Investigating Netflix Movies and Guest Stars in the Office

Using Python and the Pandas library, I cleaned and processed the data to create a comprehensive Data Frame. The Data Frame consisted of information on popular Netflix TV shows and movies, including their titles, release dates, genres, durations, countries, and cast members. To gain insights from the data, I conducted exploratory data analysis, which revealed interesting patterns and trends. For instance, I found that the majority of the popular TV shows on Netflix fall under the drama and comedy genres. Moreover, I noticed that the number of new releases on Netflix has increased significantly over the past few years. To start with, I created the year & duration of the movie, as a dictionary alongside it, I then imported Pandas and built a data frame from the dictionary, and I imported matplotlib.pyplot and created a figure, a line plot of the release year & duration, and gave it a title. I then used the CSV file as a data frame to show the top 5 movies of the data frame, then subset the data frame for movies and selected just the columns of interest which were the Titles, Countries, Genres, Release-Year, and Duration. I then created a figure and increased it with a scatter plot of duration versus year which I also gave a title, filtered for the duration of movies shorter than 60 minutes, I created a variable of colors and used the iterate method to loop the classifications of the movies which were children, documentaries and standup with the variable of colors I created and inspected the first 10 values in my list of colors. This analysis helped me to identify the genres that are most popular with viewers.