Exploratory Data Analysis With Movies

Exploratory Data Analysis With Movies

An investigation into the metrics that make blockbuster and award winning films. Microsoft wants to enter into the movie industry, however they have no prior knowledge of the industry and they need help so that their movie studio can be successful.

As a part of the Flatiron School bootcamp requirements, we are required to complete a project at the end of each learning module that demonstrates our ability to apply what we’ve learned.

The prompt for the first project is as follows:

Microsoft wants to enter into the movie industry, however they have no prior knowledge of the industry and they need help so that their movie studio can be successful.

The primary skills that required to perform the exploratory data analysis (EDA) of the movie industry included: webscraping, storing and cleaning the data in a pandas dataframe, and visualization of data using seaborn and matplotlib. I’ll describe some of the methodology I used for webscraping and cleaning, and I’ll go through some of the recommendations we made in order to be successful as a movie studio.

Webscraping

I was unfamiliar with webscraping prior to the bootcamp, but I can say without a doubt it has been one of the most useful and fun skills that I have learned in the past few weeks. Web Scraping is essentially the process of looking at the HTML for a webpage and deconstructing that HTML so that you can extract pertinent information for analysis. By using the requests and Beautiful Soup libraries we can easily get all of the html into a Jupyter notebook and start picking apart the pieces. Some of the websites we used to develop recommendations were moviefone.comimdb.com, and boxofficemojo.com. For example, this page had movie release dates for movies released in 2019 so I ended up writing code like this:

movies_= requests.get("https://www.moviefone.com/movies/2019/?     page=1")
soup = BeautifulSoup(movie_dates_page.content,'lxml')
movie_title = soup.find_all("a", class_="hub-movie-title")

Then I simply use the .text method of each of the elements in the movie_title variable and I can get each of the movie titles on that webpage into a list. I use a similar method as the one shown above to get all of the release dates into a list. The two lists can then be put into a dataframe and the dates column can be manipulated using the datetime library so that we can count the number of movies released in a certain month or on a certain day. The construction of the dataframe would look something like this:

movie_dict = {'movies':movie_list, 'release_date':dates_list}
dates_df = pd.DataFrame(data=movie_dict)

#movie_list and dates_list are previously constructed lists from #webscraping

pandas exploratory-data-analysis data-science python flatiron-school

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