In the present notebook I analize and gain insight of the kind of content available in the Netflix dataset.
The dataset (netflix_titles.csv) consists of tv shows and movies available on Netflix as of 2019 and contains information including:
General information: id, title, type (TV Show or Movie), director, cast and a brief description. Date fields: When the show was released and when it was added to the catalog. Categorization: Rating and category in which the show is listed. The dataset provides a unique perspective on the world’s most lucrative cultural industry, reflected by Netflix: Movie and TV Production. Demand for data has never been greater as digital technology transforms the ways in which films are produced, distributed and consumed by audiences in theatres and in the case of Netflix, at home.
Let's figure out current trends! For example, in this notebook, following questions are analized:
Which show is more common in this Netflix dataset, movie or TV show? What is the most frequent word in the datasets's show titles? Which country produces most titles? How long is a typical movie? Which show genre is the most frequently ocurring in Netflix?
The goal of this project is to practice creating and interpreting different types of visualizations using real world data. I create a minimum of one scatter plot, one histogram, one box plot and one bar graph with matplotlib, seaborn and worldcloud.