MOMA Analysis

July 23, 2020 - All

Dataset: My project is the MOMA archive analysis report. I get a JSON file from MOMA GitHub. It has many different variables, ‘title’, ‘Nationality’, ‘Gender’, ‘medium’, etc. Each class has those variables. Interest: I’m curious about which artist has the most amount of works collected by the MOMA library? What material is the most popular medium? What is the nationality distribution among the artists? Is there any artwork related to ‘New York’? Which classification has the largest number of artworks? Method: Python is a good tool to make a dictionary by using the keywords to loop through data. I use ‘if’, ‘else’ gramma to calculate the frequency of variables. Then, I use the ‘sort’ function to list them based on their value. After I get all the dictionaries that I want, I use‘Plotly’ to create bar charts and pie charts to visualize data. Conclusion: It’s not easy at the start when I can’t loop the right data that I want. However, after I figured it out. I found python is pretty strong.

› tags: culture / programming / python /