my project.


A large list of the legally operating businesses in NYC. For this list I had to remove a lot of unnecessary columns, as well as only select rows with entertainment/recreation related industries. Fun fact: There are Gaming Cafes in NYC.


A list of the recreation and community centers and such in NYC, again, I only selected columns with the information I need in them.


Other Tables I found, then turned into CSV Files:


Table at the Bottom of Page


The table at the bottom of the page holds statistics of the share of workers per neighborhood. With this, I cleaned the data using ctrl + f on Atom and using Regex to replace certain characters such as spaces or commas (in number values) and replace them in a way that made the file .csv friendly.


As the title suggests, this is a page listing all of the live music venues in NYC. I did a similar thing as I did with the table above to clean it.


*On all files with addresses without boroughs, I manually added the borough to them.

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What I did

Pandas and (some) pandasql: So beginning with the data, I had to clean the .csv files with a large amount of columns or unnecessary...

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