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Loading and Parsing TopTMBD.csv

✓ Published1🌍 Public
TT-Reiser
Last edited Sep 28, 2021
Created on Sep 7, 2021

Loading a TMDB movie dataset from a gist, this example uses d3.csv to parse comma-separated values and display the file size, row count, and column count in a large text element. The code also includes commented alternatives using fetch with async/await and nested promises. The data includes quantitative fields like budget and revenue, categorical genres, and release dates, while the visualization highlights parsing results rather than plotting the attributes themselves.

AI-generated description

A program that loads and parses some [CSV] about the Top 5000 Movies on TMDB.

Authored by: Tyler Reiser

Link to original Dataset: https://www.kaggle.com/tmdb/tmdb-movie-metadata

Link to Gist: https://gist.github.com/T-Reiser/1118aa906c506aa00095288bfc8e854d

Rehash of Gist README.md:

Attributes:

  • Budget is Quantitative,
  • Genre is Categorical,
  • Keywords are Categorical,
  • Original Title is Categorical,
  • Popularity is Quantitative,
  • Production Companies is Categorical,
  • Production Countries is Categorical,
  • Release Date is Ordinal,
  • Revenue is Quantitative,
  • Runtime is Quantitative,
  • Tagline is Categorical,
  • Vote Average is Quantitative,
  • Vote Count is Quantitative.

Removed Attributes:

  • URL link to homepage for movie
  • ID of the movie
  • Original Language of the movie
  • spoken languages
  • status of the movie

Tasks and Questions:

  • Is there a correlation between length or style of Tagline, and popularity of a movie?
  • What is the most popular Genre for movies, and how do the Genres evolve over time?
  • Are there certain Production Compaines that always make the most popular films, and does that change over time?
MIT Licensed

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